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maxus ( rocket )

https://en.wikipedia.org/wiki/Maxus_%28rocket%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16003024-1.html.csv

superlative

the rocket from the maxus 1b mission has reached the highest apogee among all maxus missions .

{'scope': 'all', 'col_superlative': '5', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'apogee'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; apogee }'}, 'mission'], 'result': 'maxus 1b', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; apogee } ; mission }'}, 'maxus 1b'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; apogee } ; mission } ; maxus 1b } = true', 'tointer': 'select the row whose apogee record of all rows is maximum . the mission record of this row is maxus 1b .'}

eq { hop { argmax { all_rows ; apogee } ; mission } ; maxus 1b } = true

select the row whose apogee record of all rows is maximum . the mission record of this row is maxus 1b .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'apogee_5': 5, 'mission_6': 6, 'maxus 1b_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'apogee_5': 'apogee', 'mission_6': 'mission', 'maxus 1b_7': 'maxus 1b'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'apogee_5': [0], 'mission_6': [1], 'maxus 1b_7': [2]}

['mission', 'date', 'launch site', 'motor', 'apogee']

[['maxus 1', '1991 may 8', 'esrange', 'castor 4b', '154 km'], ['maxus 1b', '1992 nov 8', 'esrange', 'castor 4b', '717 km'], ['maxus 2', '1995 nov 29', 'esrange', 'castor 4b', '706 km'], ['maxus 3', '1998 nov 24', 'esrange', 'castor 4b', '713 km'], ['maxus 4', '2001 apr 29', 'esrange', 'castor 4b', '704 km'], ['maxus 5', '2003 apr 1', 'esrange', 'castor 4b', '703 km'], ['maxus 6', '2004 nov 22', 'esrange', 'castor 4b', '707 km'], ['maxus 7', '2006 may 2', 'esrange', 'castor 4b', '705 km'], ['maxus 8', '2010 march 26', 'esrange', 'castor 4b', '703 km']]

2006 u.s. open ( golf )

https://en.wikipedia.org/wiki/2006_U.S._Open_%28golf%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12523044-5.html.csv

count

the united states is represented by 5 players .

{'scope': 'all', 'criterion': 'equal', 'value': 'united states', 'result': '5', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; country ; united states }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; united states } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; united states } } ; 5 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 5 .'}

eq { count { filter_eq { all_rows ; country ; united states } } ; 5 } = true

select the rows whose country record fuzzily matches to united states . the number of such rows is 5 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'country_5': 5, 'united states_6': 6, '5_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'country_5': 'country', 'united states_6': 'united states', '5_7': '5'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'united states_6': [0], '5_7': [2]}

['place', 'player', 'country', 'score', 'to par']

[['1', 'steve stricker', 'united states', '70 + 69 = 139', '- 1'], ['2', 'colin montgomerie', 'scotland', '69 + 71 = 140', 'e'], ['t3', 'kenneth ferrie', 'england', '71 + 70 = 141', '+ 1'], ['t3', 'geoff ogilvy', 'australia', '71 + 70 = 141', '+ 1'], ['t5', 'jim furyk', 'united states', '70 + 72 = 142', '+ 2'], ['t5', 'pádraig harrington', 'ireland', '70 + 72 = 142', '+ 2'], ['t7', 'jason dufner', 'united states', '72 + 71 = 143', '+ 3'], ['t7', 'graeme mcdowell', 'northern ireland', '71 + 72 = 143', '+ 3'], ['t7', 'phil mickelson', 'united states', '70 + 73 = 143', '+ 3'], ['t7', 'arron oberholser', 'united states', '75 + 68 = 143', '+ 3']]

2008 world junior curling championships

https://en.wikipedia.org/wiki/2008_World_Junior_Curling_Championships

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15066680-55.html.csv

unique

the only team from asia to participate in the 2008 world junior curling championship was japan .

{'scope': 'all', 'row': '4', 'col': '1', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'japan', 'subset': None}

{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to japan .', 'tostr': 'filter_eq { all_rows ; country ; japan }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; japan } } = true', 'tointer': 'select the rows whose country record fuzzily matches to japan . there is only one such row in the table .'}

only { filter_eq { all_rows ; country ; japan } } = true

select the rows whose country record fuzzily matches to japan . there is only one such row in the table .

2

2

{'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'country_4': 4, 'japan_5': 5}

{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'country_4': 'country', 'japan_5': 'japan'}

{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'country_4': [0], 'japan_5': [0]}

['country', 'skip', 'third', 'second', 'lead']

[['canada', 'kaitlyn lawes', 'jenna loder', 'liz peters', 'sarah wazney'], ['denmark', 'madeleine dupont', 'jeanne ellegaard', 'mona sylvest nielsen', 'lisa sylvest nielsen'], ['germany', 'frederike templin', 'pia - lisa schöll', 'ann kathrin bastian', 'simone ackermann'], ['japan', 'satsuki fujisawa', 'shiori fujisawa', 'yui okabe', 'madoka shinoo'], ['norway', 'anneline skårsmoen', 'kjersti husby', 'rita nerlien', 'hanne munkvold'], ['russia', 'ludmila privivkova', 'margarita fomina', 'daria kozlova', 'ekaterina galkina'], ['scotland', 'eve muirhead', 'kerry barr', 'vicki adams', 'sarah macintyre'], ['sweden', 'cecilia östlund', 'sara carlsson', 'anna domeij', 'liselotta lennartsson'], ['switzerland', 'michèle jäggi', 'marisa winkelhausen', 'nicole schwagli', 'isabel kurt'], ['united states', 'nina spatola', 'rebecca hamilton', 'anna plys', 'jenna haag']]

list of tvb series ( 1998 )

https://en.wikipedia.org/wiki/List_of_TVB_series_%281998%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18493407-1.html.csv

superlative

the 1998 tvb series " dark tales ii " had the highest number of episodes .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'number of episodes'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; number of episodes }'}, 'english title ( chinese title )'], 'result': 'dark tales ii 聊齋 ( 貳 )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; number of episodes } ; english title ( chinese title ) }'}, 'dark tales ii 聊齋 ( 貳 )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; number of episodes } ; english title ( chinese title ) } ; dark tales ii 聊齋 ( 貳 ) } = true', 'tointer': 'select the row whose number of episodes record of all rows is maximum . the english title ( chinese title ) record of this row is dark tales ii 聊齋 ( 貳 ) .'}

eq { hop { argmax { all_rows ; number of episodes } ; english title ( chinese title ) } ; dark tales ii 聊齋 ( 貳 ) } = true

select the row whose number of episodes record of all rows is maximum . the english title ( chinese title ) record of this row is dark tales ii 聊齋 ( 貳 ) .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'number of episodes_5': 5, 'english title (chinese title)_6': 6, 'dark tales ii 聊齋 (貳)_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'number of episodes_5': 'number of episodes', 'english title (chinese title)_6': 'english title ( chinese title )', 'dark tales ii 聊齋 (貳)_7': 'dark tales ii 聊齋 ( 貳 )'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'number of episodes_5': [0], 'english title (chinese title)_6': [1], 'dark tales ii 聊齋 (貳)_7': [2]}

['airing date', 'english title ( chinese title )', 'number of episodes', 'genre', 'official website']

[['12 jan - 6 feb', 'a tough side of a lady 花木蘭', '20', 'costume action', 'official website'], ['9 feb - 6 mar', "a place of one 's own 大澳的天空", '20', 'modern drama', 'official website'], ['9 mar - 1 may', 'dark tales ii 聊齋 ( 貳 )', '50', 'costume drama', 'official website'], ['4 may - 29 may', 'as sure as fate 師奶強人', '20', 'modern drama', 'official website'], ['1 jun - 31 jul', 'the duke of mount deer 鹿鼎記', '45', 'costume drama', 'official website'], ['3 aug - 4 sep', 'old time buddy - to catch a thief 難兄難弟之神探李奇', '25', 'period drama', 'official website'], ['7 sep - 25 sep', 'simply ordinary 林世榮', '15', 'costume drama', 'official website'], ['28 sep - 23 oct', 'web of love 網上有情人', '20', 'modern drama', 'official website'], ['26 oct - 18 dec', 'journey to the west ii 西遊記 ( 貳 )', '42', 'costume drama', 'official website'], ['21 dec 1998 - 15 jan 1999', 'moments of endearment 外父唔怕做', '20', 'modern drama', 'official website']]

1920 u.s. open ( golf )

https://en.wikipedia.org/wiki/1920_U.S._Open_%28golf%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18007015-1.html.csv

aggregation

the 1920 us open paid out $ 1497 in total money to the top 10 finishers .

{'scope': 'all', 'col': '6', 'type': 'sum', 'result': '1497', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'money'], 'result': '1497', 'ind': 0, 'tostr': 'sum { all_rows ; money }'}, '1497'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; money } ; 1497 } = true', 'tointer': 'the sum of the money record of all rows is 1497 .'}

round_eq { sum { all_rows ; money } ; 1497 } = true

the sum of the money record of all rows is 1497 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'money_4': 4, '1497_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'money_4': 'money', '1497_5': '1497'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'money_4': [0], '1497_5': [1]}

['place', 'player', 'country', 'score', 'to par', 'money']

[['1', 'ted ray', 'jersey', '74 + 73 + 73 + 75 = 295', '+ 11', '500'], ['t2', 'jack burke , sr', 'united states', '75 + 77 + 72 + 72 = 296', '+ 12', '188'], ['t2', 'leo diegel', 'united states', '72 + 74 + 73 + 77 = 296', '+ 12', '188'], ['t2', 'jock hutchison', 'united states', '69 + 76 + 74 + 77 = 296', '+ 12', '188'], ['t2', 'harry vardon', 'jersey', '74 + 73 + 71 + 78 = 296', '+ 12', '188'], ['t6', 'jim barnes', 'england', '76 + 70 + 76 + 76 = 298', '+ 14', '90'], ['t6', 'chick evans ( a )', 'united states', '74 + 76 + 73 + 75 = 298', '+ 14', '0'], ['t8', 'bobby jones ( a )', 'united states', '78 + 74 + 70 + 77 = 299', '+ 15', '0'], ['t8', 'willie macfarlane', 'scotland', '76 + 75 + 74 + 74 = 299', '+ 15', '80'], ['10', 'bob macdonald', 'scotland', '73 + 78 + 71 + 78 = 300', '+ 16', '75']]

united states house of representatives elections , 1972

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1972

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341707-20.html.csv

count

two of the candidates elected to the house of representative were first elected into office in the sixties .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': '196', 'result': '2', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'first elected', '196'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first elected record fuzzily matches to 196 .', 'tostr': 'filter_eq { all_rows ; first elected ; 196 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; first elected ; 196 } }', 'tointer': 'select the rows whose first elected record fuzzily matches to 196 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; first elected ; 196 } } ; 2 } = true', 'tointer': 'select the rows whose first elected record fuzzily matches to 196 . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; first elected ; 196 } } ; 2 } = true

select the rows whose first elected record fuzzily matches to 196 . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '196_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '196_6': '196', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '196_6': [0], '2_7': [2]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['louisiana 1', 'f edward hebert', 'democratic', '1940', 're - elected', 'f edward hebert ( d ) unopposed'], ['louisiana 2', 'hale boggs', 'democratic', '1946', 're - elected', 'hale boggs ( d ) unopposed'], ['louisiana 4', 'joe waggonner', 'democratic', '1961', 're - elected', 'joe waggonner ( d ) unopposed'], ['louisiana 5', 'otto passman', 'democratic', '1946', 're - elected', 'otto passman ( d ) unopposed'], ['louisiana 6', 'john rarick', 'democratic', '1966', 're - elected', 'john rarick ( d ) unopposed']]

kanpur anwarganj railway station

https://en.wikipedia.org/wiki/Kanpur_Anwarganj_railway_station

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27011761-2.html.csv

comparative

train 5037 arrives earlier in the day than train 4723 does .

