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Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 2000008935068278 rez on bnbchain for 804k note that it is not real token of renzoprotocol its price of has dropped 10000 scammer 0x4dbd4c1a95361e8b7b54ecc197988acaa6a43b94 httpstcomfbrmyupe1 posted at 20240429 021438 utc Output: [{'address': '0x4DbD4c1a95361E8B7b54Ecc197988ACaa6a43B94', 'entity': 'Scammer', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that starwallets on bnbchain was under a price manipulation attack on uniswap v2 pair with a loss of 33k attacker 0x19e3188f7637404fc627503b583377d9348ee3ad httpstcozrywjnqadg posted at 20240419 021121 utc Output: [{'address': '0x19E3188F7637404FC627503B583377d9348EE3AD', 'entity': 'StarWallets Exploit (2024-04)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that predyfinance on arbitrum was attacked with a total loss of 464k attacker unknown unknown 0x76b02ab483482740248e2ab38b5a879a31c6d008 httpstco6myrhqbg5b posted at 20240514 092957 utc Output: [{'address': '0x76b02ab483482740248e2ab38b5a879a31c6d008', 'entity': 'UNKNOWN', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: community alert the group of scammers who stole 8 figs with magnate kokomo lendora solfire etc is back with a new project on blast leaperfinance last week they funded an address on blast with 1m of laundered funds from the previous rugs and have begun adding liquidity to bait people in tracing 1 1 week ago 0x46a moves eth a few hops from magnate scam to 0x10e 0x46a10e7444bf78736e6a24b1de8dc8a20e4b3ed7 0x10ef5bb874b226bdd6860f00d5e5a2aa3e955b9a 2 bridges to 0xac9 on polygon 0xac973e1be91fe8674dff2e0de4a9375803ae4435 3 0xac9 transfers weth to 0x08 0x67b5d330001c6b281a83fbcb2cf90119f2d72b3a42352abbfafb7ea72e91a34c 0x085817190c2e9949f67e8144b45383e3c5890457 4 0x085 bridges weth to base via orbiter 0x085817190c2e9949f67e8144b45383e3c5890457 5 repays borrower on behalf of another address 0x1174 using sonne finance 0xb84987d765bd6f53f6a4273f80d100e56a786e7383e687d5fb1bd534da381a92 6 0x1174 bridges 294 eth from base to blast via bungee 0x1174f03604b606bcfe50aaaf96af4477f3e81977 7 swaps for leap and becomes top holderdeposits to protocol 0x7606c8e3b35dde1186f90a84d6ef91d108db672f85bf82ecf00a877b489f4d99 0x67243b69c6e3b2904b1fecc68ab4fa210ec9201ff6590c4cc27808d5192de315 research was coauthored with bax1337 in the past they let the tvl grow to 7 figs before stealing all of users funds deposited to the protocol and falsify kyc documents use low tier audit firms they now have launched scams on base solana scroll optimism arbitrum ethereum avalanche etc Output: [{'address': '0x46a10e7444bf78736E6a24b1De8dc8A20E4B3ED7', 'entity': 'Magnate scam', 'type': 'Scam'}, {'address': '0x10ef5bb874b226bdd6860f00d5e5a2aa3e955b9a', 'entity': 'Magnate scam', 'type': 'Scam'}, {'address': '0xAc973E1Be91fe8674dff2E0de4A9375803Ae4435', 'entity': 'Magnate scam', 'type': 'Scam'}, {'address': '0x085817190C2e9949F67e8144B45383E3C5890457', 'entity': 'Magnate scam', 'type': 'Scam'}, {'address': '0x1174f03604B606bcFe50aaaf96af4477f3E81977', 'entity': 'Magnate scam', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert the root cause is that a lowlevel call in the performaction function which lacks parameter validation is used by the hacker to steal users funds please revoke the approval on 0x3a23f943181408eac424116af7b7790c94cb97a5 httpstcoagcv3yby1c posted at 20240117 021746 utc Output: [{'address': '0x3a23f943181408eac424116af7b7790c94cb97a5', 'entity': 'Socket.tech', 'type': 'Cross-chain Bridge Protocol'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: edit 20240708 1145am utc goxxedorg has been updated this new address seems to be under control of trustee yet it hasn been included on either goxxedorg or x mtgoxbalancebot my interpretation of the blockchain activity on july 45 depending on your timezone is that 2702 btc was sent to two exchanges so about 19 of the total held this transaction split off the 270178112302 btc from the monitored address 1hehlv7zrfxwuvjuwkwt2d5xfbxxvhov68 httpsblockchaircombitcointransactionf95aa78a405007e702e6bb69732e3bbe7a0b3de75dc6e7b9b0dcc85e5e597c8e that 2702 was cutup into two multisig addresses with onchain activity consistent with typical exchange hot wallets however this was done in a two step process of transactions httpsblockchaircombitcointransaction3a0672bb5b831b886aaff6e276dcfeadeb8256ec4124c40ef50002cfe2f599f7 httpsblockchaircombitcointransactiona4f8df6f0b4a921eb311b58842b6162b66d74f3a91c341ed410ae2f4f5dc6170 the two multisig exchange hot wallet addresses being bc1qrv0v2l9ekrx3lnkeusrzvu2hqyha6ex94nxumlhec4mrye8njm7sn7e4v4 bc1qkjcuq5hgqd3y735vxl52htd2ja9xvyv8cq3gllhpwp23c3d7tdyq2ug68h there is also a small amount 002 btc lingering in 1chdmm2h4wzctxy11kyahdysiulkvffywr which i guess is controlled by trustee bottom line in my opinion most likely approx 19 of the coins were paid to 2 japanese exchanges hot wallets and the new addresses which appeared and trustee controls are not being monitored by either goxxedorg or mtgoxbalance bot which i personally find odd as i had thought both efforts would have noticed what me an relative amateur has edit link to watch this new address httpsblockchaircombitcoinaddress16arp3spfjwq6x5ftzrlu2ucean1dxvqdf Output: [{'address': '1HeHLv7ZRFxWUVjuWkWT2D5XFbXXvHoV68', 'entity': 'MtGox.com', 'type': 'Exchange'}, {'address': 'bc1qrv0v2l9ekrx3lnkeusrzvu2hqyha6ex94nxumlhec4mrye8njm7sn7e4v4', 'entity': 'Bitbank.cc', 'type': 'Exchange'}, {'address': '1ChDmM2h4wzCtxY11KyAhDYsiULKvfFywR', 'entity': 'MtGox.com', 'type': 'Exchange'}, {'address': '16ArP3SPFJwq6X5fTZRLu2UcEAn1dXVqdF', 'entity': 'MtGox.com', 'type': 'Exchange'}, {'address': 'bc1qkjcuq5hgqd3y735vxl52htd2ja9xvyv8cq3gllhpwp23c3d7tdyq2ug68h', 'entity': 'Japanese exchanges hot wallets', 'type': 'Exchange'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: certikinsight two wallets linked to eoa 0xb48b labeled coinstats exploiter 31 have deposited a combined 311 eth 960k to tornadocash eoa 0xe0994ed541e6e6dc053fd9eb03a32f3d9a9876c6 still holds 221 eth httpstcoamrstvostn Output: [{'address': '0xe0994eD541e6E6dc053Fd9eB03A32f3d9A9876C6', 'entity': 'CoinStats Exploiter 31', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: certikinsight doughfina has been exploited for 18m which is held in eoa 0x2913d90d94c9833b11a3e77f136da03075c04a0f the root cause is due to the lack of validation unvalidated calldata in the connectordeleverageparaswap contract 0x9f54e8eaa9658316bb8006e03fff1cb191aafbe6 Output: [{'address': '0x2913d90d94c9833b11a3e77f136da03075c04a0f', 'entity': 'DoughFina', 'type': 'Hack'}, {'address': '0x9f54e8eAa9658316Bb8006E03FFF1cb191AafBE6', 'entity': 'ConnectorDeleverageParaswap contract', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hi redditors nancy is a white parrot who has sworn to protect nature and has a highpotential coin on this path in this way she gets help from talented peoples and together they protect the nature and make money for their investors now let me tell you the utilities of nancy our meme and reddit competition is live now come and win some nancy with us our team always tries to keep the telegram chat active and helps us find new investors for the group every day by doing twitter and reddit raids at each milestone we donate a certain amount of the total supply to protect nature our supportive community posts posts for us to raid on their reddit and twitter accounts and greets new investors what more you can want from a community some important numbers about nancy telegram 618 members twitter 930 followers holders 251 diamond guy 502 diamond hands ath 520k current mcap 340k dear nancycoin family were thrilled to present our roadmap this journey isnt just about cryptocurrency its a story of hope a mission to raise awareness about psittacine beak and feather disease pbfd and a communitydriven adventure lets soar together through this exciting saga nancys tale a 15 chapter animated saga at the heart of nancycoin is nancys story a captivating 15chapter comic that well reveal as animated movies each chapter ends with a cliffhanger keeping you at the edge of your perch i dont want to make the post too long check our website and telegram group to check out our story chapters opened with roadmaps and milestones keep in touch with us to see rising of nancy x httpsxcomnancycoin_sol telegram nancytg dexscreener httpsdexscreenercomsolana3msy3fvunbe4bgzk6h1xlehh4bnbcbnyegxvuwhr8vjc ca 