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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'}]