{'row_1': '1', 'row_2': '3', 'col': '3', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'train no', '5037'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose train no record fuzzily matches to 5037 .', 'tostr': 'filter_eq { all_rows ; train no ; 5037 }'}, 'arrival'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; train no ; 5037 } ; arrival }', 'tointer': 'select the rows whose train no record fuzzily matches to 5037 . take the arrival record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'train no', '4723'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose train no record fuzzily matches to 4723 .', 'tostr': 'filter_eq { all_rows ; train no ; 4723 }'}, 'arrival'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; train no ; 4723 } ; arrival }', 'tointer': 'select the rows whose train no record fuzzily matches to 4723 . take the arrival record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; train no ; 5037 } ; arrival } ; hop { filter_eq { all_rows ; train no ; 4723 } ; arrival } } = true', 'tointer': 'select the rows whose train no record fuzzily matches to 5037 . take the arrival record of this row . select the rows whose train no record fuzzily matches to 4723 . take the arrival record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; train no ; 5037 } ; arrival } ; hop { filter_eq { all_rows ; train no ; 4723 } ; arrival } } = true

select the rows whose train no record fuzzily matches to 5037 . take the arrival record of this row . select the rows whose train no record fuzzily matches to 4723 . take the arrival record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'train no_7': 7, '5037_8': 8, 'arrival_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'train no_11': 11, '4723_12': 12, 'arrival_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'train no_7': 'train no', '5037_8': '5037', 'arrival_9': 'arrival', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'train no_11': 'train no', '4723_12': '4723', 'arrival_13': 'arrival'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'train no_7': [0], '5037_8': [0], 'arrival_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'train no_11': [1], '4723_12': [1], 'arrival_13': [3]}

['train no', 'train name', 'arrival', 'departure', 'days', 'platform no']

[['5037', 'kanpur - farrukhabad express', '10:55', '11:05', 'daily', 'platform no2'], ['5038', 'farrukhabad - kanpur express', '17:25', '17:30', 'daily', 'platform no2'], ['4723', 'kanpur - bhiwani kalindi express', '17:15', '17:25', 'daily', 'platform no2'], ['4724', 'bhiwani - kanpur kalindi express', '11:00', '10:55', 'daily', 'platform no1'], ['15037', 'kanpur - kasganj express', '10:45', '10:55', 'daily', 'platform no3']]

list of intel atom microprocessors

https://en.wikipedia.org/wiki/List_of_Intel_Atom_microprocessors

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16729930-11.html.csv

aggregation

the average release price in us dollars for intel atom microprocessors is about 114.7 dollars .

{'scope': 'subset', 'col': '10', 'type': 'average', 'result': '114.7', 'subset': {'col': '10', 'criterion': 'not_equal', 'value': 'n/a'}}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'release price ( usd )', 'n/a'], 'result': None, 'ind': 0, 'tostr': 'filter_not_eq { all_rows ; release price ( usd ) ; n/a }', 'tointer': 'select the rows whose release price ( usd ) record does not match to n/a .'}, 'release price ( usd )'], 'result': '114.7', 'ind': 1, 'tostr': 'avg { filter_not_eq { all_rows ; release price ( usd ) ; n/a } ; release price ( usd ) }'}, '114.7'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_not_eq { all_rows ; release price ( usd ) ; n/a } ; release price ( usd ) } ; 114.7 } = true', 'tointer': 'select the rows whose release price ( usd ) record does not match to n/a . the average of the release price ( usd ) record of these rows is 114.7 .'}

round_eq { avg { filter_not_eq { all_rows ; release price ( usd ) ; n/a } ; release price ( usd ) } ; 114.7 } = true

select the rows whose release price ( usd ) record does not match to n/a . the average of the release price ( usd ) record of these rows is 114.7 .

3

3

{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_4': 4, 'release price ( usd )_5': 5, 'n/a_6': 6, 'release price ( usd )_7': 7, '114.7_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_4': 'all_rows', 'release price ( usd )_5': 'release price ( usd )', 'n/a_6': 'n/a', 'release price ( usd )_7': 'release price ( usd )', '114.7_8': '114.7'}

{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_not_eq_0': [1], 'all_rows_4': [0], 'release price ( usd )_5': [0], 'n/a_6': [0], 'release price ( usd )_7': [1], '114.7_8': [2]}

['model number', 'sspec number', 'frequency', 'l2 cache', 'mult', 'voltage', 'socket', 'release date', 'part number ( s )', 'release price ( usd )']

[['atom z500', 'slb6q ( c0 )', '800 mhz', '512 kb', '8', '0.712 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566uc800de', '45'], ['atom z510', 'slb2c ( c0 )', '1.1 ghz', '512 kb', '11', '0.75 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566uc005de', '45'], ['atom z510p', 'slgpq ( c0 )', '1.1 ghz', '512 kb', '11', '0.8 - 1.1 v', 'bga 437', 'march 2 , 2009', 'ch80566ec005dw', 'n / a'], ['atom z510pt', 'slgpr ( c0 )', '1.1 ghz', '512 kb', '11', '0.75 - 1.1 v', 'bga 437', 'march 2 , 2009', 'ch80566ec005dt', 'n / a'], ['atom z515', 'slgmg ( c0 )', '1.2 ghz', '512 kb', '12', '0.712 - 1v', 'bga 441', 'april 8 , 2009', 'ac80566uc009dv', 'n / a'], ['atom z520', 'slb2h ( c0 )', '1.33 ghz', '512 kb', '10', '0.75 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566ue014dw', '65'], ['atom z520pt', 'slgpp ( c0 )', '1.33 ghz', '512 kb', '10', '0.9 - 1.1 v', 'bga 437', 'march 2 , 2009', 'ch80566ee014dt', 'n / a'], ['atom z530', 'slb6p ( c0 )', '1.6 ghz', '512 kb', '12', '0.75 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566ue025dw', '95'], ['atom z530p', 'slgpn ( c0 )', '1.6 ghz', '512 kb', '12', '0.8 - 1.1 v', 'bga 437', 'march 2 , 2009', 'ch80566ee025dw', 'n / a'], ['atom z540', 'slb2 m ( c0 )', '1.87 ghz', '512 kb', '14', '0.75 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566ue036dw', '160'], ['atom z550', 'slgpt ( c0 )', '2 ghz', '512 kb', '15', '0.75 - 1.1 v', 'bga 441', 'april 8 , 2009', 'ac80566ue041dw', '249.47 retail'], ['atom z560', 'slh63 ( c0 )', '2.13 ghz', '512 kb', '16', '0.75 - 1.1 v', 'bga 441', 'q2 2010', 'ac80566ue046dw', '144']]

southeast asian games

https://en.wikipedia.org/wiki/Southeast_Asian_Games

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1575383-9.html.csv

superlative

in the southeast asian games , for the teams that have over 100 gold medals , the country with the most silver medals is indonesia .

{'scope': 'subset', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1,2', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': '100'}}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'gold', '100'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; gold ; 100 }', 'tointer': 'select the rows whose gold record is greater than 100 .'}, 'silver'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_greater { all_rows ; gold ; 100 } ; silver }'}, 'country'], 'result': 'indonesia', 'ind': 2, 'tostr': 'hop { argmax { filter_greater { all_rows ; gold ; 100 } ; silver } ; country }'}, 'indonesia'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_greater { all_rows ; gold ; 100 } ; silver } ; country } ; indonesia } = true', 'tointer': 'select the rows whose gold record is greater than 100 . select the row whose silver record of these rows is maximum . the country record of this row is indonesia .'}

eq { hop { argmax { filter_greater { all_rows ; gold ; 100 } ; silver } ; country } ; indonesia } = true

select the rows whose gold record is greater than 100 . select the row whose silver record of these rows is maximum . the country record of this row is indonesia .

4

4

{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'gold_6': 6, '100_7': 7, 'silver_8': 8, 'country_9': 9, 'indonesia_10': 10}

{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'gold_6': 'gold', '100_7': '100', 'silver_8': 'silver', 'country_9': 'country', 'indonesia_10': 'indonesia'}

{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'gold_6': [0], '100_7': [0], 'silver_8': [1], 'country_9': [2], 'indonesia_10': [3]}

['country', 'gold', 'silver', 'bronze', 'total']

[['indonesia', '1602', '1413', '1395', '4410'], ['thailand', '1513', '1318', '1315', '4146'], ['philippines', '836', '971', '1191', '2998'], ['malaysia', '805', '772', '1067', '2644'], ['vietnam', '586', '540', '618', '1744'], ['singapore', '508', '559', '841', '1906'], ['myanmar', '249', '410', '579', '1238'], ['laos', '53', '60', '170', '283'], ['cambodia', '11', '26', '104', '151'], ['brunei', '10', '38', '120', '168'], ['timor - leste', '1', '1', '12', '14']]

1987 denver dynamite season

https://en.wikipedia.org/wiki/1987_Denver_Dynamite_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11938965-7.html.csv

aggregation

the average sack per player for the 1987 denver dynamite season was 1 .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '1', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'sack'], 'result': '1', 'ind': 0, 'tostr': 'avg { all_rows ; sack }'}, '1'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; sack } ; 1 } = true', 'tointer': 'the average of the sack record of all rows is 1 .'}

round_eq { avg { all_rows ; sack } ; 1 } = true

the average of the sack record of all rows is 1 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'sack_4': 4, '1_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'sack_4': 'sack', '1_5': '1'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'sack_4': [0], '1_5': [1]}

['player', 'tackles', 'solo', 'assisted', 'sack', 'yards', "td 's"]

[['william cotman', '26.5', '24', '5', '0', '10', '0'], ['keith smith', '23', '22', '2', '5', '0', '0'], ['gary mullen', '21', '20', '2', '0', '23', '0'], ['steve trimble', '19', '17', '4', '0', '0', '0'], ['richard prather', '16', '15', '2', '0', '0', '0'], ['rob devita', '12.5', '9', '7', '6', '0', '0'], ['clyde skipper', '12', '12', '0', '0', '10', '0'], ['chris brewer', '12', '9', '6', '0', '0', '0'], ['kelly kirchbaum', '11', '9', '4', '0', '0', '0'], ['chuck harris', '9', '6', '6', '3', '0', '0'], ['patrick cain', '7', '7', '0', '3', '0', '0'], ['richard rodgers', '6.5', '5', '3', '0', '23', '0'], ['larry friday', '6.5', '6', '1', '0', '0', '0'], ['jon norris', '5', '4', '2', '2', '0', '0'], ['phil forte', '3', '3', '0', '1', '0', '0'], ['durell taylor', '2', '2', '0', '0', '0', '0'], ['lazlo mike - mayer', '2', '2', '0', '0', '0', '0'], ['marco morales', '1', '1', '0', '0', '0', '0']]

2007 - 08 washington capitals season

https://en.wikipedia.org/wiki/2007%E2%80%9308_Washington_Capitals_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11772462-6.html.csv

comparative

the washington capitals had a game against the colorado visitor earlier than florida .

{'row_1': '4', 'row_2': '8', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visitor', 'colorado'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose visitor record fuzzily matches to colorado .', 'tostr': 'filter_eq { all_rows ; visitor ; colorado }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; visitor ; colorado } ; date }', 'tointer': 'select the rows whose visitor record fuzzily matches to colorado . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visitor', 'florida'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose visitor record fuzzily matches to florida .', 'tostr': 'filter_eq { all_rows ; visitor ; florida }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; visitor ; florida } ; date }', 'tointer': 'select the rows whose visitor record fuzzily matches to florida . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; visitor ; colorado } ; date } ; hop { filter_eq { all_rows ; visitor ; florida } ; date } } = true', 'tointer': 'select the rows whose visitor record fuzzily matches to colorado . take the date record of this row . select the rows whose visitor record fuzzily matches to florida . take the date record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; visitor ; colorado } ; date } ; hop { filter_eq { all_rows ; visitor ; florida } ; date } } = true

select the rows whose visitor record fuzzily matches to colorado . take the date record of this row . select the rows whose visitor record fuzzily matches to florida . take the date record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'visitor_7': 7, 'colorado_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'visitor_11': 11, 'florida_12': 12, 'date_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'visitor_7': 'visitor', 'colorado_8': 'colorado', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'visitor_11': 'visitor', 'florida_12': 'florida', 'date_13': 'date'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'visitor_7': [0], 'colorado_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'visitor_11': [1], 'florida_12': [1], 'date_13': [3]}

['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record']

[['january 1', 'ottawa', '3 - 6', 'washington', 'kolzig', '14547', '16 - 19 - 5'], ['january 3', 'washington', '0 - 2', 'boston', 'kolzig', '12240', '16 - 20 - 5'], ['january 5', 'washington', '5 - 4', 'montreal', 'kolzig', '21273', '17 - 20 - 5'], ['january 9', 'colorado', '1 - 2', 'washington', 'kolzig', '16168', '18 - 20 - 5'], ['january 13', 'philadelphia', '6 - 4', 'washington', 'kolzig', '17713', '18 - 21 - 5'], ['january 15', 'ottawa', '2 - 4', 'washington', 'johnson', '15261', '19 - 21 - 5'], ['january 17', 'edmonton', '4 - 5', 'washington', 'kolzig', '13399', '20 - 21 - 5'], ['january 19', 'florida', '3 - 5', 'washington', 'johnson', '16973', '21 - 21 - 5'], ['january 21', 'washington', '6 - 5', 'pittsburgh', 'kolzig', '17050', '22 - 21 - 5'], ['january 23', 'washington', '2 - 3', 'toronto', 'kolzig', '19479', '22 - 22 - 5'], ['january 24', 'toronto', '1 - 2', 'washington', 'johnson', '14094', '23 - 22 - 5'], ['january 29', 'washington', '0 - 4', 'montreal', 'johnson', '21273', '23 - 23 - 5'], ['january 31', 'montreal', '4 - 5', 'washington', 'kolzig', '14930', '24 - 23 - 5']]

new democratic party candidates , 2008 canadian federal election

https://en.wikipedia.org/wiki/New_Democratic_Party_candidates%2C_2008_Canadian_federal_election

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10953705-9.html.csv

comparative

ron strynadka received less votes than patricia cordner in the 2008 canadian federal election .