2d8l2q6hsadcjc8juxzqdrczly8jcr6fr3sy5lsrg34l check out our website for more details and become a part of the nancy revolution today let ride this wave together to unprecedented heights nancycoin wings of wonder Output: [{'address': '3MsY3FvUnBe4BGzK6h1XLEhH4bNBCBNyegXvUwhr8vJc', 'entity': 'NancyCoin', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 2000000049057474 saga on bnbchain for 324k its price has dropped 10000 scammer 0x1bd5f53b00c76d12f87e003a8fdda9321b36db9e httpstcogbkobyk0he posted at 20240406 151425 utc Output: [{'address': '0x1BD5f53B00C76d12F87e003a8fDDA9321B36dB9e', 'entity': 'SAGA Token Rug pull (2024-04)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that normiebase on base was attacked due to a logic flaw resulted in its price plummeted 99 attacker 0xf7f3a556ac21d081f6dba961b6a84e52e37a717d httpstco53jwobkmsi httpstcowplq8oponh posted at 20240526 071119 utc Output: [{'address': '0xf7f3a556ac21d081f6dba961b6a84e52e37a717d', 'entity': 'NORMIE Base Exploit (05-2024)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert do not buy nobl now unknown unknown 0x88b9f5c66342ebaf661b3e2836b807c8cb1b3195 httpstcop8nravzvcx posted at 20240419 093230 utc Output: [{'address': '0x88b9f5c66342ebaf661b3e2836b807c8cb1b3195', 'entity': '$NOBL', 'type': 'Community reported scam '}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert the dex velocorexyz on zksync and linea was attacked lost millions of dollar stay safe attacker 0x8cdc37ed79c5ef116b9dc2a53cb86acaca3716bf httpstcodweostwkvv httpstcoyq2wzjwhfy posted at 20240602 045455 utc Output: [{'address': '0x8cdc37ed79c5ef116b9dc2a53cb86acaca3716bf', 'entity': 'Velocore.xyz Exploit (06-2024)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: update 0x4a68c250486a116dc8d6a0c5b0677de07cc09c5d 0x7a5dbb33d16c4ea459e788258560cf6792b0ed79 0xfa2e4ec5a818c2d2c352f4b152c06338d476b646 0x7ef8b34565ea6fb89c4894e431468209b073d273 0xcf043b3040d708526a3491a8306159c87ba8bba2 0xee1e6daf90194b4fdf776e557c40edcf7d89cb9b 0x7730a96483f47f036abd6ee34ba6455505c5ea05 Output: [{'address': '0x4a68c250486a116dc8d6a0c5b0677de07cc09c5d', 'entity': 'POODL Token: POODL Token', 'type': 'Token'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped tons of pixel on bnbchain for 99k usdt after minted tons of pixel the price of pixel has dropped 9999 note that it is not the real token of pixels pixel 0xa5c7ed2e42d69fede93635457a4808c11fb156fb httpstcoo5r4lowbuw posted at 20240219 140415 utc Output: [{'address': '0xa5c7ed2e42d69fede93635457a4808c11fb156fb', 'entity': 'UNKNOWN', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert munchables on blast has been attacked with a loss of 174k eth 62m attacker 0x6e8836f050a315611208a5cd7e228701563d09c5 httpstcoz86gjrol1a httpstconeo7opakes posted at 20240327 014227 utc Output: [{'address': '0x6e8836f050a315611208a5cd7e228701563d09c5', 'entity': 'Munchables Exploit (2024-03)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert minterest on mantle was under an exchange rate manipulation attack with a loss of 14m the protocol has been paused for supplying and borrowing attacker 0x618f768af6291705eb13e0b2e96600b3851911d1 httpstcokxdyrdvx4w httpstco9rxix6on4u posted at 20240715 035719 utc Output: [{'address': '0x618f768af6291705eb13e0b2e96600b3851911d1', 'entity': 'Attacker', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull an account related to the deployer dumped 226995494385 alt on bnbchain for 252k after minted tons of alt alt 0x4597fbaea5eb9c98f0aed9383246522064aff31e both the deployer of dmail and the alt are 0xe0f9ecf727c5976dbe6d89f9f30e60b98b6e6570 httpstcoham088jq38 posted at 20240125 103039 utc Output: [{'address': '0x4597fbaea5eb9c98f0aed9383246522064aff31e', 'entity': '$ALT', 'type': 'Community reported scam'}, {'address': '0xe0F9eCF727C5976DBE6D89F9F30e60B98b6e6570', 'entity': 'deployer of $DMAIL and $ALT', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: a few weeks ago i published research on 25 lazarus group hacks which resulted in 38m frozen i am sharing 7 additional wallet addresses which currently hold 618m 891 btc tied to these hacks bc1qw88pehjuejym9jyfgn6vn4aaw7q232hlyzzn6f bc1q27vxzyuh4vqwt3u9aqpuk7z5xtgz9y0tqxzesq bc1q62clzxr4vcycjfdqe33ake4dk9fenkpaddkteq bc1qfad2yxulctgz6g6tw635n52cw3v7wxydmtmd0f bc1q972gcd3ywyc2n2p5lzs5mdwra5q8nymzg0qlx0 bc1qfenmgt8x2ndhm00xsv09snvandvl9j9w0fhtzw bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg Output: [{'address': 'bc1qw88pehjuejym9jyfgn6vn4aaw7q232hlyzzn6f', 'entity': 'Lazarus Group', 'type': 'Sanction list'}, {'address': 'bc1q27vxzyuh4vqwt3u9aqpuk7z5xtgz9y0tqxzesq', 'entity': 'Lazarus Group', 'type': 'Sanction list'}, {'address': 'bc1q62clzxr4vcycjfdqe33ake4dk9fenkpaddkteq', 'entity': 'Lazarus Group', 'type': 'Sanction list'}, {'address': 'bc1qfad2yxulctgz6g6tw635n52cw3v7wxydmtmd0f', 'entity': 'Lazarus Group', 'type': 'Sanction list'}, {'address': 'bc1q972gcd3ywyc2n2p5lzs5mdwra5q8nymzg0qlx0', 'entity': 'Lazarus Group', 'type': 'Sanction list'}, {'address': 'bc1qfenmgt8x2ndhm00xsv09snvandvl9j9w0fhtzw', 'entity': 'Lazarus Group', 'type': 'Sanction list'}, {'address': 'bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg', 'entity': 'Lazarus Group', 'type': 'Sanction list'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that an attack on bnbchain on users who approved an closedsource contract0x389a9a with a loss of 46k please revoke approvals from the contract 0x389a9ae29fbe53cca7bc8b7a4d9d0a04078e1c24 attacker 0x123fa25c574bb3158ecf6515595932a92a1da510 posted at 20240428 024643 utc Output: [{'address': '0x123fA25C574bB3158Ecf6515595932A92A1Da510', 'entity': 'Phishing Scam #229', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: lookonchain bnbtiger is here to put smile on our faces what are you waiting for join the moving train final stages of dappsstaking followed by cz binance bnbtiger bnbtiger 0xac68931b666e086e9de380cfdb0fb5704a35dc2d Output: [{'address': '0xac68931b666e086e9de380cfdb0fb5704a35dc2d', 'entity': 'BNBTiger', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: khamzat your team is incompetent as you directly linked the team wallets with the insider wallets buying up 78 of the supply why do all of you instantly nuke your reputation with meme coin scams summary using timing analysis confirms 71 insider and dev team wallets is directly linked as they were funded by same address on ethereum smash dev solana address was funded via ethereum 0x8ae40d1e89cd7333efe42a35969f0b8548fe54a8 0x8ae sends 05 eth to binance on july 2 at 221 pm utc 0xe0bba2925ea629ac918f700dde196a2f12e13b1d69ace379224f09ae34238652 smash dev receives 1183 sol from binance on july 2 at 225 pm utc 2tnzi8cmraat1y9dpuyzp5rlufufnunvgerypsh369lukdryl5vcb8nfbiwh1y5bcytuqer1gfksbnjxyv69tzgp 0x8ae sends 3825 eth to sideshift on july 2 at 222 pm utc 0x4bc49dbda6ede2fa82640e3caaedf4e593c2c9f2cdb4b77f6d3f693b409e9054 smash 71 insider wallet receives 8655 sol from sideshift on july 2 at 223 pm utc uxqjyco11h3cpk7tgxoray2gxoqfmtm2khlcrz2cfjnv6sugvhhzbv1eiithkjeqe8w6vypkortwjebam56pffx 862 sol was dispersed to 24 addresses in these two txns 24hydatqyphrgu5vnyj9biwyszytxzppxsxy2j7xkbhcpunob4csn1lfgrxssxjcvvcl6ax5lwjapbv9dckdtvd1 3ngc1qajz8hyxe8hdnkwoqf6kqvxaeln9cd6ztwrdhgwo3hnrkzm1usrmwo8pfwlzmctzhx1lglejuh1fkwf94lp those 24 addresses bought up 712m smash or 712 of entire supply 2ptq3zxvqkwi1frwlbp2r8r35dd4gcjhjtpnt5nybqql 2zkoctu2zuta5scpm5ksassjqmpum7pbbzpzvoihzqyq 4cm1qppakmjpz4hbkyxzk57k1s3xszciremervfgqexd 55lpcraktrmy8zg67xcovezu2bpr2rgebfnvtz4tzhd5 5wj8axwzslm56qirzgibhwrf4vgjaqeyhaexhqxijoo8 5qid7mknktckrjcsmxg6zcjcjfps9zhywejzsr6snkgh 85tq95nklfsbpifeungsaltsx8gnzbke5qkejysyrgox 86ud7yybu5p2j4wdgnkcoxpmjny5npxak3erjjukabp7 8v9djfdeapadmnzj7ckcemsgalpmzedsejhrwaujwcla b1mzd5yweddnjjfza1238dtk7lpygquhc934clr2rgbd cvyf45jxhp6rvwp44cvmwtg1ylwm2e9hmqs2k3pdywsh czbbs3ajuqmtpgpevekxfah6dbskamubk5vgngq1s1v4 dqs8jreepxrfzk98cgmdtuq6va7no3xshgk7pvjhqp57 dwytf1fqp73w6vcvylgadf5tosm6qfbbt3dxyux9hava f86tvrovfsqtqdmbobkzug4eeyfixr2uzotn7rivyway h129asevcf4gq6p3hxcmfzailrrncs5dj6kwvznh4xt8 hdttactvkdr7qhsfucfa2c9fepyqwqygeuwmphtqse5c hpjgsytk9opifuabeysctuvqc7rz18pkdkzvpx9nmeiu j31vecv5zshbxknwrtpebgahjlwzdvswshby6gxwapff 2evaw7ozm46ixsysauseqnimksowd2n2pnfcbiftxmyc 3zujxarkzbqcu5vsmpuwhbfuemewybradfhrwdadpvmx fcwejowhotm8vbqpqffdc5h7uyensteazjx89rvgkw8y el9wskztp8vdy9vxrcrad8jxz1f3fd4akcaxdytsudp 268siztgfne5oyyu54f29v9phidbwtkx8e19nt7ubnmx then the 712m smash got dispersed to from each address to more addresses where i stopped tracking Output: [{'address': 'UNKNOWN 0x8ae40d1e89cd7333efe42a35969f0b8548fe54a8', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '2zkocTu2zUtA5scpM5KsASSJQmpUm7PbbZpZvoihZqYq', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '4CM1qppAKMJPz4hbKyXzK57k1s3XszCiremERvFGqExD', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '55LPcRakTrmY8zg67Xcovezu2bpr2rgebFNvTz4tZhd5', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '5Wj8AXWZsLM56qirzgiBHwrF4VGjaqeyhaeXHqXijoo8', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '5qid7mkNktcKRJcSmxg6ZcjcjFps9ZHYweJzSr6SNkGH', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '85Tq95nKLFsbPiFEUngSALtsX8gnzbke5qkejySYrGoX', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '86UD7yybU5P2J4WdGNKCoxpMjNY5NpxaK3ERjjuKabP7', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '8V9DJfDeAPAdMNZj7ckcEmSgALPMzeDsEjhRWAUJWCLa', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'B1MZD5YWeDDnJJfza1238DTk7LPyGqUHc934CLR2RgBd', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'CVYF45JXHP6rVwP44CvMwtG1yLwM2E9hmQs2K3PdyWsh', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'CzbBs3ajUQMtpGpEVeKxfAh6dBSkaMubK5VGngq1s1V4', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'DQS8JREePXRfZk98cgmDTuQ6vA7no3xshGk7PvJhQp57', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'DWYtF1FQp73w6vCVyLgadF5TosM6qfbBt3dXYUX9hAva', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'F86TvrovFsqtQDMbobKZuG4eeyfiXR2uzotN7RiVYwaY', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'H129AsEvCF4GQ6p3HxCmFzaiLRRncS5dj6kWVZnh4Xt8', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'HDTTACtvkDr7qhsfUcfa2c9FEPYqWqygEUwMPhTqsE5c', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'HPjGsYtk9opifuaBEysCTUVQc7Rz18PKdkZvpX9NMEiU', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'J31vEcv5zsHbxknWRTPebGAhjLWZDVSwsHBY6GxwapfJ', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '2EvaW7oZM46iXsYSAUSEqniMksoWD2n2PnfcbiFtXmyC', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '3zUJxARkZBqCU5VSMPUwhBFuemewybRadFhRwdaDpvMx', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'FcWEjoWHoTM8Vbqpqffdc5h7uyENSteAZJx89rvGkw8y', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': 'eL9wskZTp8vdy9VxRCrAd8JxZ1f3fD4aKcAxdYTSUDP', 'entity': 'Khamzat Smash', 'type': 'Scam'}, {'address': '268SiZTGfnE5oyyU54f29v9PhidBWTkx8E19nT7ubnMX', 'entity': 'Khamzat Smash', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert goledofinance on conflux was attacked with a loss of 79m cfxworth 17m its lvt also drops 3m to only 235k attacker 0xa66b15ff3b9e31f579d06d70b527ad0a8f2ffb58 goledofinance is a aave v2 fork protocol httpstcoi49iidhka0 posted at 20240128 042242 utc Output: [{'address': '0xa66b15ff3b9e31f579d06d70b527ad0a8f2ffb58', 'entity': 'Attacker', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 123336949297867 slerf on bnbchain for 468k note that it is not the real token of slerf on solana its price has dropped 10000 scammer 0xbe1e6703040236126e9e8b655f5aafd68d014c08 httpstcoefqdou0lpt posted at 20240320 012306 utc Output: [{'address': '0xbe1e6703040236126e9e8b655f5aafd68d014c08', 'entity': 'SLERF Token Rug pull (2024-03)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hashditcasestudy case 18 2 days ago on july82024 hashdit monitored a user losing 95k worth of rev3l tokens to a fake verification address on bsc fake verification address 0xf0439c33313f975a404a57a6f4b538c142f0eade victim 0x8cbbcca6201301c6f42865c316a14a68bf60513a phishing tx httpstcouasvby4ltw this is due to him signing a approveincrease allowance bep20 transfer transaction to the fake verification address just 31 blocks prior to the incident httpstcosw6pmae2qn this could have been prevented as our phishing model flags all approval increaseallowance fresh transfer to an unverified eoa drainer address as high risk a general guideline for all users always check your approvalpermit spenders or transfer recipients when signing transactions protect yourself today by installing our hashdit chrome extension and metamask snaps httpstcobsxkr7gup2 httpstcotsfzls6t0e Output: [{'address': '0xf0439C33313f975a404A57A6F4B538c142F0EAdE', 'entity': 'Fake Verification Address', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull an account related to the deployer dumped 132001750286 jup on bnbchain for 403k the price of jup has dropped 10000 jup 0x2cfafed678503dc4bc56f1ef89b71a37528d6ef9 the deployer is still 0xe0f96570 httpstcomz8dzim6dg posted at 20240129 123127 utc Output: [{'address': '0x2cfafed678503dc4bc56f1ef89b71a37528d6ef9', 'entity': '$JUP', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert the shido exploiter dumped the rest of 1b shido for 263 ethworth 890k today resulting in a total loss of 956 ethworth 32m the asset is now sitting in the wallet 0x4621e0cd8c91ecf1b0efcbf07f0838a5ee25c5dd the shido team sent an onchain message to the hacker to negotiate a bounty to the hacker instead of legal action httpstco1fdyqtbjx5 posted at 20240301 064338 utc Output: [{'address': '0x4621e0cd8c91ecf1b0efcbf07f0838a5ee25c5dd', 'entity': 'Shido Exploit (2024-03)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that citadeldao on arbitrum was attacked with a loss of 93k attacker 0xfcf88e5e1314ca3b6be7eed851568834233f8b49 httpstcozzthrcznml posted at 20240127 130637 utc Output: [{'address': '0xfcf88e5e1314ca3b6be7eed851568834233f8b49', 'entity': 'UNKNOWN', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert gemholiceco on zksync probably rugpull one year ago 921 eth locked unexpectedly in the presale due to the old transfer issue on zksync but was just taken away one day ago scammer 0x3c67ae2a58c43e220c15159733022225b773eedc httpstcol0v3fegfk3 posted at 20240608 140424 utc Output: [{'address': '0x3c67aE2a58c43E220c15159733022225b773eeDC', 'entity': 'GemholicECO on #zkSync - Rugpull', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: in our attempt to warn users of risky tokens as early as possible hashdit will continually share our consolidated list regularly 0x4a68c250486a116dc8d6a0c5b0677de07cc09c5d 0x7a5dbb33d16c4ea459e788258560cf6792b0ed79 0xfa2e4ec5a818c2d2c352f4b152c06338d476b646 0x7ef8b34565ea6fb89c4894e431468209b073d273 0xcf043b3040d708526a3491a8306159c87ba8bba2 0xee1e6daf90194b4fdf776e557c40edcf7d89cb9b 0x7730a96483f47f036abd6ee34ba6455505c5ea05 educate yourself about the risks and always do your own research dyor when diving into new ventures psa do reach out to us on tg or our mail to dispute any disagreements we will be glad to resolve them thank you and stay vigilant update Output: [{'address': '0xee1e6daf90194b4fdf776e557c40edcf7d89cb9b', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0xcf043b3040d708526a3491a8306159c87ba8bba2', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x7a5dbb33d16c4ea459e788258560cf6792b0ed79', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0xfa2e4ec5a818c2d2c352f4b152c06338d476b646', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x7ef8b34565ea6fb89c4894e431468209b073d273', 'entity': 'UNKNOWN', 'type': 'Suspicious'}, {'address': '0x7730a96483f47f036abd6ee34ba6455505c5ea05', 'entity': 'UNKNOWN', 'type': 'Suspicious'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that mim_spell on ethereum was attacked with a total loss of 1807 ethworth 41m attacker 0x87f585809ce79ae39a5fa0c7c96d0d159eb678c9 httpstconw24hexhpn posted at 20240130 103906 utc Output: [{'address': '0x87f585809ce79ae39a5fa0c7c96d0d159eb678c9', 'entity': 'Abaracadbra.money Exploit (2024-01)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hashdit hi what did you find out about 0xd77e6e1ab2b56537ec1a3f26576c94d5358c855f Output: [{'address': '0xd77e6e1ab2b56537ec1a3f26576c94d5358c855f', 'entity': 'Fake Phishing Address Reported by Etherscan', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that an attack on bnbchain on users who approved a closedsource contract0x389a9a with a loss of 46k please revoke approvals from the contract 0x389a9ae29fbe53cca7bc8b7a4d9d0a04078e1c24 attacker 0x123fa25c574bb3158ecf6515595932a92a1da510 posted at 20240428 024833 utc Output: [{'address': '0x123fA25C574bB3158Ecf6515595932A92A1Da510', 'entity': 'Phishing Scam #229', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that barley_finance on ethereum was attacked with a total loss of 58 ethworth 13k attacker 0x7b3a6eff1c9925e509c2b01a389238c1fcc462b6 httpstcotc02xil2oy posted at 20240129 000040 utc Output: [{'address': '0x7b3a6eff1c9925e509c2b01a389238c1fcc462b6', 'entity': 