{'row_1': '4', 'row_2': '10', 'col': '6', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'candidate', 'ron strynadka'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose candidate record fuzzily matches to ron strynadka .', 'tostr': 'filter_eq { all_rows ; candidate ; ron strynadka }'}, 'votes'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; candidate ; ron strynadka } ; votes }', 'tointer': 'select the rows whose candidate record fuzzily matches to ron strynadka . take the votes record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'candidate', 'patricia cordner'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose candidate record fuzzily matches to patricia cordner .', 'tostr': 'filter_eq { all_rows ; candidate ; patricia cordner }'}, 'votes'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; candidate ; patricia cordner } ; votes }', 'tointer': 'select the rows whose candidate record fuzzily matches to patricia cordner . take the votes record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; candidate ; ron strynadka } ; votes } ; hop { filter_eq { all_rows ; candidate ; patricia cordner } ; votes } } = true', 'tointer': 'select the rows whose candidate record fuzzily matches to ron strynadka . take the votes record of this row . select the rows whose candidate record fuzzily matches to patricia cordner . take the votes record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; candidate ; ron strynadka } ; votes } ; hop { filter_eq { all_rows ; candidate ; patricia cordner } ; votes } } = true

select the rows whose candidate record fuzzily matches to ron strynadka . take the votes record of this row . select the rows whose candidate record fuzzily matches to patricia cordner . take the votes record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'candidate_7': 7, 'ron strynadka_8': 8, 'votes_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'candidate_11': 11, 'patricia cordner_12': 12, 'votes_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'candidate_7': 'candidate', 'ron strynadka_8': 'ron strynadka', 'votes_9': 'votes', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'candidate_11': 'candidate', 'patricia cordner_12': 'patricia cordner', 'votes_13': 'votes'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'candidate_7': [0], 'ron strynadka_8': [0], 'votes_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'candidate_11': [1], 'patricia cordner_12': [1], 'votes_13': [3]}

['riding', 'candidate', 'gender', 'residence', 'occupation', 'votes', 'rank']

[['brandon-souris', 'jean luc bouché', 'm', 'brandon', 'locomotive engineer', '6055', '2nd'], ['charleswood-st james-assiniboia', 'fiona shiells', 'f', 'winnipeg', 'ministerial assistant', '7190', '3rd'], ['churchill', 'niki ashton', 'f', 'thompson', 'researcher', '8734', '1st'], ['dauphin-swan river-marquette', 'ron strynadka', 'm', 'birtle', 'retired', '4914', '2nd'], ['elmwood-transcona', 'jim maloway', 'm', 'winnipeg', 'small businessman', '14355', '1st'], ['kildonan-st paul', 'ross eadie', 'm', 'winnipeg', 'self employed / consultant', '12093', '2nd'], ['portage-lisgar', 'mohamed alli', 'm', 'winnipeg', 'distribution centre associate', '2353', '4th'], ['provencher', 'ross c martin', 'm', 'oakbank', 'design coordinator', '4947', '2nd'], ['saint boniface', 'matt schaubroeck', 'm', 'winnipeg', 'student', '5502', '3rd'], ['selkirk-interlake', 'patricia cordner', 'f', 'selkirk', 'retired', '9506', '2nd'], ['winnipeg centre', 'pat martin', 'm', 'winnipeg', 'parliamentarian', '12285', '1st'], ['winnipeg north', 'judy wasylycia - leis', 'f', 'winnipeg', 'parliamentarian', '14097', '1st'], ['winnipeg south', 'sean robert', 'm', 'winnipeg', 'product consultant - mlcc', '4673', '3rd'], ['winnipeg south centre', 'rachel heinrichs', 'f', 'winnipeg', 'student', '5490', '3rd']]

east coast conference

https://en.wikipedia.org/wiki/East_Coast_Conference

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1969577-3.html.csv

aggregation

for schools in the east coast conference the total combined number of students enrolled is 44185 .

{'scope': 'all', 'col': '6', 'type': 'sum', 'result': '44185', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'enrollment'], 'result': '44185', 'ind': 0, 'tostr': 'sum { all_rows ; enrollment }'}, '44185'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; enrollment } ; 44185 } = true', 'tointer': 'the sum of the enrollment record of all rows is 44185 .'}

round_eq { sum { all_rows ; enrollment } ; 44185 } = true

the sum of the enrollment record of all rows is 44185 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'enrollment_4': 4, '44185_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'enrollment_4': 'enrollment', '44185_5': '44185'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'enrollment_4': [0], '44185_5': [1]}

['institution', 'nickname', 'location', 'founded', 'type', 'enrollment', 'joined', 'left', 'current conference']

[['adelphi university', 'panthers', 'garden city , new york', '1896', 'private', '7859', '1989', '2009', 'northeast - 10 ( ne - 10 )'], ['concordia college', 'clippers', 'bronxville , new york', '1881', 'private', '2431', '1989', '2009', 'cacc'], ['university of new haven', 'chargers', 'west haven , connecticut', '1920', 'private', '6400', '2002', '2008', 'northeast - 10 ( ne - 10 )'], ['new jersey institute of technology ( njit )', 'highlanders', 'newark , new jersey', '1881', 'public', '9944', '1997', '2000', 'ncaa d - i independent'], ['pace university', 'setters', 'new york city , new york', '1906', 'private', '14177', '1989', '1997', 'northeast - 10 ( ne - 10 )'], ['philadelphia university', 'rams', 'philadelphia , pennsylvania', '1884', 'private', '3374', '1991', '2005', 'cacc']]

1986 - 87 segunda división

https://en.wikipedia.org/wiki/1986%E2%80%9387_Segunda_Divisi%C3%B3n

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12109851-6.html.csv

majority

most of the teams scored at least 50 goals for in the 1986-87 segunda division .

{'scope': 'all', 'col': '8', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '50', 'subset': None}

{'func': 'most_greater', 'args': ['all_rows', 'goals for', '50'], 'result': True, 'ind': 0, 'tointer': 'for the goals for records of all rows , most of them are greater than 50 .', 'tostr': 'most_greater { all_rows ; goals for ; 50 } = true'}

most_greater { all_rows ; goals for ; 50 } = true

for the goals for records of all rows , most of them are greater than 50 .

1

1

{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'goals for_3': 3, '50_4': 4}

{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'goals for_3': 'goals for', '50_4': '50'}

{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'goals for_3': [0], '50_4': [0]}

['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference']

[['1', 'barcelona atlã ¨ tic', '44', '42 - 2', '16', '10', '18', '56', '58', '- 2'], ['2', 'ue figueres', '44', '42 - 2', '15', '12', '17', '59', '53', '+ 6'], ['3', 'cartagena fc', '44', '42 - 2', '14', '14', '16', '52', '67', '- 15'], ['4', 'real oviedo', '44', '40 - 4', '13', '14', '17', '50', '64', '- 14'], ['5', 'castilla cf', '44', '33 - 11', '11', '11', '22', '49', '71', '- 22'], ['6', 'jerez deportivo', '44', '22 - 22', '5', '12', '27', '32', '78', '- 46']]

2008 pga championship

https://en.wikipedia.org/wiki/2008_PGA_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17807292-6.html.csv

aggregation

the total prize money at the 2008 pga championships top 10 was 4769400 .

{'scope': 'all', 'col': '6', 'type': 'sum', 'result': '4769400', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'money'], 'result': '4769400', 'ind': 0, 'tostr': 'sum { all_rows ; money }'}, '4769400'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; money } ; 4769400 } = true', 'tointer': 'the sum of the money record of all rows is 4769400 .'}

round_eq { sum { all_rows ; money } ; 4769400 } = true

the sum of the money record of all rows is 4769400 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'money_4': 4, '4769400_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'money_4': 'money', '4769400_5': '4769400'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'money_4': [0], '4769400_5': [1]}

['place', 'player', 'country', 'score', 'to par', 'money']

[['1', 'pádraig harrington', 'ireland', '71 + 74 + 66 + 66 = 277', '- 3', '1350000'], ['t2', 'ben curtis', 'united states', '73 + 67 + 68 + 71 = 279', '- 1', '660000'], ['t2', 'sergio garcía', 'spain', '69 + 73 + 69 + 68 = 279', '- 1', '660000'], ['t4', 'henrik stenson', 'sweden', '71 + 70 + 68 + 72 = 281', '+ 1', '330000'], ['t4', 'camilo villegas', 'colombia', '74 + 72 + 67 + 68 = 281', '+ 1', '330000'], ['6', 'steve flesch', 'united states', '73 + 70 + 70 + 69 = 282', '+ 2', '270000'], ['t7', 'phil mickelson', 'united states', '70 + 73 + 71 + 70 = 284', '+ 4', '231250'], ['t7', 'andrés romero', 'argentina', '69 + 78 + 65 + 72 = 284', '+ 4', '231250'], ['t9', 'alastair forsyth', 'scotland', '73 + 72 + 70 + 70 = 285', '+ 5', '176725'], ['t9', 'justin rose', 'england', '73 + 67 + 74 + 71 = 285', '+ 5', '176725'], ['t9', 'jeev milkha singh', 'india', '68 + 74 + 70 + 73 = 285', '+ 5', '176725'], ['t9', 'charlie wi', 'south korea', '70 + 70 + 71 + 74 = 285', '+ 5', '176725']]

2005 - 06 philadelphia flyers season

https://en.wikipedia.org/wiki/2005%E2%80%9306_Philadelphia_Flyers_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14159731-4.html.csv

majority

the majority of these game decisions were made by esche .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'esche', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'decision', 'esche'], 'result': True, 'ind': 0, 'tointer': 'for the decision records of all rows , most of them fuzzily match to esche .', 'tostr': 'most_eq { all_rows ; decision ; esche } = true'}

most_eq { all_rows ; decision ; esche } = true

for the decision records of all rows , most of them fuzzily match to esche .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'decision_3': 3, 'esche_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'decision_3': 'decision', 'esche_4': 'esche'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'decision_3': [0], 'esche_4': [0]}

['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record']

[['november 3', 'washington', '1 - 8', 'philadelphia', 'esche', '19253', '7 - 3 - 1'], ['november 5', 'atlanta', '3 - 4', 'philadelphia', 'esche', '19587', '8 - 3 - 1'], ['november 8', 'boston', '3 - 4', 'philadelphia', 'esche', '19587', '9 - 3 - 1'], ['november 10', 'ny islanders', '2 - 3', 'philadelphia', 'niittymaki', '19601', '10 - 3 - 1'], ['november 12', 'florida', '4 - 5', 'philadelphia', 'esche', '19654', '11 - 3 - 1'], ['november 14', 'philadelphia', '2 - 5', 'tampa bay', 'esche', '20020', '11 - 4 - 1'], ['november 16', 'pittsburgh', '3 - 2', 'philadelphia', 'niittymaki', '19687', '11 - 4 - 2'], ['november 18', 'atlanta', '6 - 5', 'philadelphia', 'esche', '19533', '11 - 4 - 3'], ['november 19', 'philadelphia', '6 - 3', 'pittsburgh', 'niittymaki', '17132', '12 - 4 - 3'], ['november 22', 'tampa bay', '4 - 2', 'philadelphia', 'esche', '19567', '12 - 5 - 3'], ['november 25', 'philadelphia', '5 - 3', 'boston', 'niittymaki', '17565', '13 - 5 - 3'], ['november 26', 'ny islanders', '4 - 2', 'philadelphia', 'niittymaki', '19780', '13 - 6 - 3'], ['november 29', 'philadelphia', '4 - 2', 'ny islanders', 'esche', '12354', '14 - 6 - 3'], ['november 30', 'new jersey', '1 - 2', 'philadelphia', 'esche', '19573', '15 - 6 - 3']]

1999 new orleans saints season

https://en.wikipedia.org/wiki/1999_New_Orleans_Saints_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16713182-1.html.csv

count

of the games with a crowd over 50000 , that the new orleans saints played in the 1999 season , they only won two of them .

{'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'w', 'result': '2', 'col': '4', 'subset': {'col': '5', 'criterion': 'greater_than', 'value': '50000'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'attendance', '50000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; attendance ; 50000 }', 'tointer': 'select the rows whose attendance record is greater than 50000 .'}, 'result', 'w'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose result record fuzzily matches to w .', 'tostr': 'filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; result ; w }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; result ; w } }', 'tointer': 'select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; result ; w } } ; 2 } = true', 'tointer': 'select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 2 .'}

eq { count { filter_eq { filter_greater { all_rows ; attendance ; 50000 } ; result ; w } } ; 2 } = true

select the rows whose attendance record is greater than 50000 . among these rows , select the rows whose result record fuzzily matches to w . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'attendance_6': 6, '50000_7': 7, 'result_8': 8, 'w_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'attendance_6': 'attendance', '50000_7': '50000', 'result_8': 'result', 'w_9': 'w', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'attendance_6': [0], '50000_7': [0], 'result_8': [1], 'w_9': [1], '2_10': [3]}

['week', 'date', 'opponent', 'result', 'attendance']

[['1', 'september 12 , 1999', 'carolina panthers', 'w 19 - 10', '58166'], ['2', 'september 19 , 1999', 'san francisco 49ers', 'l 21 - 28', '67685'], ['4', 'october 3 , 1999', 'chicago bears', 'l 10 - 14', '66944'], ['5', 'october 10 , 1999', 'atlanta falcons', 'l 17 - 20', '57289'], ['6', 'october 17 , 1999', 'tennessee titans', 'l 21 - 24', '51875'], ['7', 'october 24 , 1999', 'new york giants', 'l 3 - 31', '77982'], ['8', 'october 31 , 1999', 'cleveland browns', 'l 16 - 21', '48817'], ['9', 'november 7 , 1999', 'tampa bay buccaneers', 'l 16 - 31', '47129'], ['10', 'november 14 , 1999', 'san francisco 49ers', 'w 24 - 6', '52198'], ['11', 'november 21 , 1999', 'jacksonville jaguars', 'l 23 - 41', '69772'], ['12', 'november 28 , 1999', 'st louis rams', 'l 12 - 43', '65864'], ['13', 'december 5 , 1999', 'atlanta falcons', 'l 12 - 35', '62568'], ['14', 'december 12 , 1999', 'st louis rams', 'l 14 - 30', '46838'], ['15', 'december 19 , 1999', 'baltimore ravens', 'l 8 - 31', '67597'], ['16', 'december 24 , 1999', 'dallas cowboys', 'w 31 - 24', '47835'], ['17', 'january 2 , 2000', 'carolina panthers', 'l 13 - 45', '56929']]

1971 nhl amateur draft

https://en.wikipedia.org/wiki/1971_NHL_Amateur_Draft

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1213511-4.html.csv

majority

in the 1971 nhl amateur draft , most of the left wings were from canada .

{'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'canada', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'left wing'}}

{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'left wing'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; position ; left wing }', 'tointer': 'select the rows whose position record fuzzily matches to left wing .'}, 'nationality', 'canada'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose position record fuzzily matches to left wing . for the nationality records of these rows , most of them fuzzily match to canada .', 'tostr': 'most_eq { filter_eq { all_rows ; position ; left wing } ; nationality ; canada } = true'}

most_eq { filter_eq { all_rows ; position ; left wing } ; nationality ; canada } = true

select the rows whose position record fuzzily matches to left wing . for the nationality records of these rows , most of them fuzzily match to canada .

2

2

{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'position_4': 4, 'left wing_5': 5, 'nationality_6': 6, 'canada_7': 7}

{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'position_4': 'position', 'left wing_5': 'left wing', 'nationality_6': 'nationality', 'canada_7': 'canada'}

{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'position_4': [0], 'left wing_5': [0], 'nationality_6': [1], 'canada_7': [1]}

['pick', 'player', 'position', 'nationality', 'nhl team', 'college / junior / club team']

[['43', 'hartland monahan', 'right wing', 'canada', 'california golden seals', 'montreal junior canadiens ( oha )'], ['44', 'george hulme', 'goaltender', 'canada', 'detroit red wings', 'st catharines black hawks ( oha )'], ['45', 'ed sidebottom', 'defence', 'canada', 'montreal canadiens', 'estevan bruins ( wchl )'], ['46', 'gerry methe', 'left wing', 'canada', 'pittsburgh penguins', 'oshawa generals ( oha )'], ['47', 'bob richer', 'centre', 'canada', 'buffalo sabres', 'trois - riviã ¨ res draveurs ( qmjhl )'], ['48', 'neil komadoski', 'defence', 'canada', 'los angeles kings', 'winnipeg jets ( wchl )'], ['49', 'mike legge', 'left wing', 'canada', 'minnesota north stars', 'winipeg jets ( wchl )'], ['50', 'ted scharf', 'defence', 'canada', 'philadelphia flyers', 'kitchener rangers ( oha )'], ['51', 'rick cunningham', 'defence', 'canada', 'toronto maple leafs', 'peterborough petes ( oha )'], ['52', 'derek harker', 'defence', 'canada', 'st louis blues', 'edmonton oil kings ( wchl )'], ['53', 'greg hubick', 'defence', 'canada', 'montreal canadiens', 'university of minnesota duluth ( wcha )'], ['54', 'clyde simon', 'right wing', 'canada', 'chicago black hawks', 'st catharines black hawks ( oha )'], ['55', 'jerry butler', 'right wing', 'canada', 'new york rangers', 'hamilton red wings ( oha )'], ['56', 'dave hynes', 'left wing', 'united states', 'boston bruins', 'harvard university ( ecac )']]

2007 detroit indy grand prix

https://en.wikipedia.org/wiki/2007_Detroit_Indy_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17242268-2.html.csv

aggregation

all of the drivers in the 2007 detroit indy grand prix scored a combined total of 434 points .

{'scope': 'all', 'col': '7', 'type': 'sum', 'result': '434', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'points'], 'result': '434', 'ind': 0, 'tostr': 'sum { all_rows ; points }'}, '434'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; points } ; 434 } = true', 'tointer': 'the sum of the points record of all rows is 434 .'}

round_eq { sum { all_rows ; points } ; 434 } = true

the sum of the points record of all rows is 434 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'points_4': 4, '434_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'points_4': 'points', '434_5': '434'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'points_4': [0], '434_5': [1]}

['driver', 'team', 'laps', 'time / retired', 'grid', 'laps led', 'points']

[['tony kanaan', 'andretti green', '89', '2:11:50.5097', '4', '20', '50'], ['danica patrick', 'andretti green', '89', '+ 0.4865', '11', '9', '40'], ['dan wheldon', 'target chip ganassi', '89', '+ 1.2207', '16', '0', '35'], ['darren manning', 'aj foyt racing', '89', '+ 1.9217', '8', '0', '32'], ['kosuke matsuura', 'panther racing', '88', '+ 1 lap', '14', '0', '30'], ['dario franchitti', 'andretti green', '88', '+ 1 lap', '2', '27', '31'], ['buddy rice', 'dreyer & reinbold racing', '87', 'contact', '15', '7', '26'], ['scott dixon', 'target chip ganassi', '87', 'contact', '3', '0', '24'], ['a j foyt iv', 'vision racing', '87', 'mechanical', '13', '0', '22'], ['ed carpenter', 'vision racing', '86', '+ 3 laps', '12', '0', '20'], ['scott sharp', 'rahal letterman', '82', '+ 7 laps', '17', '0', '19'], ['sam hornish , jr', 'team penske', '75', '+ 14 laps', '7', '0', '18'], ['tomas scheckter', 'vision racing', '67', 'contact', '9', '0', '17'], ['hãlio castroneves', 'team penske', '67', 'contact', '1', '26', '16'], ['vitor meira', 'panther racing', '31', 'contact', '10', '0', '15'], ['sarah fisher', 'dreyer & reinbold racing', '29', 'contact', '18', '0', '14'], ['marco andretti', 'andretti green', '27', 'mechanical', '6', '0', '13'], ['ryan hunter - reay', 'rahal letterman', '24', 'mechanical', '5', '0', '12']]

united states house of representatives elections , 1946

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1946

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342233-3.html.csv

ordinal

in the election of 1946 for united states house of representatives , the incumbent who was first elected the second earliest was frank w boykin .

{'row': '1', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'first elected', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first elected ; 2 }'}, 'incumbent'], 'result': 'frank w boykin', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent }'}, 'frank w boykin'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; frank w boykin } = true', 'tointer': 'select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is frank w boykin .'}

eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; frank w boykin } = true

select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is frank w boykin .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '2_6': 6, 'incumbent_7': 7, 'frank w boykin_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '2_6': '2', 'incumbent_7': 'incumbent', 'frank w boykin_8': 'frank w boykin'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '2_6': [0], 'incumbent_7': [1], 'frank w boykin_8': [2]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['alabama 1', 'frank w boykin', 'democratic', '1935', 're - elected', 'frank w boykin ( d ) unopposed'], ['alabama 2', 'george m grant', 'democratic', '1938', 're - elected', 'george m grant ( d ) unopposed'], ['alabama 3', 'george w andrews', 'democratic', '1944', 're - elected', 'george w andrews ( d ) unopposed'], ['alabama 4', 'sam hobbs', 'democratic', '1934', 're - elected', 'sam hobbs ( d ) 88.1 % roger s bingham ( r ) 11.9 %'], ['alabama 5', 'albert rains', 'democratic', '1944', 're - elected', 'albert rains ( d ) unopposed'], ['alabama 6', 'pete jarman', 'democratic', '1936', 're - elected', 'pete jarman ( d ) unopposed'], ['alabama 7', 'carter manasco', 'democratic', '1941', 're - elected', 'carter manasco ( d ) 72.7 % m h woodward ( r ) 27.3 %'], ['alabama 8', 'john sparkman', 'democratic', '1936', 're - elected elected simultaneously to u s senate', 'john sparkman ( d ) 92.4 % arthur south ( r ) 7.6 %']]

tatjana maria

https://en.wikipedia.org/wiki/Tatjana_Maria

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11737744-5.html.csv

unique

the us open is the only grand slam that tatjana maria has two career wins in .

{'scope': 'all', 'row': '4', 'col': '8', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': '2 -', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'career win - loss', '2 -'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose career win - loss record fuzzily matches to 2 - .', 'tostr': 'filter_eq { all_rows ; career win - loss ; 2 - }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; career win - loss ; 2 - } }', 'tointer': 'select the rows whose career win - loss record fuzzily matches to 2 - . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'career win - loss', '2 -'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose career win - loss record fuzzily matches to 2 - .', 'tostr': 'filter_eq { all_rows ; career win - loss ; 2 - }'}, 'tournament'], 'result': 'us open', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; career win - loss ; 2 - } ; tournament }'}, 'us open'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; career win - loss ; 2 - } ; tournament } ; us open }', 'tointer': 'the tournament record of this unqiue row is us open .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; career win - loss ; 2 - } } ; eq { hop { filter_eq { all_rows ; career win - loss ; 2 - } ; tournament } ; us open } } = true', 'tointer': 'select the rows whose career win - loss record fuzzily matches to 2 - . there is only one such row in the table . the tournament record of this unqiue row is us open .'}

and { only { filter_eq { all_rows ; career win - loss ; 2 - } } ; eq { hop { filter_eq { all_rows ; career win - loss ; 2 - } ; tournament } ; us open } } = true

select the rows whose career win - loss record fuzzily matches to 2 - . there is only one such row in the table . the tournament record of this unqiue row is us open .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'career win - loss_7': 7, '2 -_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'us open_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'career win - loss_7': 'career win - loss', '2 -_8': '2 -', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'us open_10': 'us open'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'career win - loss_7': [0], '2 -_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'us open_10': [3]}

['tournament', '2007', '2008', '2009', '2010', '2011', '2012', 'career win - loss']

[['australian open', 'a', 'a', 'a', '1r', '1r', 'a', '0 - 3'], ['french open', 'a', 'a', 'a', '2r', 'a', 'a', '1 - 2'], ['wimbledon', '2r', '1r', '1r', 'a', 'a', 'a', '1 - 4'], ['us open', 'a', 'a', '2r', '1r', 'a', '2r', '2 - 3'], ['win - loss', '1 - 1', '0 - 1', '1 - 2', '1 - 3', '0 - 1', '1 - 1', '4 - 12']]

miss andretti

https://en.wikipedia.org/wiki/Miss_Andretti

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14981555-1.html.csv

aggregation

miss andretti 's average wight was 54.29 kilograms for all 7 races .

{'scope': 'all', 'col': '7', 'type': 'average', 'result': '54.29', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'weight ( kg )'], 'result': '54.29', 'ind': 0, 'tostr': 'avg { all_rows ; weight ( kg ) }'}, '54.29'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; weight ( kg ) } ; 54.29 } = true', 'tointer': 'the average of the weight ( kg ) record of all rows is 54.29 .'}

round_eq { avg { all_rows ; weight ( kg ) } ; 54.29 } = true

the average of the weight ( kg ) record of all rows is 54.29 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'weight (kg)_4': 4, '54.29_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'weight (kg)_4': 'weight ( kg )', '54.29_5': '54.29'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'weight (kg)_4': [0], '54.29_5': [1]}

['result', 'date', 'race', 'venue', 'group', 'distance', 'weight ( kg )', 'jockey', 'winner / 2nd']

[['won', '29 dec 2004', '3yo hcp restricted maiden', 'pinjarra', 'na', '1200 m', '55', 'k forrester', '2nd - hello doctor'], ['2nd', '26 jan 2005', '3yo hcp restricted fillies', 'belmont', 'na', '1200 m', '52.5', 'k forrester', '1st - lust for dust'], ['won', '12 feb 2005', '3yo hcp restricted', 'belmont', 'na', '1200 m', '52', 'k forrester', "2nd - key 's ace"], ['won', '27 feb 2005', '3yo hcp restricted fillies & mares', 'pinjarra', 'na', '1400 m', '53.5', 'k forrester', '2nd - blondelle'], ['won', '25 apr 2005', '3yo hcp restricted fillies', 'belmont', 'na', '1000 m', '59', 'k forrester', '2nd - final effect'], ['won', '14 may 2005', '3yo hcp restricted', 'belmont', 'na', '1200 m', '52.5', 'k forrester', '2nd - zed power'], ['won', '28 may 2005', '3yo hcp restricted fillies & mares', 'belmont', 'na', '1200 m', '55.5', 'k forrester', '2nd - eroded']]

tiny lund

https://en.wikipedia.org/wiki/Tiny_Lund

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1777959-1.html.csv

comparative

tiny lund finished in a higher position in his race in 1963 than he did in his race in 1971 .