'Attacker', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a deployer dumped 798940370140 gtai on bnbchain for 329k usdt the price of gtai has dropped 10000 gtai 0xf5461d03062ec667854445cd1e3f6ab5fa25fa44 httpstcoondrnkjz98 posted at 20240124 083804 utc Output: [{'address': '0xf5461d03062ec667854445cd1e3f6ab5fa25fa44', 'entity': '$GTAI', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: certikinsight on sunday the pancakebunnyfin exploiter deposited 1002 eth 29m into tornadocash via 0xd0f2259e0bd71e849143bbc07f4e427bb6f7756b bunny finance was exploited for 45m in may 2021 the exploiter still holds 114m dai in 0x820c httpstcojcc18q1niy Output: [{'address': '0xd0f2259e0bd71e849143bbc07f4e427bb6f7756b', 'entity': '@TornadoCash', 'type': 'Mixing serive'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected unizen_io on ethereum was attacked with a loss of 21m there is a sudden update upgrade of their aggregator a few hours ago now the aggregator has been paused please revoke approvals for the contract 0x17b45dc664304eb44a0bd3bceec46f9d9543afcb attacker 0xb660cae1a59336676ea1887b15eb3c0badb90d78 posted at 20240309 010912 utc Output: [{'address': '0x17b45dC664304Eb44A0Bd3BcEeC46f9d9543AFCB', 'entity': 'Safe - Gnosis Safe', 'type': 'Wallet'}, {'address': '0xb660cae1a59336676ea1887b15eb3c0badb90d78', 'entity': 'Unizen.io Exploit (2024-03)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert hedgeyfinance on eth was attacked with a loss of 13m attacker 0xded2b1a426e1b7d415a40bcad44e98f47181dda2 httpstcoyxqdgbozbs posted at 20240419 073246 utc Output: [{'address': '0xDed2b1a426E1b7d415A40Bcad44e98F47181dda2', 'entity': 'Attacker', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 327538196627380 foxy on bnbchain for 13m its price has dropped 10000 scammer 0x2783633c051bf74b6fba4732137894d8b90bebd9 httpstcozo0taw2t0d posted at 20240410 090016 utc Output: [{'address': '0x2783633c051bf74b6fBa4732137894d8b90bEbd9', 'entity': 'FOXY Token Rug pull (2024-04)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hashditcasestudy case 18 2 days ago on july82024 hashdit monitored a user losing 95k worth of rev3l tokens to a fake verification address on bsc fake verification address unknown unknown unknown 0xf0439c33313f975a404a57a6f4b538c142f0eade victim unknown unknown unknown 0x8cbbcca6201301c6f42865c316a14a68bf60513a phishing tx httpstcouasvby4ltw this is due to him signing a approveincrease allowance bep20 transfer transaction to the fake verification address just 31 blocks prior to the incident httpstcosw6pmae2qn this could have been prevented as our phishing model flags all approval increaseallowance fresh transfer to an unverified eoa drainer address as high risk a general guideline for all users always check your approvalpermit spenders or transfer recipients when signing transactions protect yourself today by installing our hashdit chrome extension and metamask snaps httpstcobsxkr7gup2 httpstcotsfzls6t0e Output: [{'address': '0x8cbbcca6201301c6f42865c316a14a68bf60513a', 'entity': 'UNKNOWN', 'type': 'Victim'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert the attack resulted in a 1900 ethworth 65m loss due to a lack of parameter check using carefully constructed transaction parameters the hackers stole the victims authorization for the vulnerable contract and eventually stole the funds hacker 0x94641c01a4937f2c8ef930580cf396142a2942dc posted at 20240229 025137 utc Output: [{'address': '0x94641c01a4937f2C8eF930580cF396142a2942DC', 'entity': 'Reported by the CDA #599', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 2000000045120078 saga on bnbchain for 986k usdt its price has dropped 10000 scammer 0x1bd5f53b00c76d12f87e003a8fdda9321b36db9e httpstco8x4eeq8olc posted at 20240407 113929 utc Output: [{'address': '0x1BD5f53B00C76d12F87e003a8fDDA9321B36dB9e', 'entity': 'SAGA Token Rug pull (2024-04)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: it appears ripple was hacked for 213m xrp 1125m source address rjnlz3a1qpkfwctjlphmmzafbkutc2qojm so far the stolen funds have been laundered through mexc gate binance kraken okx htx hitbtc etc httpstcohkgyslqemv Output: [{'address': 'rJNLz3A1qPKfWCtJLPhmMZAfBkutC2Qojm', 'entity': 'Ripple.com', 'type': 'Service'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: a wallet suspected to be gcrgiganticrebirth deposited all 108m trump885m into btseexchange 7 hours ago making a profit of 83m this wallet spent 540700 usdt to buy 108m trump at an average price of 05 from nov 22 to dec 4 2023 address 0x4ab89a958214b1f65ff9c3b110dc4eed1d021323 Output: [{'address': '0x4Ab89A958214B1F65fF9C3B110Dc4eed1d021323', 'entity': 'GCR', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped tons of not on ethereum for 53 eth worth 202k the price of not has dropped 10000 not 0xb52ca047171f8965cb19421618a7f28c6540861d httpstcovd2tukcc39 posted at 20240307 083944 utc Output: [{'address': '0xb52ca047171f8965cb19421618a7f28c6540861d', 'entity': '$NOT', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hi redditors nancy is a white parrot who has sworn to protect nature and has a highpotential coin on this path in this way she gets help from talented peoples and together they protect the nature and make money for their investors now let me tell you the utilities of nancy our meme and reddit competition is live now come and win some nancy with us our team always tries to keep the telegram chat active and helps us find new investors for the group every day by doing twitter and reddit raids at each milestone we donate a certain amount of the total supply to protect nature our supportive community posts posts for us to raid on their reddit and twitter accounts and greets new investors what more you can want from a community some important numbers about nancy telegram 618 members twitter 930 followers holders 251 diamond guy 502 diamond hands ath 520k current mcap 340k dear nancycoin family were thrilled to present our roadmap this journey isnt just about cryptocurrency its a story of hope a mission to raise awareness about psittacine beak and feather disease pbfd and a communitydriven adventure lets soar together through this exciting saga nancys tale a 15 chapter animated saga at the heart of nancycoin is nancys story a captivating 15chapter comic that well reveal as animated movies each chapter ends with a cliffhanger keeping you at the edge of your perch i dont want to make the post too long check our website and telegram group to check out our story chapters opened with roadmaps and milestones keep in touch with us to see rising of nancy x httpsxcomnancycoin_sol you can find our telegram via twitter dexscreener httpsdexscreenercomsolana3msy3fvunbe4bgzk6h1xlehh4bnbcbnyegxvuwhr8vjc ca dnfxs7ecajbxwmqzd8izleim11ctdvffp7qit9uswqy8 check out our website for more details and become a part of the nancy revolution today let ride this wave together to unprecedented heights nancycoin wings of wonder Output: [{'address': '3MsY3FvUnBe4BGzK6h1XLEhH4bNBCBNyegXvUwhr8vJc', 'entity': 'NancyCoin', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert senecausd is under an ongoing attack due to an open external call bug please revoke your approvals asap for ethereum 0xbc83f2711d0749d7454e4a9d53d8594df0377c05 arbitrum 0x2d99e1116e73110b88c468189aa6af8bb4675ec9 posted at 20240228 161140 utc Output: [{'address': '0xbc83f2711d0749d7454e4a9d53d8594df0377c05', 'entity': 'UNKNOWN', 'type': 'Hack'}, {'address': '0x2d99e1116e73110b88c468189aa6af8bb4675ec9', 'entity': 'UNKNOWN', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that the lending protocol uwu_lend on eth was under a series attacks with a loss of 146m attacker 0x841ddf093f5188989fa1524e7b893de64b421f47 httpstcou2vhrsrlfz posted at 20240610 122620 utc Output: [{'address': '0x841dDf093f5188989fA1524e7B893de64B421f47', 'entity': 'UwU Lend Exploit (06-2024)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert gogalagames on ethereum was attacked with a loss of 5b gala worth 213m the attacker minted 5b gala attacker 0xec78b484a28a73f4f1cff23b9cd31aa12c1d9b4e httpstcosvwkrymqwi httpstcoeboxpjdt2u posted at 20240521 003556 utc Output: [{'address': '0xEC78b484a28a73F4f1cff23B9cD31aA12c1D9b4e', 'entity': 'Gala Games Exploit (2024-05)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: certikinsight minterest lending contracts on mantle have been exploited for 14m by unknown 0x618f768af6291705eb13e0b2e96600b3851911d1 currently some operations on the minterest app have been paused httpstcozxatj1oawd