{'row_1': '3', 'row_2': '9', 'col': '4', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1963'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1963 .', 'tostr': 'filter_eq { all_rows ; year ; 1963 }'}, 'finish'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1963 } ; finish }', 'tointer': 'select the rows whose year record fuzzily matches to 1963 . take the finish record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1971'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1971 .', 'tostr': 'filter_eq { all_rows ; year ; 1971 }'}, 'finish'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1971 } ; finish }', 'tointer': 'select the rows whose year record fuzzily matches to 1971 . take the finish record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; year ; 1963 } ; finish } ; hop { filter_eq { all_rows ; year ; 1971 } ; finish } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1963 . take the finish record of this row . select the rows whose year record fuzzily matches to 1971 . take the finish record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; year ; 1963 } ; finish } ; hop { filter_eq { all_rows ; year ; 1971 } ; finish } } = true

select the rows whose year record fuzzily matches to 1963 . take the finish record of this row . select the rows whose year record fuzzily matches to 1971 . take the finish record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1963_8': 8, 'finish_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1971_12': 12, 'finish_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1963_8': '1963', 'finish_9': 'finish', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1971_12': '1971', 'finish_13': 'finish'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1963_8': [0], 'finish_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1971_12': [1], 'finish_13': [3]}

['year', 'manufacturer', 'start', 'finish', 'team']

[['1959', 'chevrolet', '13', '40', 'buck baker'], ['1960', 'oldsmobile', '64', '51', 'gazaway'], ['1963', 'ford', '12', '1', 'wood'], ['1964', 'ford', '13', '11', 'graham shaw'], ['1965', 'ford', '24', '29', 'lyle stelter'], ['1967', 'plymouth', '11', '4', 'petty'], ['1968', 'mercury', '5', '9', 'moore'], ['1970', 'dodge', '8', '13', 'john mcconnell'], ['1971', 'dodge', '23', '39', 'john mcconnell'], ['1973', 'chevrolet', '19', '36', 'carl price']]

list of republic of doyle episodes

https://en.wikipedia.org/wiki/List_of_Republic_of_Doyle_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27547668-3.html.csv

superlative

episode 13 of republic of doyle had the most viewers .

{'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'viewers'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; viewers }'}, 'no'], 'result': '1', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; viewers } ; no }'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; viewers } ; no } ; 1 } = true', 'tointer': 'select the row whose viewers record of all rows is maximum . the no record of this row is 1 .'}

eq { hop { argmax { all_rows ; viewers } ; no } ; 1 } = true

select the row whose viewers record of all rows is maximum . the no record of this row is 1 .

3

3

{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'viewers_5': 5, 'no_6': 6, '1_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'viewers_5': 'viewers', 'no_6': 'no', '1_7': '1'}

{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'viewers_5': [0], 'no_6': [1], '1_7': [2]}

['', 'no', 'title', 'directed by', 'written by', 'viewers', 'original airdate', 'prod code']

[['13', '1', 'live and let doyle', 'james allodi', 'allan hawco', '1038000', 'january 12 , 2011', '201'], ['14', '2', 'popeye doyle', 'steve scaini', 'allan hawco', '944000', 'january 19 , 2011', '202'], ['15', '3', 'a stand up guy', 'steve scaini', 'perry chafe', '776000', 'january 26 , 2011', '203'], ['16', '4', 'the son also rises', 'steve dimarco', 'jesse mckeown', '899000', 'february 2 , 2011', '204'], ['17', '5', 'something old , someone blue', 'james allodi', 'adam higgs & jackie may', '854000', 'february 9 , 2011', '205'], ['18', '6', 'the ryans and the pittmans', 'steve dimarco', 'greg nelson', '843000', 'february 16 , 2011', '206'], ['19', '7', 'crashing on the couch', 'keith samples', 'jackie may', '760000', 'february 23 , 2011', '207'], ['20', '8', 'sympathy for the devil', 'stacey curtis', 'john callaghan', '834400', 'march 2 , 2011', '208'], ['21', '9', 'will the real des courtney please stand up', 'keith samples', 'greg nelson', '1026000', 'march 9 , 2011', '209'], ['22', '10', 'the special detective', 'steve scaini', 'adam higgs', '836000', 'march 16 , 2011', '210'], ['23', '11', "do n't gamble with city hall", 'john vatcher', 'jackie may', '1021000', 'march 23 , 2011', '211'], ['24', '12', "st john 's town", 'keith samples', 'perry chafe', '730000', 'march 30 , 2011', '212']]

jack mcgrath

https://en.wikipedia.org/wiki/Jack_McGrath

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1236208-1.html.csv

count

jack mcgrath ranked first two times in 1954 and again in 1955 .

{'scope': 'all', 'criterion': 'equal', 'value': '1', 'result': '2', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'rank', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rank record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; rank ; 1 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; rank ; 1 } }', 'tointer': 'select the rows whose rank record is equal to 1 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; rank ; 1 } } ; 2 } = true', 'tointer': 'select the rows whose rank record is equal to 1 . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; rank ; 1 } } ; 2 } = true

select the rows whose rank record is equal to 1 . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'rank_5': 5, '1_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'rank_5': 'rank', '1_6': '1', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'rank_5': [0], '1_6': [0], '2_7': [2]}

['year', 'start', 'qual', 'rank', 'finish', 'laps']

[['1948', '13', '124.580', '16', '21', '70'], ['1949', '3', '128.884', '8', '26', '39'], ['1950', '6', '131.868', '10', '14', '131'], ['1951', '3', '134.303', '8', '3', '200'], ['1952', '3', '136.664', '5', '11', '200'], ['1953', '3', '136.602', '13', '5', '200'], ['1954', '1', '141.033', '1', '3', '200'], ['1955', '3', '142.580', '1', '26', '54']]

federal league ( ohsaa )

https://en.wikipedia.org/wiki/Federal_League_%28OHSAA%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26466528-1.html.csv

unique

in the federal league , the only school that has polar bears as a mascot is jackson .

{'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'polar bears', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nickname', 'polar bears'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nickname record fuzzily matches to polar bears .', 'tostr': 'filter_eq { all_rows ; nickname ; polar bears }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nickname ; polar bears } }', 'tointer': 'select the rows whose nickname record fuzzily matches to polar bears . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nickname', 'polar bears'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nickname record fuzzily matches to polar bears .', 'tostr': 'filter_eq { all_rows ; nickname ; polar bears }'}, 'school'], 'result': 'jackson', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nickname ; polar bears } ; school }'}, 'jackson'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; nickname ; polar bears } ; school } ; jackson }', 'tointer': 'the school record of this unqiue row is jackson .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; nickname ; polar bears } } ; eq { hop { filter_eq { all_rows ; nickname ; polar bears } ; school } ; jackson } } = true', 'tointer': 'select the rows whose nickname record fuzzily matches to polar bears . there is only one such row in the table . the school record of this unqiue row is jackson .'}

and { only { filter_eq { all_rows ; nickname ; polar bears } } ; eq { hop { filter_eq { all_rows ; nickname ; polar bears } ; school } ; jackson } } = true

select the rows whose nickname record fuzzily matches to polar bears . there is only one such row in the table . the school record of this unqiue row is jackson .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nickname_7': 7, 'polar bears_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'school_9': 9, 'jackson_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nickname_7': 'nickname', 'polar bears_8': 'polar bears', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'school_9': 'school', 'jackson_10': 'jackson'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'nickname_7': [0], 'polar bears_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'school_9': [2], 'jackson_10': [3]}

['school', 'nickname', 'location', 'colors', 'join date']

[['canton mckinley', 'bulldogs', 'canton', 'red , black', '2003'], ['glenoak', 'golden eagles', 'canton', 'forest green , vegas gold', '1975'], ['hoover', 'vikings', 'north canton', 'black , orange', '1968'], ['jackson', 'polar bears', 'jackson township', 'purple , gold', '1964'], ['lake', 'blue streaks', 'uniontown', 'blue , red , white', '1987']]

chris wood ( golfer )

https://en.wikipedia.org/wiki/Chris_Wood_%28golfer%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18512893-3.html.csv

comparative

chris wood made more cuts at the open championship than he did at the pga championship .

{'row_1': '3', 'row_2': '4', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'the open championship'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to the open championship .', 'tostr': 'filter_eq { all_rows ; tournament ; the open championship }'}, 'cuts made'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; the open championship } ; cuts made }', 'tointer': 'select the rows whose tournament record fuzzily matches to the open championship . take the cuts made record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'pga championship'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to pga championship .', 'tostr': 'filter_eq { all_rows ; tournament ; pga championship }'}, 'cuts made'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tournament ; pga championship } ; cuts made }', 'tointer': 'select the rows whose tournament record fuzzily matches to pga championship . take the cuts made record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; tournament ; the open championship } ; cuts made } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; cuts made } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to the open championship . take the cuts made record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the cuts made record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; tournament ; the open championship } ; cuts made } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; cuts made } } = true

select the rows whose tournament record fuzzily matches to the open championship . take the cuts made record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the cuts made record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'tournament_7': 7, 'the open championship_8': 8, 'cuts made_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tournament_11': 11, 'pga championship_12': 12, 'cuts made_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'tournament_7': 'tournament', 'the open championship_8': 'the open championship', 'cuts made_9': 'cuts made', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tournament_11': 'tournament', 'pga championship_12': 'pga championship', 'cuts made_13': 'cuts made'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tournament_7': [0], 'the open championship_8': [0], 'cuts made_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tournament_11': [1], 'pga championship_12': [1], 'cuts made_13': [3]}

['tournament', 'wins', 'top - 5', 'events', 'cuts made']

[['masters tournament', '0', '0', '1', '0'], ['us open', '0', '0', '0', '0'], ['the open championship', '0', '2', '4', '3'], ['pga championship', '0', '0', '3', '1'], ['totals', '0', '2', '8', '4']]

2008 skycity triple crown

https://en.wikipedia.org/wiki/2008_Skycity_Triple_Crown

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18282916-2.html.csv

unique

in the 2008 skycity triple crown , the only ford performance racing team that scored more than 1400 points was won with mark winterbottom driving .

{'scope': 'subset', 'row': '1', 'col': '5', 'col_other': '3', 'criterion': 'greater_than', 'value': '1400', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'ford performance racing'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'ford performance racing'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; team ; ford performance racing }', 'tointer': 'select the rows whose team record fuzzily matches to ford performance racing .'}, 'points', '1400'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to ford performance racing . among these rows , select the rows whose points record is greater than 1400 .', 'tostr': 'filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } }', 'tointer': 'select the rows whose team record fuzzily matches to ford performance racing . among these rows , select the rows whose points record is greater than 1400 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'ford performance racing'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; team ; ford performance racing }', 'tointer': 'select the rows whose team record fuzzily matches to ford performance racing .'}, 'points', '1400'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to ford performance racing . among these rows , select the rows whose points record is greater than 1400 .', 'tostr': 'filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 }'}, 'name'], 'result': 'mark winterbottom', 'ind': 3, 'tostr': 'hop { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } ; name }'}, 'mark winterbottom'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } ; name } ; mark winterbottom }', 'tointer': 'the name record of this unqiue row is mark winterbottom .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } } ; eq { hop { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } ; name } ; mark winterbottom } } = true', 'tointer': 'select the rows whose team record fuzzily matches to ford performance racing . among these rows , select the rows whose points record is greater than 1400 . there is only one such row in the table . the name record of this unqiue row is mark winterbottom .'}

and { only { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } } ; eq { hop { filter_greater { filter_eq { all_rows ; team ; ford performance racing } ; points ; 1400 } ; name } ; mark winterbottom } } = true

select the rows whose team record fuzzily matches to ford performance racing . among these rows , select the rows whose points record is greater than 1400 . there is only one such row in the table . the name record of this unqiue row is mark winterbottom .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'team_8': 8, 'ford performance racing_9': 9, 'points_10': 10, '1400_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'name_12': 12, 'mark winterbottom_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'team_8': 'team', 'ford performance racing_9': 'ford performance racing', 'points_10': 'points', '1400_11': '1400', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'name_12': 'name', 'mark winterbottom_13': 'mark winterbottom'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_greater_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'team_8': [0], 'ford performance racing_9': [0], 'points_10': [1], '1400_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'name_12': [3], 'mark winterbottom_13': [4]}

['position', 'number', 'name', 'team', 'points']

[['1', '5', 'mark winterbottom', 'ford performance racing', '1402'], ['2', '1', 'garth tander', 'holden racing team', '1344'], ['3', '88', 'jamie whincup', 'team vodafone', '1276'], ['4', '15', 'rick kelly', 'hsv dealer team', '1208'], ['5', '6', 'steven richards', 'ford performance racing', '1123']]

1955 vfl season

https://en.wikipedia.org/wiki/1955_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10773753-12.html.csv

count

in the 1955 vfl season , among the games where away team scored below 10.00 , 3 of them drew less than 40,000 people .

{'scope': 'subset', 'criterion': 'less_than', 'value': '40000', 'result': '3', 'col': '6', 'subset': {'col': '4', 'criterion': 'less_than', 'value': '10'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'away team score', '10'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; away team score ; 10 }', 'tointer': 'select the rows whose away team score record is less than 10 .'}, 'crowd', '40000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is less than 40000 .', 'tostr': 'filter_less { filter_less { all_rows ; away team score ; 10 } ; crowd ; 40000 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_less { filter_less { all_rows ; away team score ; 10 } ; crowd ; 40000 } }', 'tointer': 'select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is less than 40000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_less { all_rows ; away team score ; 10 } ; crowd ; 40000 } } ; 3 } = true', 'tointer': 'select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is less than 40000 . the number of such rows is 3 .'}

eq { count { filter_less { filter_less { all_rows ; away team score ; 10 } ; crowd ; 40000 } } ; 3 } = true

select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is less than 40000 . the number of such rows is 3 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'away team score_6': 6, '10_7': 7, 'crowd_8': 8, '40000_9': 9, '3_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'away team score_6': 'away team score', '10_7': '10', 'crowd_8': 'crowd', '40000_9': '40000', '3_10': '3'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'away team score_6': [0], '10_7': [0], 'crowd_8': [1], '40000_9': [1], '3_10': [3]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['melbourne', '17.16 ( 118 )', 'st kilda', '3.3 ( 21 )', 'mcg', '15624', '9 july 1955'], ['essendon', '14.7 ( 91 )', 'richmond', '5.20 ( 50 )', 'windy hill', '19500', '9 july 1955'], ['south melbourne', '13.10 ( 88 )', 'geelong', '15.9 ( 99 )', 'lake oval', '17000', '9 july 1955'], ['hawthorn', '18.12 ( 120 )', 'fitzroy', '9.5 ( 59 )', 'glenferrie oval', '10500', '9 july 1955'], ['footscray', '5.13 ( 43 )', 'collingwood', '5.7 ( 37 )', 'western oval', '42354', '9 july 1955'], ['north melbourne', '13.10 ( 88 )', 'carlton', '16.9 ( 105 )', 'arden street oval', '12000', '9 july 1955']]

2007 icc world twenty20 statistics

https://en.wikipedia.org/wiki/2007_ICC_World_Twenty20_statistics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13219504-10.html.csv

ordinal

herschelle gibbs / justin kempwas the icc wolrld twenty20 parternship that scored the second highest runs .

{'row': '3', 'col': '2', 'order': '2', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'runs', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; runs ; 2 }'}, 'partnerships'], 'result': 'herschelle gibbs / justin kemp', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; runs ; 2 } ; partnerships }'}, 'herschelle gibbs / justin kemp'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; runs ; 2 } ; partnerships } ; herschelle gibbs / justin kemp } = true', 'tointer': 'select the row whose runs record of all rows is 2nd maximum . the partnerships record of this row is herschelle gibbs / justin kemp .'}

eq { hop { nth_argmax { all_rows ; runs ; 2 } ; partnerships } ; herschelle gibbs / justin kemp } = true

select the row whose runs record of all rows is 2nd maximum . the partnerships record of this row is herschelle gibbs / justin kemp .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'runs_5': 5, '2_6': 6, 'partnerships_7': 7, 'herschelle gibbs / justin kemp_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'runs_5': 'runs', '2_6': '2', 'partnerships_7': 'partnerships', 'herschelle gibbs / justin kemp_8': 'herschelle gibbs / justin kemp'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'runs_5': [0], '2_6': [0], 'partnerships_7': [1], 'herschelle gibbs / justin kemp_8': [2]}

['wicket', 'runs', 'partnerships', 'venue', 'date']

[['1st', '145', 'chris gayle / devon smith', 'johannesburg', '2007 - 09 - 11'], ['2nd', '95', 'devon smith / shivnarine chanderpaul', 'johannesburg', '2007 - 09 - 13'], ['3rd', '120', 'herschelle gibbs / justin kemp', 'johannesburg', '2007 - 09 - 11'], ['4th', '101', 'younis khan / shoaib malik', 'johannesburg', '2007 - 09 - 17'], ['5th', '119', 'shoaib malik / misbah - ul - haq', 'johannesburg', '2007 - 09 - 18'], ['6th', '73', 'craig mcmillan / jacob oram', 'johannesburg', '2007 - 09 - 16'], ['7th', '45', 'jehan mubarak / gayan wijekoon', 'johannesburg', '2007 - 09 - 14'], ['8th', '40', 'jehan mubarak / chaminda vaas', 'newlands , cape town', '2007 - 09 - 17'], ['9th', '27', 'jimmy kamande / rajesh bhudia', 'durban', '2007 - 09 - 12'], ['10th', '18', 'majid haq / dewald nel', 'durban', '2007 - 09 - 12']]

kansas state wildcats men 's basketball

https://en.wikipedia.org/wiki/Kansas_State_Wildcats_men%27s_basketball

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15740666-4.html.csv

count

kansas state wildcats played two teams with oklahoma in their name .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'oklahoma', 'result': '2', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'kansas state vs', 'oklahoma'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose kansas state vs record fuzzily matches to oklahoma .', 'tostr': 'filter_eq { all_rows ; kansas state vs ; oklahoma }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; kansas state vs ; oklahoma } }', 'tointer': 'select the rows whose kansas state vs record fuzzily matches to oklahoma . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; kansas state vs ; oklahoma } } ; 2 } = true', 'tointer': 'select the rows whose kansas state vs record fuzzily matches to oklahoma . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; kansas state vs ; oklahoma } } ; 2 } = true

select the rows whose kansas state vs record fuzzily matches to oklahoma . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'kansas state vs_5': 5, 'oklahoma_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'kansas state vs_5': 'kansas state vs', 'oklahoma_6': 'oklahoma', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'kansas state vs_5': [0], 'oklahoma_6': [0], '2_7': [2]}

['kansas state vs', 'overall record', 'manhattan', "opponent 's venue", 'neutral site', 'last 5 meetings', 'last 10 meetings', 'current streak', 'big 12 games']

[['baylor', 'ksu , 16 - 12', 'ksu , 10 - 5', 'bu , 5 - 4', 'tied , 2 - 2', 'ksu , 3 - 2', 'ksu , 6 - 4', 'w 2', 'tied , 11 - 11'], ['iowa state', 'ksu , 136 - 80', 'ksu , 79 - 25', 'ksu , 49 - 47', 'isu , 9 - 7', 'isu , 3 - 2', 'ksu , 6 - 4', 'w 1', 'ksu , 18 - 17'], ['kansas', 'ku , 186 - 91', 'ku , 75 - 45', 'ku , 84 - 35', 'ku , 27 - 11', 'ku , 5 - 0', 'ku , 9 - 1', 'l 5', 'ku , 39 - 3'], ['oklahoma', 'ou , 104 - 93', 'ksu , 56 - 35', 'ou , 60 - 26', 'ksu , 11 - 9', 'ksu , 3 - 2', 'ksu , 6 - 4', 'w 2', 'ou , 12 - 8'], ['oklahoma state', 'ksu , 74 - 48', 'ksu , 36 - 15', 'osu , 29 - 25', 'ksu , 13 - 4', 'ksu , 4 - 1', 'ksu , 6 - 4', 'w 1', 'osu , 14 - 7'], ['texas', 'ksu , 16 - 10', 'ksu , 8 - 3', 'tied , 6 - 6', 'ksu , 2 - 1', 'ksu , 4 - 1', 'ksu , 7 - 3', 'w 3', 'ksu , 11 - 10'], ['tcu', 'ksu , 5 - 2', 'ksu , 3 - 1', 'ksu , 2 - 0', 'tcu , 1 - 0', 'ksu , 4 - 1', 'ksu , 5 - 2', 'w 2', 'ksu , 2 - 0'], ['texas tech', 'ksu , 18 - 12', 'ksu , 11 - 3', 'ttu , 7 - 6', 'ttu , 2 - 1', 'ksu , 5 - 0', 'ksu , 8 - 2', 'w 7', 'tied , 11 - 11'], ['west virginia', 'ksu , 3 - 1', 'ksu , 1 - 0', 'ksu , 2 - 0', 'wvu 1 - 0', 'ksu , 3 - 1', 'ksu , 3 - 1', 'w 2', 'ksu , 2 - 0']]

hawthorne ( season 1 )

https://en.wikipedia.org/wiki/Hawthorne_%28season_1%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-30030227-1.html.csv

count

all of the air dates of hawthorne , season 1 were in the summer of 2009 .

{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '9', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'original air date'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose original air date record is arbitrary .', 'tostr': 'filter_all { all_rows ; original air date }'}], 'result': '9', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; original air date } }', 'tointer': 'select the rows whose original air date record is arbitrary . the number of such rows is 9 .'}, '9'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; original air date } } ; 9 } = true', 'tointer': 'select the rows whose original air date record is arbitrary . the number of such rows is 9 .'}

eq { count { filter_all { all_rows ; original air date } } ; 9 } = true

select the rows whose original air date record is arbitrary . the number of such rows is 9 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'original air date_5': 5, '9_6': 6}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'original air date_5': 'original air date', '9_6': '9'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'original air date_5': [0], '9_6': [2]}

['series', 'title', 'directed by', 'written by', 'original air date', 'viewers ( million )']

[['1', 'pilot', 'mikael salomon', 'john masius', 'june 16 , 2009', '3.82'], ['2', 'healing time', 'arvin brown', 'john masius', 'june 23 , 2009', '3.80'], ['3', 'yielding', 'jeff bleckner', 'sarah thorp', 'june 30 , 2009', 'n / a'], ['4', 'all the wrong places', 'andy wolk', 'glen mazzara', 'july 7 , 2009', 'n / a'], ['5', 'the sense of belonging', 'mike robe', 'anna c miller', 'july 14 , 2009', '3.21'], ['6', 'trust me', 'ed bianchi', 'jeff rake', 'july 21 , 2009', 'n / a'], ['7', 'night moves', 'roxann dawson', 'bill chais', 'july 28 , 2009', '3.61'], ['8', 'no guts , no glory', 'andy wolk', 'laurie arent', 'august 4 , 2009', '3.58'], ['9', "mother 's day", 'jeff bleckner', 'glen mazzara', 'august 11 , 2009', '3.35']]

pete sampras career statistics

https://en.wikipedia.org/wiki/Pete_Sampras_career_statistics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22834834-2.html.csv

majority

pete samparas played most of his matches on carpet surface from 1991-1997 .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'carpet ( i )', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'carpet ( i )'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to carpet ( i ) .', 'tostr': 'most_eq { all_rows ; surface ; carpet ( i ) } = true'}

most_eq { all_rows ; surface ; carpet ( i ) } = true

for the surface records of all rows , most of them fuzzily match to carpet ( i ) .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'carpet (i)_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'carpet (i)_4': 'carpet ( i )'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'carpet (i)_4': [0]}

['outcome', 'year', 'championship', 'surface', 'opponent in the final', 'score in the final']

[['winner', '1991', 'frankfurt', 'carpet ( i )', 'jim courier', '3 - 6 , 7 - 6 ( 7 - 5 ) , 6 - 3 , 6 - 4'], ['runner - up', '1993', 'frankfurt', 'carpet ( i )', 'michael stich', '6 - 7 ( 3 - 7 ) , 6 - 2 , 6 - 7 ( 7 - 9 ) , 2 - 6'], ['winner', '1994', 'frankfurt', 'carpet ( i )', 'boris becker', '4 - 6 , 6 - 3 , 7 - 5 , 6 - 4'], ['winner', '1996', 'hannover', 'carpet ( i )', 'boris becker', '3 - 6 , 7 - 6 ( 7 - 5 ) , 7 - 6 ( 7 - 4 ) , 6 - 7 ( 11 - 13 ) , 6 - 4'], ['winner', '1997', 'hannover', 'hard ( i )', 'yevgeny kafelnikov', '6 - 3 , 6 - 2 , 6 - 2']]

axis & allies

https://en.wikipedia.org/wiki/Axis_%26_Allies

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-173475-1.html.csv

ordinal

in axis & allies , the 2nd highest number of pieces was in the year 1999 .

{'row': '2', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'pieces', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; pieces ; 2 }'}, 'release'], 'result': '1999', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; pieces ; 2 } ; release }'}, '1999'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; pieces ; 2 } ; release } ; 1999 } = true', 'tointer': 'select the row whose pieces record of all rows is 2nd maximum . the release record of this row is 1999 .'}

eq { hop { nth_argmax { all_rows ; pieces ; 2 } ; release } ; 1999 } = true

select the row whose pieces record of all rows is 2nd maximum . the release record of this row is 1999 .