httpstcopgq0wzi1q3 Output: [{'address': '0x618F768aF6291705Eb13E0B2E96600b3851911D1', 'entity': 'Minterest lending contracts', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that mars_defi412 on bnbchain was attacked with a loss of 100k attacker 0x306174b707ebf6d7301a0bcd898ae1666ec176ae httpstcogiam32gppz posted at 20240416 084415 utc Output: [{'address': '0x306174B707EbF6d7301a0BCd898ae1666Ec176ae', 'entity': 'MARS Token Exploit (2024-04)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped tons of eigenlayer on ethereum for 104 eth 400k which resulted in its price dropping 100 note that the token is not the real token of eigenlayer scammer 0xde68f30bee7398706df04eca625ac79089fc30aa eigenlayer 0x1a4f05bd24c95147608657c26cd931c7b246cd21 posted at 20240315 024035 utc Output: [{'address': '0xDE68f30bEE7398706DF04ECA625AC79089fc30Aa', 'entity': 'EIGENLAYER Token Rug pull (2024-03)', 'type': 'Scam'}, {'address': '0x1a4f05bd24c95147608657c26cd931c7b246cd21', 'entity': 'Fake EigenLayer Exploit (2024-03)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: 24 over the weekend tether blacklisted a wallet with 296m usdt on tron connected to huione 14m flowed into this wallet from the dmm bitcoin hack in a 3 day period tnvakwqzau7xl9bcnvlmf9kseqkwes4ug8 httpstcodxfobi9xkw Output: [{'address': 'TNVaKWQzau7xL9bcnvLmF9KSEQkWEs4Ug8', 'entity': 'Huionepay.com.kh', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a deployer related address dumped 13307962 dmailanother fake dmail on bnbchain for 396k usdt its deployer is still 0xe0f96570 the price of dmail has dropped 982 dmail 0x5f65577c6a7ea9458ef0530738ca6ecb3c92b380 httpstcoj3vwot8fp0 posted at 20240126 033525 utc Output: [{'address': '0x5f65577c6a7ea9458ef0530738ca6ecb3c92b380', 'entity': 'DMAIL', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert defiplaza on eth was attacked with a loss of 200k due to a calculation issue the original attack was frontran by the mev frontrunner yoink frontrunner 0xfde0d1575ed8e06fbf36256bcdfa1f359281455a httpstcotrr3baixb5 posted at 20240706 084102 utc Output: [{'address': '0xFDe0d1575Ed8E06FBf36256bcdfA1F359281455A', 'entity': 'ParaSwap Exploit (2024-03)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: lets endorse the president trump trump a 4mbvbtlhgo6gawm7jafajuo9cts4an7nuvkwbzuhq3dx rade httpstcouamhhzfg6f g httpstcowu6erh8phl realdonaldtrump trump trump2024 memecoin httpstcovcufis4r4w Output: [{'address': '4mbvBTLhGo6GaWm7jAfajUo9Cts4aN7nUvKWBzuhQ3dX', 'entity': '$TRUMP', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that a token named scroll was attacked with a loss of 295k note that it is not the real token of scroll_zkp attacker 0x55db954f0121e09ec838a20c216eabf35ca32cdd httpstco14k3wmjoon posted at 20240529 062838 utc Output: [{'address': '0x55db954f0121e09ec838a20c216eabf35ca32cdd', 'entity': 'Scroll_ZKP TokenExploit (06-2024)\n', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hey ego the gecko community wee got some fantastic updates to share with you today new alltime high we are thrilled to announce that ego the gecko ego has reached a new alltime high with a market cap of 90k this milestone is a testament to the strength and enthusiasm of our community thank you for your unwavering support meme contest on x formerly twitter with 300 in prizes to celebrate our new ath wee launching a meme contest exclusively on x formerly twitter show off your creativity and humor by creating the best ego the gecko memes there 300 up for grabs for the most entertaining and popular memes stay tuned for the contest rules and how to participate we cant wait to see what you come up with bridge to base network update our expansion to the base network is well underway and wee making excellent progress the bridge development is moving smoothly and wee excited to share more details with you soon this strategic move will open up new opportunities for our community and bring even greater liquidity to ego thank you for being a vital part of the ego the gecko journey your support drives us forward and wee excited to continue growing together keep an eye out for more updates and announcements go ego httpsxcomegogecko httpslinktreeegothegecko ca eravjmgpnmh3wpj9zz9hb6vzxjfpicckuerxnacop1ka feel free to reach out if you have any questions or need more info wee excited to welcome you to the ego the gecko community Output: [{'address': 'ERAVJmgPNMh3Wpj9zZ9Hb6vZxjFpiCckUErXnACop1kA', 'entity': 'Ego the Gecko', 'type': 'ICO'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: introduction to matt furie matt furie born on august 14 1979 is a distinguished artist best known for creating the boys club comic series this series has given birth to several iconic internet meme characters such as pepe brett andy and landwolf furies artistic brilliance cultural impact resilience and sincere approach to his work and audience have made him a significant figure in the realms of art and internet culture artistic brilliance and cultural impact matt furies work is not just about creating art its about making a cultural statement the characters from boys club have transcended the pages of the comic series to become symbols of internet culture with pepe the frog being the most notable among them furies ability to capture the zeitgeist and create characters that resonate with a wide audience is a testament to his exceptional talent and vision the creation of hedz matt one of furies notable works hedz matt is a special artwork that offers a glimpse into his inner personality and how he perceives himself this piece is more than just art it is a reflection of furies inner world and artistic ethos hedz matt is associated with the unique contract address unknown 0x790814cd782983fab4d7b92cf155187a865d9f18 making it a significant piece for collectors and fans alike join the matt furie token community the matt furie token community is dedicated to celebrating and preserving the legacy of this extraordinary artist by joining this community you become part of a group that deeply appreciates and honors matt furies contributions to art and internet culture it is a space for fans to connect share and celebrate the impact of furies work how to get involved to discover more about matt furie and his projects this platform provides comprehensive information about his artwork projects and the significant impact he has made in the art world engaging with this community offers a unique opportunity to explore and celebrate the work of one of the most influential artists in contemporary culture conclusion matt furies combination of artistic brilliance cultural impact resilience and genuine approach to his work and fans makes him an extraordinary figure in the world of art and internet culture celebrating his legacy through the matt furie token community is a tribute to his enduring influence and exceptional talent join today and be a part of this vibrant community dedicated to honoring the legacy of matt furie all links linktreehttpslinktreemattfurie0x79httpslinktreemattfurie0x79 Output: [{'address': '0x790814cd782983fab4d7b92cf155187a865d9f18', 'entity': 'Matt Furie', 'type': 'ICO'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert ripple was hacked with a loss of 213m xrp worth 1125m victim address rjnlz3a1qpkfwctjlphmmzafbkutc2qojm httpstcopreqgelj2s posted at 20240131 141400 utc Output: [{'address': 'rJNLz3A1qPKfWCtJLPhmMZAfBkutC2Qojm', 'entity': 'Ripple.com', 'type': 'Service'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hashditcasestudy case 17 2 days ago on july012024 hashdit monitored a user losing 18k worth of hello tokens to a inferno drainer address on bsc create2 scam address 0x9b22da147c87d3c57e639db7ea377428cece7d54 victim 0x1c4c7f8e322a327b773784a5c4a49545cd9c6525 phishing tx httpstcomzlrb60qry this is due to him signing a basic bep20 transfer transaction to the drainer address just 6 blocks prior to the incident httpstco6gkaspvpr7 this could have been prevented as our phishing model flags all approval increaseallowance fresh transfer to an unverified eoa drainer address as high risk a general guideline for all users always check your approvalpermit spenders or transfer recipients when signing transactions protect yourself today by installing our hashdit chrome extension httpstcobsxkr7gup2 Output: [{'address': '0x9b22da147c87d3c57e639db7ea377428cece7d54', 'entity': 'Inferno Drainer', 'type': 'Scam'}, {'address': '0x1C4C7F8e322A327B773784A5C4A49545cD9c6525', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: hashdit i got my superrare stolen by 0xf7c05db7485e5c707335bd55517a8661d3ef5d46 Output: [{'address': '0xF7c05DB7485e5c707335BD55517A8661d3Ef5D46', 'entity': 'Reported by Cybera ', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a deployer dumped tons of optimus on bnbchain for 450k usdt after minted tons of optimus the price of optimus has dropped 10000 optimus unknown unknown 0x1b694e88e3ac77fc60b526622f9fdb42cd9822be httpstcoibq8qx93yc posted at 20240201 054532 utc Output: [{'address': '0x1b694e88e3ac77fc60b526622f9fdb42cd9822be', 'entity': '$Optimus', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected socketdottech was under an attack with a loss of 33m due to input validation issue attacker 0x50df5a2217588772471b84adbbe4194a2ed39066 all the contacts were paused by socket and recommend users revoking the approvals httpstcofs9zzalhko posted at 20240117 010240 utc Output: [{'address': '0x50df5a2217588772471b84adbbe4194a2ed39066', 'entity': 'Socket Protocol Exploit (2024-01)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: introduction froglic also known as pink hood froglicker is the latest creation from matt furie the legendary artist behind pepe immortalized within the hedz collection froglic represents a unique blend of digital art and blockchain innovation token information token symbol froglic contract address eth 0x4abd5745f326932b1b673bfa592a20d7bb6bc455 base contract address 0x0dd66db0f5c7ec8a4547338a0072fb8b64278c0c token standard erc20 total supply 10000000000 froglic uniswap and liquidity uniswap v2 pool 95 of the total supply is allocated to the uniswap v2 liquidity pool ensuring ample liquidity for trading this liquidity is locked to secure the ecosystem crosschain bridge froglic is making a leap to base utilizing secure crosschain bridging to expand its reach eth to base tokens are locked on eth and minted on base base to eth tokens are burnt on base and unlocked on eth supermigrate facilitates this transition making it seamless for users tokenomics team marketing 5 of the total supply is allocated with 25 for team efforts and 25 for marketing fair launch no presale is conducted the launch is fair with all tokens available for purchase postlaunch matt furie fund we are giving back to matt furie with a dedicated fund contributions can be made in eth or froglic and funds will be used to support matt future creations the wallet address for contributions is mattfurievaulteth gameplay and community engagement froglic is not just a token it an interactive experience play the froglic game directly on telegram where you can challenge competitors engage in daily challenges or compete with others leap and collect navigate through ascending platforms to earn rewards earn and share collect xp and referral fees by sharing your referral link conclusion froglic offers a charming blend of art gaming and blockchain technology join the community play the game and be a part of froglic journey across the blockchain socials twitter httpstwittercomfrogliccoin website httpswwwfroglicio telegram froglic play game on telegram froglic_bot Output: [{'address': '0x0dd66db0f5c7ec8a4547338a0072fb8b64278c0c', 'entity': 'Froglic', 'type': 'ICO'}, {'address': '0x4aBD5745F326932B1b673bfa592a20d7BB6bc455', 'entity': 'Froglic', 'type': 'ICO'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert suspecious rugpull a scammer dumped 400000000000000 dorami on eth for 187 eth worth 63k its price of has dropped 10000 scammer 0x9d6e4baf4958cddd23898d76668f94969b6813a7 httpstcolxs3hr28bm posted at 20240627 032903 utc Output: [{'address': '0x9D6E4BaF4958cDdd23898d76668F94969b6813a7', 'entity': 'Scammer', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that the investment contract on eth was under a flashloan attack with a loss of 79k attacker 0x29a4507d39e90e7e47f9047061f70eb41456091e httpstcobqamqzwe6p posted at 20240328 090738 utc Output: [{'address': '0x29a4507D39e90e7E47F9047061F70eB41456091E', 'entity': 'Attacker', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: zachxbt hi paolo this transaction is fraudulent please block it immediately httpstcopm2py8a1ka my friends wallet was drained tlymtfvmwdqt3gw7qt2scmbhjda3xaucwe Output: [{'address': 'TLymtFvMWdqt3gW7qT2ScmBhJda3xAucWE', 'entity': 'UNKNOWN', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped tons of gpt on bnbchain for 250k usdt after minted tons of gpt the price of gpt has dropped 10000 gpt 0x4349031b3a29ddb70c16982673a6b7a4b6ec3226 httpstcoiqlmffdrt9 posted at 20240227 060428 utc Output: [{'address': '0x4349031b3a29ddb70c16982673a6b7a4b6ec3226', 'entity': 'UNKNOWN', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: lookonchain giganticrebirth arkhamintel its not binance deposit address unknown unknown unknown 0x294270fc94a9c9f1aeac1b67858b6473f5ac3d2a doxxed 0x321 amp not gcr 0x4ab deposit here httpstcovvbmvxkr2d Output: [{'address': '0x294270fC94A9c9f1AeaC1b67858b6473f5AC3d2a', 'entity': 'Binance', 'type': 'Exchange'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 1302457392051 fight on eth for 84 eth worth 286k its price of has dropped 10000 scammer 0x1c0054b566581ae7fac5760d40bf3a7560112c66 httpstcooz6nn7hocn posted at 20240716 102646 utc Output: [{'address': '0x1C0054B566581ae7faC5760D40Bf3A7560112C66', 'entity': 'Scammer', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert a critical vulnerability was identified in the augustus v6 contract of paraswap impacting users who approved it affected users must revoke permissions to the augustus v6 contract unknown 0x00000000fdac7708d0d360bddc1bc7d097f47439 httpstconidojhkghu posted at 20240320 092836 utc Output: [{'address': '0x00000000FdAC7708D0D360BDDc1bc7d097F47439', 'entity': 'Augustus V6 contract', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert magpieprotocol announced that all users with any approval and funds in their wallets should remove their approvals to magpie contracts now revoke your approvals with httpstcob9cvifolj6 magpie contract addressesto be revoked ethereum magpie protocol exploit 042024 0xcf32c5bb41f7a302298a2d2072155800871baad3 polygon 0xcf32c5bb41f7a302298a2d2072155800871baad3 bsc 0xcf32c5bb41f7a302298a2d2072155800871baad3 avalanche magpie protocol exploit 042024 0x746b0ca3762e229d4dcbd22b4a10906aa788d396 arbitrum 0xcf32c5bb41f7a302298a2d2072155800871baad3 optimism 0xcf32c5bb41f7a302298a2d2072155800871baad3 polygon zkevm unknown 0x59b37ed62599f3d2f9a593be0153ef08702cb370 base magpie protocol exploit 042024 0x6a1431bb23e08e3209dae3130b441863855fc14b zksync unknown 0x5fe556bcf5fc7db6e075ca6f4cd4f8bbee2a3e54 blast unknown 0x956df8424b556f0076e8abf5481605f5a791cc7f blast 0x956df8424b556f0076e8abf5481605f5a791cc7f posted at 20240423 103201 utc Output: [{'address': '0x5fe556bcf5fc7db6e075ca6f4cd4f8bbee2a3e54', 'entity': 'magpie protocol', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: if you find my research insightful please consider donating to my ens gitcoin or sharing the thread zachxbteth 0x9d727911b54c455b0071a7b682fcf4bc444b5596 httpstconrjog2wod9 httpstcoljosmytvrh Output: [{'address': '0x9D727911B54C455B0071A7B682FcF4Bc444B5596', 'entity': 'ZachXBT Legal Fund Donation Address', 'type': 'Wallet'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that z123 on bnbchain was attacked with a loss of 135k attacker 0x3026c464d3bd6ef0ced0d49e80f171b58176ce32 httpstcoibwmpvsuep posted at 20240422 090653 utc Output: [{'address': '0x3026C464d3Bd6Ef0CeD0D49e80f171b58176Ce32', 'entity': 'Phishing Scam #225', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert peckshieldalert dolomite exchanges old contracts on ethereum were exploited resulted in a loss of 2m revoke your approvals to the contract 0x7f49ac8fdb38d24b686130e22579c7efe69b19c0 attacker 0x52522d35725836d48e12e64731fa170bcd9423bf httpstcorelxqzfxc8 posted at 20240321 014958 utc Output: [{'address': '0x7f49ac8fdb38d24b686130e22579c7efe69b19c0', 'entity': 'Dolomite Exchange', 'type': 'Exchange'}, {'address': '0x52522d35725836d48e12e64731fa170bcd9423bf', 'entity': 'UNKNOWN', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 1769800761000000 tnsr on eth for 172 eth worth 628k its price has dropped 10000 scammer 0xd12f0395bee391b3b79786f82b7a5dca72a7494a httpstcowyafnetuq4 posted at 20240409 074833 utc Output: [{'address': '0xD12F0395beE391B3b79786F82b7a5dCa72A7494a', 'entity': 'TNSR Token Rug pull (2024-04)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert a scammer dumped 1769800761000000 fake monad on eth for 74 weth worth 266k note that the fake monda is not a real token of monad_xyz its prices has dropped 100 scammer 0x89f9c5fec21919d8de008449fd81601c9920659c httpstcoln7l09qrhe posted at 20240411 035248 utc Output: [{'address': '0x89F9c5FeC21919D8De008449fd81601c9920659C', 'entity': 'MONDA Token Rug pull (2024-04)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: zachxbt looks like adam cochran supported project farmerfrens is draining users wallets here an address on arb they are currently sending assets to unknown unknown 0x97471fe292b3edfd98e3a08cda9b935eaa988228 Output: [{'address': '0x97471fe292b3edfd98e3a08cda9b935eaa988228', 'entity': 'farmerfrens', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that prismafi on eth was attacked with a loss of 2275 eth 81m attacker 0x7e39e3b3ff7adef2613d5cc49558eab74b9a4202 httpstcofterana7ar posted at 20240328 115056 utc Output: [{'address': '0x7e39e3b3ff7adef2613d5cc49558eab74b9a4202', 'entity': 'Prisma Finance Flashloan Attack (2024-03)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that sonnefinance on optimistic was under a serial attacks with a loss of more than 20m attacker 0xae4a7cde7c99fb98b0d5fa414aa40f0300531f43 httpstcop8hpwz87vh posted at 20240515 002015 utc Output: [{'address': '0xae4A7cDe7C99fb98B0D5fA414aa40F0300531F43', 'entity': 'Sonne Finance Exploit (2024-05)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: the attacker took control of the deployer address on blast and withdrew various assets worth 13m to 0x895a80371fc0e6987e27ddc7aa0e851bc3538ea8 bridged the fund to ethereum 0xd30ebc0a9acda91d383675eaab3ff24f06d07ece and deposit them into tornado cash Output: [{'address': '0x895a80371fc0e6987e27ddc7aa0e851bc3538ea8', 'entity': 'Blast', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: an hour ago we experienced a security breach by external attackers resulting in the theft of some funds from our staking contract we are taking immediate measures to address the situation by withdrawing liquidity from the current opsec contract we need to act fast to recover and migrate the contract address ca to support this urgent migration we need your help please send your opsec tokens to the recovery address below to receive v2 tokens during the migration opsec recovery address 0x362538c16a2868038ae72b608c080b6433f979c9 snapshot of current holders of v2 airdrop was taken please do not buy current opsec token key points funds are safe you can relax and follow the instructions provided above marketing and development wallets are secure we are migrating them to isolated fresh wallets postmigration benefits include increased contract security no contract dumps lower tax rates higher liquidity user safety all users are safe if you are an opsec holder who did not purchase after 1515 utc wednesday 10 july 2024 you are completely safe we will handle individual cases as needed upcoming updates more details will be shared asap regarding the root cause of the breach an ama is live now on telegram addressing the situation rest assured this message is from the internal opsec team our twitter account has not been hacked end of post beware of scams Output: [{'address': '0x362538c16a2868038AE72B608c080B6433f979C9', 'entity': 'OpSec team', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a deployer dumped tons of lola on bnbchain for 400k usdt after minted tons of lola the price of lola has dropped 10000 lola 0x6fa9f76cd08680a487b96abcbe82ccf309cc6cff httpstcofqvt42vorv posted at 20240205 025641 utc Output: [{'address': '0x6fa9f76cd08680a487b96abcbe82ccf309cc6cff', 'entity': '$Lola', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 1769800761000000 rez on eth for 86 eth worth 272k note that it is not real token of renzoprotocol its price has dropped 10000 scammer 0x77f3fa7aff7c99713b4365115f7ec6ce57c9f203 httpstcou6i0ltyahk posted at 20240425 012649 utc Output: [{'address': '0x77f3FA7aff7c99713B4365115F7EC6Ce57C9F203', 'entity': 'Phishing Scam #226', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: lookonchain shelly 8f772bk2yverhz8whxj92sfwkpesg973sqjga9ujpump httpstcoyldvqpxwrj Output: [{'address': '8f772bk2YVErHz8WhXJ92sfwKpEsG973SQjGa9ujpump', 'entity': '$Shelly', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: in our attempt to warn users of risky tokens as early as possible hashdit will continually share our consolidated list regularly 0xbcf621d79f1da2136f732ac9c0fe7af2268d2b79 0x8bec5226504399997d1efef15cd05623b3cf8888 0x7c51cc9485758883b45e544e4a62daa2c6d962d8 0xc298d75509943156f1b43f8ba9f484827a22c2c2 0xefa4cda666c6a1c03a38e4885266b019dec57662 educate yourself about the risks and always do your own research dyor when diving into new ventures psa do reach out to us on tg or our mail to dispute any disagreements we will be glad to resolve them thank you and stay vigilant update Output: [{'address': '0x8bec5226504399997d1efef15cd05623b3cf8888', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected socketdottech was under an attack with a loss of 33m due to input validation issue attacker 0x50df5a2217588772471b84adbbe4194a2ed39066 all the contracts were paused by socket and recommend users revoking the approvals httpstcofs9zzalhko posted at 20240117 011804 utc Output: [{'address': '0x50df5a2217588772471b84adbbe4194a2ed39066', 'entity': 'Socket Protocol Exploit (2024-01)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout dected that openleverage on bnbchain was attacked mutiple times with a loss of 230k stolen assets are not sit at 0x5bb5b6d41c3e5e41d9b9ed33d12f1537a1293d5f attacker 0xd0c8af170397c04525a02234b65e9a39969f4e93 httpstco43rvfgqcvd posted at 20240401 055946 utc Output: [{'address': '0x5bb5b6d41c3e5e41d9b9ed33d12f1537a1293d5f', 'entity': 'Reported by the CDA ', 'type': 'Hack'}, {'address': '0xd0c8af170397c04525a02234b65e9a39969f4e93', 'entity': 'OpenLeverage Exploit (2024-04)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: important update from minterest attention minterest users we are currently investigating an exploit on minterest as a precautionary measure we have temporarily paused some operations on the minterest app paused supply borrow active repay withdraw suspect address 0x618f768af6291705eb13e0b2e96600b3851911d1 txn hash 0xb3c4c313a8d3e2843c9e6e313b199d7339211cdc70c2eca9f4d88b1e155fd6bd contract address 0x9b506584a0f2176494d5f9c858437b54df97bc06 no further actions can be taken by the exploiter at this timewe are working diligently to resolve this issue and full operations will resume shortly thank you for your patience and understanding during this process Output: [{'address': '0x9B506584a0F2176494D5F9C858437b54DF97Bc06', 'entity': 'Minterest', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: lookonchain fexir1 pboys is taking off ca b3i6okurnq7kyjahgfdbvzcmedbbuqf4xgn6apzy6u3t cto x httpstcohotdvvvubu website httpstcoxg5nhbeutq tg portal httpstconoacaz1qkw httpstcoinahjdxbkr Output: [{'address': 'B3i6oKURnq7kyJAhGFdBVzcmedbbuQf4XgN6APZY6u3t', 'entity': '$PBOYS', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: certikalert a strong and active community defines a project dyor has the best community ever with an hardworking dev come and have a look ca bgfp2wx1fx8qb6714844jgiemnpn3jjb5xa1zcjouvcn twitter httpstco0vbrzr9vpr httpstcomnzqo4udw5 Output: [{'address': 'BgfP2WX1Fx8qB6714844JGiemnPn3jJb5xa1ZCjouvCn', 'entity': 'UNKNOWN', 'type': 'Community reported scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: khamzat your team is incompetent as you directly linked the team wallets with the insider wallets buying up 78 of the supply why do all of you instantly nuke your reputation with meme coin scams summary using timing analysis confirms 71 insider and dev team wallets is directly linked as they were funded by same address on ethereum smash dev solana address was funded via ethereum unknown 0x8ae40d1e89cd7333efe42a35969f0b8548fe54a8 0x8ae sends 05 eth to binance on july 2 at 221 pm utc 0xe0bba2925ea629ac918f700dde196a2f12e13b1d69ace379224f09ae34238652 smash dev receives 1183 sol from binance on july 2 at 225 pm utc 2tnzi8cmraat1y9dpuyzp5rlufufnunvgerypsh369lukdryl5vcb8nfbiwh1y5bcytuqer1gfksbnjxyv69tzgp 0x8ae sends 3825 eth to sideshift on july 2 at 222 pm utc 0x4bc49dbda6ede2fa82640e3caaedf4e593c2c9f2cdb4b77f6d3f693b409e9054 smash 71 insider wallet receives 8655 sol from sideshift on july 2 at 223 pm utc uxqjyco11h3cpk7tgxoray2gxoqfmtm2khlcrz2cfjnv6sugvhhzbv1eiithkjeqe8w6vypkortwjebam56pffx 862 sol was dispersed to 24 addresses in these two