3

3

{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'pieces_5': 5, '2_6': 6, 'release_7': 7, '1999_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'pieces_5': 'pieces', '2_6': '2', 'release_7': 'release', '1999_8': '1999'}

{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'pieces_5': [0], '2_6': [0], 'release_7': [1], '1999_8': [2]}

['release', 'title', 'start', 'pieces', 'board ( inches )', 'board ( cm )', 'type', 'new units new units when compared to the original a & a : classic version of the game', 'playable powers']

[['1981', 'axis & allies ( nova games edition )', '1942', '415', '37 19 ½', '93 50', 'global', 'same as classic plus nuke pieces were cardboard', '5 : germany , japan , ussr , uk , usa'], ['1999', 'axis & allies : europe', '1941', '373', '30 20', '75 50', 'theater', 'destroyer , artillery', '4 : germany , ussr , uk , usa'], ['2004', 'axis & allies : d - day', '1944', '241', '30 20', '75 50', 'local', 'artillery , blockhouse', '3 : germany , uk , usa'], ['2006', 'axis & allies : battle of the bulge', '1944', '157', '30 20', '75 50', 'local', 'artillery , truck', '3 : germany , uk , usa'], ['2007', 'axis & allies : guadalcanal', '1942', '172', '30 20', '75 50', 'local', 'destroyer , cruiser , artillery', '2 : japan , usa']]

éric bernard

https://en.wikipedia.org/wiki/%C3%89ric_Bernard

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1219780-1.html.csv

aggregation

eric bernard had a total of 10 points with the lamborghini v12 chassis .

{'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '10', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'lamborghini v12'}}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'engine', 'lamborghini v12'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; engine ; lamborghini v12 }', 'tointer': 'select the rows whose engine record fuzzily matches to lamborghini v12 .'}, 'points'], 'result': '10', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; engine ; lamborghini v12 } ; points }'}, '10'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; engine ; lamborghini v12 } ; points } ; 10 } = true', 'tointer': 'select the rows whose engine record fuzzily matches to lamborghini v12 . the sum of the points record of these rows is 10 .'}

round_eq { sum { filter_eq { all_rows ; engine ; lamborghini v12 } ; points } ; 10 } = true

select the rows whose engine record fuzzily matches to lamborghini v12 . the sum of the points record of these rows is 10 .

3

3

{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'engine_5': 5, 'lamborghini v12_6': 6, 'points_7': 7, '10_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'engine_5': 'engine', 'lamborghini v12_6': 'lamborghini v12', 'points_7': 'points', '10_8': '10'}

{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'engine_5': [0], 'lamborghini v12_6': [0], 'points_7': [1], '10_8': [2]}

['year', 'entrant', 'chassis', 'engine', 'points']

[['1989', 'equipe larrousse', 'lola lc89', 'lamborghini v12', '0'], ['1990', 'espo larrousse f1', 'lola lc89b', 'lamborghini v12', '5'], ['1990', 'espo larrousse f1', 'lola 90', 'lamborghini v12', '5'], ['1991', 'larrousse f1', 'larrousse lola 91', 'cosworth v8', '1'], ['1994', 'ligier gitanes blondes', 'ligier js39b', 'renault v10', '4'], ['1994', 'team lotus', 'lotus 109', 'mugen honda v10', '4']]

1958 formula one season

https://en.wikipedia.org/wiki/1958_Formula_One_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140110-6.html.csv

majority

the most constructor used during the 1958 formula one season was ferrari .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'ferrari', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'constructor', 'ferrari'], 'result': True, 'ind': 0, 'tointer': 'for the constructor records of all rows , most of them fuzzily match to ferrari .', 'tostr': 'most_eq { all_rows ; constructor ; ferrari } = true'}

most_eq { all_rows ; constructor ; ferrari } = true

for the constructor records of all rows , most of them fuzzily match to ferrari .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'constructor_3': 3, 'ferrari_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'constructor_3': 'constructor', 'ferrari_4': 'ferrari'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'constructor_3': [0], 'ferrari_4': [0]}

['race name', 'circuit', 'date', 'winning driver', 'constructor', 'report']

[['vi glover trophy', 'goodwood', '7 april', 'mike hawthorn', 'ferrari', 'report'], ['viii gran premio di siracusa', 'syracuse', '13 april', 'luigi musso', 'ferrari', 'report'], ['xiii barc aintree 200', 'aintree', '19 april', 'stirling moss', 'cooper - climax', 'report'], ['x brdc international trophy', 'silverstone', '3 may', 'peter collins', 'ferrari', 'report'], ['vi grand prix de caen', 'caen', '20 july', 'stirling moss', 'cooper - climax', 'report']]

wru division five south west

https://en.wikipedia.org/wiki/WRU_Division_Five_South_West

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17675675-1.html.csv

ordinal

tycroes rfc had the second most losses in the wru division five south west .

{'row': '10', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'lost', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; lost ; 2 }'}, 'club'], 'result': 'tycroes rfc', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; lost ; 2 } ; club }'}, 'tycroes rfc'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; lost ; 2 } ; club } ; tycroes rfc } = true', 'tointer': 'select the row whose lost record of all rows is 2nd maximum . the club record of this row is tycroes rfc .'}

eq { hop { nth_argmax { all_rows ; lost ; 2 } ; club } ; tycroes rfc } = true

select the row whose lost record of all rows is 2nd maximum . the club record of this row is tycroes rfc .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'lost_5': 5, '2_6': 6, 'club_7': 7, 'tycroes rfc_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'lost_5': 'lost', '2_6': '2', 'club_7': 'club', 'tycroes rfc_8': 'tycroes rfc'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'lost_5': [0], '2_6': [0], 'club_7': [1], 'tycroes rfc_8': [2]}

['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points']

[['club', 'played', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['birchgrove rfc', '20', '0', '3', '538', '257', '82', '29', '13', '2', '83'], ['neath athletic rfc', '20', '0', '3', '616', '194', '89', '24', '12', '2', '82'], ['trebanos rfc', '20', '0', '3', '701', '223', '99', '27', '13', '0', '81'], ['gowerton rfc', '20', '0', '9', '439', '389', '55', '52', '5', '5', '54'], ['llandybie rfc', '20', '0', '9', '338', '374', '38', '55', '4', '3', '51'], ['alltwen rfc', '20', '1', '10', '445', '382', '50', '42', '5', '4', '47'], ['crynant rfc', '20', '0', '12', '315', '454', '43', '66', '4', '3', '39'], ['glais rfc', '20', '1', '13', '233', '444', '33', '64', '0', '1', '27'], ['tycroes rfc', '20', '0', '15', '250', '617', '32', '88', '3', '3', '26'], ['cwmtwrch rfc', '20', '2', '14', '179', '466', '25', '66', '1', '1', '22'], ['cwmgors rfc', '20', '0', '17', '206', '460', '31', '64', '3', '6', '21'], ['penlan rfc', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0']]

slovak national badminton championships

https://en.wikipedia.org/wiki/Slovak_National_Badminton_Championships

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15740721-1.html.csv

count

gabriela zabavníková won the women 's singles in the slovak national badminton championships a total of three times .

{'scope': 'all', 'criterion': 'equal', 'value': 'gabriela zabavníková', 'result': '3', 'col': '3', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', "women 's singles", 'gabriela zabavníková'], 'result': None, 'ind': 0, 'tointer': "select the rows whose women 's singles record fuzzily matches to gabriela zabavníková .", 'tostr': "filter_eq { all_rows ; women 's singles ; gabriela zabavníková }"}], 'result': '3', 'ind': 1, 'tostr': "count { filter_eq { all_rows ; women 's singles ; gabriela zabavníková } }", 'tointer': "select the rows whose women 's singles record fuzzily matches to gabriela zabavníková . the number of such rows is 3 ."}, '3'], 'result': True, 'ind': 2, 'tostr': "eq { count { filter_eq { all_rows ; women 's singles ; gabriela zabavníková } } ; 3 } = true", 'tointer': "select the rows whose women 's singles record fuzzily matches to gabriela zabavníková . the number of such rows is 3 ."}

eq { count { filter_eq { all_rows ; women 's singles ; gabriela zabavníková } } ; 3 } = true

select the rows whose women 's singles record fuzzily matches to gabriela zabavníková . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, "women 's singles_5": 5, 'gabriela zabavníková_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', "women 's singles_5": "women 's singles", 'gabriela zabavníková_6': 'gabriela zabavníková', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], "women 's singles_5": [0], 'gabriela zabavníková_6': [0], '3_7': [2]}

['year', "men 's singles", "women 's singles", "men 's doubles", "women 's doubles", 'mixed doubles']

[['1993', 'juraj brestovský', 'ľuba hanzušová', 'juraj brestovský igor novák', 'ľuba hanzušová aurita hirková', 'peter púdela martina švecová'], ['1994', 'juraj brestovský', 'ľuba hanzušová', 'róbert cyprian peter púdela', 'ľuba hanzušová daniela tomášová', 'peter púdela martina švecová'], ['1995', 'igor novák', 'ľuba hanzušová', 'róbert cyprian peter púdela', 'katarína pokorná alexandra felgrová', 'peter púdela alexandra felgrová'], ['1996', 'marián šulko', 'alexandra felgrová', 'juraj brestovský igor novák', 'radka majorská barbora bobrovská', 'jaroslav heleš katarína pokorná'], ['1997', 'pavel mečár', 'kvetoslava orlovská', 'marián šulko marek navrátil', 'barbora bobrovská radka majorská', 'juraj brestovský zuzana kenížová'], ['1998', 'pavel mečár', 'kvetoslava orlovská', 'pavel mečár jaroslav marek', 'kvetoslava orlovská gabriela zabavníková', 'pavel mečár barbora bobrovská'], ['1999', 'marián šulko', 'kvetoslava orlovská', 'pavel mečár marián šulko', 'barbora bobrovská alexandra felgrová', 'pavel mečár barbora bobrovská'], ['2000', 'marián šulko', 'gabriela zabavníková', 'marián šulko pavel mečár', 'kvetoslava orlovská gabriela zabavníková', 'pavel mečár barbora bobrovská'], ['2001', 'marián šulko', 'gabriela zabavníková', 'marián šulko pavel mečár', 'barbora bobrovská eva sládeková', 'pavel mečár barbora bobrovská'], ['2002', 'lukáš klačanský', 'kvetoslava orlovská', 'marián šulko pavel mečár', 'kvetoslava orlovská gabriela zabavníková', 'pavel mečár barbora bobrovská'], ['2003', 'marián šulko', 'gabriela zabavníková', 'marián šulko pavel mečár', 'zuzana orlovská gabriela zabavníková', 'pavel mečár barbora bobrovská'], ['2004', 'marián šulko', 'kvetoslava orlovská', 'marián šulko pavel mečár', 'barbora bobrovská eva sládeková', 'pavel mečár barbora bobrovská'], ['2005', 'michal matejka', 'eva sládeková', 'marián šulko pavel mečár', 'alexandra felgrová kristína ludíková', 'pavel mečár barbora bobrovská'], ['2006', 'marián šulko', 'kristína ludíková', 'michal matejka marián šulko', 'alexandra felgrová kristína ludíková', 'ladislav tomčko kvetoslava orlovská'], ['2007', 'marián šulko', 'eva sládeková', 'vladimír závada marián smrek', 'kvetoslava orlovská zuzana orlovská', 'vladimír turlík gabriela zabavníková'], ['2008', 'marián šulko', 'kvetoslava orlovská', 'vladimír závada marián smrek', 'júlia turzáková gabriela zabavníková', 'marián smrek kvetoslava orlovská'], ['2009', 'michal matejka', 'monika fašungová', 'vladimír závada marián smrek', 'barbora bobrovská zuzana orlovská', 'vladimír závada zuzana orlovská'], ['2010', 'michal matejka', 'ivana kubíková', 'marián šulko pavel mečár', 'barbora bobrovská zuzana orlovská', 'vladimír závada zuzana orlovská']]

los angeles lakers all - time roster

https://en.wikipedia.org/wiki/Los_Angeles_Lakers_all-time_roster

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10560886-12.html.csv

unique

jim krebs is the only player on the los angeles lakers all - time roster from southern methodist college .