txns 24hydatqyphrgu5vnyj9biwyszytxzppxsxy2j7xkbhcpunob4csn1lfgrxssxjcvvcl6ax5lwjapbv9dckdtvd1 3ngc1qajz8hyxe8hdnkwoqf6kqvxaeln9cd6ztwrdhgwo3hnrkzm1usrmwo8pfwlzmctzhx1lglejuh1fkwf94lp those 24 addresses bought up 712m smash or 712 of entire supply 2ptq3zxvqkwi1frwlbp2r8r35dd4gcjhjtpnt5nybqql 2zkoctu2zuta5scpm5ksassjqmpum7pbbzpzvoihzqyq 4cm1qppakmjpz4hbkyxzk57k1s3xszciremervfgqexd 55lpcraktrmy8zg67xcovezu2bpr2rgebfnvtz4tzhd5 5wj8axwzslm56qirzgibhwrf4vgjaqeyhaexhqxijoo8 5qid7mknktckrjcsmxg6zcjcjfps9zhywejzsr6snkgh 85tq95nklfsbpifeungsaltsx8gnzbke5qkejysyrgox 86ud7yybu5p2j4wdgnkcoxpmjny5npxak3erjjukabp7 8v9djfdeapadmnzj7ckcemsgalpmzedsejhrwaujwcla b1mzd5yweddnjjfza1238dtk7lpygquhc934clr2rgbd cvyf45jxhp6rvwp44cvmwtg1ylwm2e9hmqs2k3pdywsh czbbs3ajuqmtpgpevekxfah6dbskamubk5vgngq1s1v4 dqs8jreepxrfzk98cgmdtuq6va7no3xshgk7pvjhqp57 dwytf1fqp73w6vcvylgadf5tosm6qfbbt3dxyux9hava f86tvrovfsqtqdmbobkzug4eeyfixr2uzotn7rivyway h129asevcf4gq6p3hxcmfzailrrncs5dj6kwvznh4xt8 hdttactvkdr7qhsfucfa2c9fepyqwqygeuwmphtqse5c hpjgsytk9opifuabeysctuvqc7rz18pkdkzvpx9nmeiu j31vecv5zshbxknwrtpebgahjlwzdvswshby6gxwapff 2evaw7ozm46ixsysauseqnimksowd2n2pnfcbiftxmyc 3zujxarkzbqcu5vsmpuwhbfuemewybradfhrwdadpvmx fcwejowhotm8vbqpqffdc5h7uyensteazjx89rvgkw8y el9wskztp8vdy9vxrcrad8jxz1f3fd4akcaxdytsudp 268siztgfne5oyyu54f29v9phidbwtkx8e19nt7ubnmx then the 712m smash got dispersed to from each address to more addresses where i stopped tracking Output: [{'address': '2pTq3ZxVQkwi1FRWLbp2R8r35dd4GcJHjTPnT5NyBQQL', 'entity': 'Khamzat Smash', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert it turns doughfina on eth was under multiple attacks with a loss of more than 18m all the stolen assets are swapped for eth now sit at 0x2913d90d94c9833b11a3e77f136da03075c04a0f attacker 0x67104175fc5fabbdb5a1876c3914e04b94c71741 httpstco9vgyttqtzf posted at 20240712 071716 utc Output: [{'address': '0x67104175fc5fabbdb5A1876c3914e04B94c71741', 'entity': 'Reported by the CDA ', 'type': 'Phishing'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert rugpull a scammer dumped 1603040070640000 gensyn on eth for 81 eth worth 256k its price has dropped 10000 scammer 0x8db6ce0bdc84a68cfc756b99c036f55e72172540 httpstcop7nju1fwmf posted at 20240426 093639 utc Output: [{'address': '0x8DB6ce0BDc84A68CFC756b99c036f55e72172540', 'entity': 'Rugpull Gensyn dumper (04- 2024)', 'type': 'Scam'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert pumpdotfun on solana was attacked with a loss of 19m due to the private key compromised from a former employee the httpstco21l55r7ngw team paused the trading and redeployed the contract to prevent the attack attacker 7ihn8qatfnodtrtqgulczbut3phwpdtu5brcu4it2pap posted at 20240517 020049 utc Output: [{'address': '7ihN8QaTfNoDTRTQGULCzbUT3PHwPDTu5Brcu4iT2paP', 'entity': 'Pump.fun Exploit (2024-05)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that gpu on bnbchain was attacked with a loss of 32k due to a logic flaw attacker 0xcc78063840428c5ae53f3dc6d80759984788cbc0 httpstcowbiu18l48s posted at 20240508 104719 utc Output: [{'address': '0xcc78063840428c5ae53f3dc6d80759984788cbc0', 'entity': 'GPU token exploit (05-2024)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert our metascout detected that a contract tokentracker0x424f71 on bnbchain under a series attacks with a loss of 85k attacker 0x762fac581205423f5029f01f699b78582485d054 httpstcovl1vri27gu posted at 20240528 073946 utc Output: [{'address': '0x762faC581205423F5029F01F699B78582485D054', 'entity': 'BTC Token Exploit (2024-05)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert suspicious tokens transfers from fixedfloat wallets detected the tokens worth 28m including eth usdt weth dai and usdc 2m of them swapped for eth and desposited into the exch exchange it is a potential private key leakage issue attacker 0xfa0200a7b73f2b36d14815336483039ecc6dea8b posted at 20240402 094116 utc Output: [{'address': '0xFA0200A7b73F2B36D14815336483039ecC6dea8b', 'entity': 'Fixedfloat.com Exploit (2024-02)', 'type': 'Hack'}]

Instruction: You are an expert cryptocurrency investigator and analyst. Use this social media post as context to detect and classify the cryptocurrency address mentioned. This is the social media tweet: ${tweets} #TASK 1 : You should extract the crypto address from the tweet. Important : You can use these regex to extract the crypto addresses : /(0x)[0-9A-Fa-f]{40}/; /[13][1-9A-HJ-NP-Za-km-z]{25,34}|bc1[qp][0-9A-Za-z]{37,62}/; /T[1-9A-HJ-NP-Za-km-z]{33}/ /r[0-9a-zA-Z]{24,34}/ /[1-9A-HJ-NP-Za-km-z]{32,44}/ /1[0-9a-zA-Z]{44,50}/ /A[a-km-zA-HJ-NP-Z1-9]{25,34}/ Even if you detect a cryptocurrency address that does not match these crypto addreses, extract it. Then classify the cryptocurrency address into their corresponding type based on the post context. 1) Airdrop : type refers to addresses used to distribute free cryptocurrencies or tokens to other wallets to advertise or draw attention to a project. " 2) Darkweb : refers to addresses and entities that are linked to dark web activities such as dark web marketplaces, dark web scams, stolen funds, etc绐讹溅 They should be treated carefully. 3)Donations : refers to addresses that are used by projects, companies, or non-lucrative associations to raise funds. 4)Exchange : refers to Centralized Exchanges (CEXs) platforms providing services such as trading, swaping, staking and storage of crypto currencies. As a result, CEXs are considered as Virtual Asset Service Providers (VASPs). Generally, CEXs need to be regulated by financial regulators within the countries they are willing to do business. Usually, CEXs require Know-Your-Customer (KYC) verification and need to implement AML and CTF controls to be compliant with the regulatory requirements. 5)Gambling : refers to addresses that are used in crypto casinos for example. As with traditional casinos, money laundering is a risk because you can enter with some crypto amount and get out with a different amount on another address. 6) ICO : refers to addresses used to raise funds for new cryptocurrency projects. Unlike Donations, investors are getting a counterparty for their investment, often in the form of tokens. The risk is medium since it is difficult to assess the viability of the project. : 7) Phishing : refers to addresses that are used to steal sensitive data such as private keys by posing as a trustworthy entity, an exchange platform for example. 8)Scam : refers to addresses that are used in various scams aimed at defrauding people out of their cryptos. A scam could be fake giveaways, ransomware, Ponzi schemes .. 9) Hack: refers to addresses that were used to hack or exploit cryptocurrency platforms and resulted in a loss of funds of the hacked/exploited platforms. 10) Victim : If the adress belongs to a vistim taht was hacked scammed ... If none of the previous entities matches then use the type : "Community reported scam". Remember only use this type if none of all the other types is accurate. #TASK 2 : Besides the type, you need to extract the entity for the address. The entity is the name of the group or entity the address belongs to. If no entity is mentioned return the entity UNKNOWN. Try to get the entity from the conetxt of the tweet. For exemple "Over the past 1.5 months one person has created 114 meme coin scams" the entity here is "meme coin scams". #THE OUTPUT FORMAT : The response should be a JSON file with 3 keys : the address,the associated entity and the type. The address, entity and type must be all wrapped in the same json {}. You only generate the responses as an array of JSON based on the provided context. This is an example : [{"address": "bc1qmd3kzw0ge45eag7qpuhyxa5kdv4hqh3kxp44dg","entity": "Lazarus Group", "type": "Community reported scam"}]. You will return only return the array without extra informations. The json have only three keys : "address","entity","type". If there are multiple addresses or even if there is one address you will return an array [] of jsons as follows : [{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""},{"address": "","entity": "", "type": ""}....]. Remember you return directly the result without markdown. Input: tweet from metatrustalert metatrustalert suspicious transactions happened to lifiprotocol on eth resulted in assets loss revoke approvals for their contract 0x1231deb6f5749ef6ce6943a275a1d3e7486f4eae now posted at 20240716 125545 utc Output: [{'address': '0x1231deb6f5749ef6ce6943a275a1d3e7486f4eae', 'entity': 'LI.FI', 'type': 'Cross-chain Bridge Protocol'}]