{'scope': 'all', 'row': '12', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'southern methodist', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school / country', 'southern methodist'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school / country record fuzzily matches to southern methodist .', 'tostr': 'filter_eq { all_rows ; school / country ; southern methodist }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; school / country ; southern methodist } }', 'tointer': 'select the rows whose school / country record fuzzily matches to southern methodist . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school / country', 'southern methodist'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school / country record fuzzily matches to southern methodist .', 'tostr': 'filter_eq { all_rows ; school / country ; southern methodist }'}, 'player'], 'result': 'jim krebs', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school / country ; southern methodist } ; player }'}, 'jim krebs'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; school / country ; southern methodist } ; player } ; jim krebs }', 'tointer': 'the player record of this unqiue row is jim krebs .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; school / country ; southern methodist } } ; eq { hop { filter_eq { all_rows ; school / country ; southern methodist } ; player } ; jim krebs } } = true', 'tointer': 'select the rows whose school / country record fuzzily matches to southern methodist . there is only one such row in the table . the player record of this unqiue row is jim krebs .'}

and { only { filter_eq { all_rows ; school / country ; southern methodist } } ; eq { hop { filter_eq { all_rows ; school / country ; southern methodist } ; player } ; jim krebs } } = true

select the rows whose school / country record fuzzily matches to southern methodist . there is only one such row in the table . the player record of this unqiue row is jim krebs .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'school / country_7': 7, 'southern methodist_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'jim krebs_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'school / country_7': 'school / country', 'southern methodist_8': 'southern methodist', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'jim krebs_10': 'jim krebs'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'school / country_7': [0], 'southern methodist_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'jim krebs_10': [3]}

['player', 'nationality', 'position', 'from', 'school / country']

[['edwin kachan', 'united states', 'guard', '1948', 'depaul'], ['ed kalafat', 'united states', 'forward / center', '1954', 'minnesota'], ['jason kapono', 'united states', 'forward', '2011', 'ucla'], ['coby karl', 'united states', 'guard', '2007', 'boise state'], ['jerome kersey', 'united states', 'forward', '1996', 'longwood'], ['randolph keys', 'united states', 'guard / forward', '1994', 'southern mississippi'], ['earnie killum', 'united states', 'guard', '1970', 'stetson'], ['frankie king', 'united states', 'guard', '1995', 'western carolina'], ['jim king', 'united states', 'guard', '1963', 'tulsa'], ['joe kleine', 'united states', 'center', '1996', 'arkansas'], ['travis knight', 'united states', 'forward / center', '1996 , 1999', 'connecticut'], ['jim krebs', 'united states', 'forward / center', '1957', 'southern methodist'], ['larry krystkowiak', 'united states', 'forward / center', '1996', 'montana'], ['mitch kupchak', 'united states', 'forward / center', '1981', 'north carolina'], ['cj kupec', 'united states', 'forward / center', '1975', 'michigan']]

chris haggard

https://en.wikipedia.org/wiki/Chris_Haggard

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14734893-1.html.csv

ordinal

of the tournaments that chris haggard participated in , the 2nd to last one was in san jose .

{'row': '17', 'col': '2', 'order': '2', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; date ; 2 }'}, 'tournament'], 'result': 'san jose , usa', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; date ; 2 } ; tournament }'}, 'san jose , usa'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; san jose , usa } = true', 'tointer': 'select the row whose date record of all rows is 2nd maximum . the tournament record of this row is san jose , usa .'}

eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; san jose , usa } = true

select the row whose date record of all rows is 2nd maximum . the tournament record of this row is san jose , usa .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'tournament_7': 7, 'san jose , usa_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'date_5': 'date', '2_6': '2', 'tournament_7': 'tournament', 'san jose , usa_8': 'san jose , usa'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'tournament_7': [1], 'san jose , usa_8': [2]}

['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score in the final']

[['runner - up', '30 august 1998', 'boston , usa', 'hard', 'jack waite', 'jacco eltingh paul haarhuis', '3 - 6 , 2 - 6'], ['runner - up', '15 november 1998', 'stockholm , sweden', 'hard', 'peter nyborg', 'nicklas kulti mikael tillström', '5 - 7 , 6 - 3 , 5 - 7'], ['winner', '1 august 1999', 'kitzbühel , austria', 'clay', 'peter nyborg', 'álex calatrava dušan vemić', '6 - 3 , 6 - 7 ( 4 - 7 ) , 7 - 6 ( 7 - 4 )'], ['winner', '21 july 2002', 'amersfoort , netherlands', 'clay', 'jeff coetzee', 'andré sá alexandre simoni', '7 - 6 ( 7 - 1 ) , 6 - 3'], ['winner', '6 october 2002', 'tokyo , japan', 'hard', 'jeff coetzee', 'jan - michael gambill graydon oliver', '7 - 6 ( 7 - 4 ) , 6 - 4'], ['winner', '5 january 2003', 'adelaide , australia', 'hard', 'jeff coetzee', 'max mirnyi jeff morrison', '2 - 6 , 6 - 4 , 7 - 6 ( 9 - 7 )'], ['runner - up', '27 april 2003', 'barcelona , spain', 'clay', 'robbie koenig', 'bob bryan mike bryan', '4 - 6 , 3 - 6'], ['runner - up', '20 july 2003', 'amersfoort , netherlands', 'clay', 'andré sá', 'devin bowen ashley fisher', '0 - 6 , 4 - 6'], ['runner - up', '3 august 2003', 'washington , usa', 'hard', 'paul hanley', 'yevgeny kafelnikov sargis sargsian', '5 - 7 , 6 - 4 , 2 - 6'], ['runner - up', '22 february 2004', 'memphis , usa', 'hard', 'jeff coetzee', 'bob bryan mike bryan', '3 - 6 , 4 - 6'], ['runner - up', '7 march 2004', 'scottsdale , usa', 'hard', 'jeff coetzee', 'rick leach brian macphie', '3 - 6 , 1 - 6'], ['winner', '22 august 2004', 'washington , usa', 'hard', 'robbie koenig', 'travis parrott dmitry tursunov', '7 - 6 ( 7 - 3 ) , 6 - 1'], ['runner - up', '5 february 2006', 'delray beach , usa', 'hard', 'wesley moodie', 'mark knowles daniel nestor', '2 - 6 , 3 - 6'], ['winner', '26 february 2006', 'memphis , usa', 'hard', 'ivo karlović', 'james blake mardy fish', '0 - 6 , 7 - 5 ,'], ['runner - up', '25 june 2006', "'s - hertogenbosch , netherlands", 'grass', 'arnaud clément', 'martin damm leander paes', '1 - 6 , 6 - 7 ( 3 - 7 )'], ['runner - up', '14 january 2007', 'auckland , new zealand', 'hard', 'simon aspelin', 'jeff coetzee rogier wassen', '7 - 6 ( 13 - 11 ) , 3 - 6 ,'], ['runner - up', '18 february 2007', 'san jose , usa', 'hard', 'rainer schüttler', 'eric butorac jamie murray', '5 - 7 , 6 - 7 ( 6 - 8 )'], ['runner - up', '16 september 2007', 'beijing , china', 'hard', 'lu yen - hsun', 'rik de voest ashley fisher', '7 - 6 ( 7 - 3 ) , 0 - 6 ,']]

nickelodeon movies

https://en.wikipedia.org/wiki/Nickelodeon_Movies

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1305286-6.html.csv

count

the actor dev patel was nominated for an award based on a role in a nickelodeon movie one time .

{'scope': 'all', 'criterion': 'equal', 'value': 'dev patel', 'result': '1', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner / nominee ( s )', 'dev patel'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner / nominee ( s ) record fuzzily matches to dev patel .', 'tostr': 'filter_eq { all_rows ; winner / nominee ( s ) ; dev patel }'}], 'result': '1', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; winner / nominee ( s ) ; dev patel } }', 'tointer': 'select the rows whose winner / nominee ( s ) record fuzzily matches to dev patel . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; winner / nominee ( s ) ; dev patel } } ; 1 } = true', 'tointer': 'select the rows whose winner / nominee ( s ) record fuzzily matches to dev patel . the number of such rows is 1 .'}

eq { count { filter_eq { all_rows ; winner / nominee ( s ) ; dev patel } } ; 1 } = true

select the rows whose winner / nominee ( s ) record fuzzily matches to dev patel . the number of such rows is 1 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'winner / nominee (s)_5': 5, 'dev patel_6': 6, '1_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'winner / nominee (s)_5': 'winner / nominee ( s )', 'dev patel_6': 'dev patel', '1_7': '1'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'winner / nominee (s)_5': [0], 'dev patel_6': [0], '1_7': [2]}

['year', 'category', 'film', 'winner / nominee ( s )', 'result']

[['2010', 'worst actor', 'imagine that', 'eddie murphy', 'nominated'], ['2010', 'worst actor of the decade', 'imagine that', 'eddie murphy', 'won'], ['2011', 'worst picture', 'the last airbender', 'n / a', 'won'], ['2011', 'worst supporting actor', 'the last airbender', 'jackson rathbone', 'won'], ['2011', 'worst supporting actor', 'the last airbender', 'dev patel', 'nominated'], ['2011', 'worst supporting actress', 'the last airbender', 'nicola peltz', 'nominated'], ['2011', 'worst screen ensemble', 'the last airbender', 'the entire cast', 'nominated'], ['2011', 'worst prequel , remake , rip - off or sequel', 'the last airbender', 'n / a', 'nominated'], ['2011', 'worst director', 'the last airbender', 'm night shyamalan', 'won'], ['2011', 'worst screenplay', 'the last airbender', 'm night shyamalan', 'won'], ['2011', 'worst eye - gouging misuse of 3 - d', 'the last airbender', 'n / a', 'won']]

media in fargo - moorhead

https://en.wikipedia.org/wiki/Media_in_Fargo%E2%80%93Moorhead

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11527967-4.html.csv

count

in fargo - moorhead , 2 stations are owned by radio fargo - moorhead .

{'scope': 'all', 'criterion': 'equal', 'value': 'radio fargo - moorhead', 'result': '2', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'owner', 'radio fargo - moorhead'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose owner record fuzzily matches to radio fargo - moorhead .', 'tostr': 'filter_eq { all_rows ; owner ; radio fargo - moorhead }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; owner ; radio fargo - moorhead } }', 'tointer': 'select the rows whose owner record fuzzily matches to radio fargo - moorhead . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; owner ; radio fargo - moorhead } } ; 2 } = true', 'tointer': 'select the rows whose owner record fuzzily matches to radio fargo - moorhead . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; owner ; radio fargo - moorhead } } ; 2 } = true

select the rows whose owner record fuzzily matches to radio fargo - moorhead . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'owner_5': 5, 'radio fargo - moorhead_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'owner_5': 'owner', 'radio fargo - moorhead_6': 'radio fargo - moorhead', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'owner_5': [0], 'radio fargo - moorhead_6': [0], '2_7': [2]}

['frequency', 'call sign', 'name', 'format', 'owner']

[['740 am', 'kvox', '740 the fan ( fox sports radio )', 'sports', 'radio fargo - moorhead'], ['790 am', 'kfgo', 'the mighty 790 kfgo', 'news / talk', 'radio fargo - moorhead'], ['890 am', 'kqlx', 'ag news 890', 'news / classic country', 'great plains integrated marketing'], ['970 am', 'wday', 'wday 970', 'news / talk', 'forum communications'], ['1100 am', 'wzfg', 'am 1100 the flag', 'talk', 'great plains integrated marketing'], ['1200 am', 'kfnw', 'praise 1200', 'christian', 'northwestern college'], ['1280 am', 'kvxr', 'real presence radio', 'catholic', 'real presence radio'], ['1660 am', 'kqwb', '1660 espn', 'sports', 'triad broadcasting']]

list of robin hood ( 2006 tv series ) episodes

https://en.wikipedia.org/wiki/List_of_Robin_Hood_%282006_TV_series%29_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14330096-4.html.csv

superlative

michael chaplin is the writer of the first aired episode of the robin hood 2006 tv series .

{'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '4', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'original air date'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; original air date }'}, 'writer'], 'result': 'michael chaplin', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; original air date } ; writer }'}, 'michael chaplin'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; original air date } ; writer } ; michael chaplin } = true', 'tointer': 'select the row whose original air date record of all rows is minimum . the writer record of this row is michael chaplin .'}

eq { hop { argmin { all_rows ; original air date } ; writer } ; michael chaplin } = true

select the row whose original air date record of all rows is minimum . the writer record of this row is michael chaplin .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'original air date_5': 5, 'writer_6': 6, 'michael chaplin_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'original air date_5': 'original air date', 'writer_6': 'writer', 'michael chaplin_7': 'michael chaplin'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'original air date_5': [0], 'writer_6': [1], 'michael chaplin_7': [2]}

['total', 'series', 'title', 'writer', 'director', 'original air date']

[['27', '1', 'total eclipse', 'michael chaplin', 'douglas mackinnon', '28 march 2009 , 6:50 pm - 7:35 pm'], ['28', '2', 'cause and effect', 'simon j ashford', 'douglas mackinnon', '4 april 2009 , 6:25 pm - 7:10 pm'], ['29', '3', 'lost in translation', 'ryan craig', 'alex pillai', '11 april 2009 , 7:45 pm - 8:30 pm'], ['30', '4', 'sins of the father', 'holly phillips', 'alex pillai', '18 april 2009 , 6:10 pm - 6:55 pm'], ['31', '5', 'let the games commence', 'lisa holdsworth', 'patrick lau', '25 april 2009 , 6:15 pm - 7:00 pm'], ['32', '6', 'do you love me', 'timothy prager', 'patrick lau', '2 may 2009 , 6:20 pm - 7:05 pm'], ['33', '7', 'too hot to handle', 'chris lang', 'john greening', '9 may 2009 , 6:15 pm - 7:00 pm'], ['34', '8', 'the king is dead , long live the king …', 'john jackson', 'john greening', '23 may 2009 , 6:35 pm - 7:20 pm'], ['35', '9', 'a dangerous deal', 'michael chaplin', 'graeme harper', '30 may 2009 , 7:25 pm - 8:10 pm'], ['36', '10', 'bad blood', 'lisa holdsworth', 'roger goldby', '6 june 2009 , 6:45 pm - 7:30 pm'], ['37', '11', 'the enemy of my enemy', 'timothy prager', 'graeme harper', '13 june 2009 , 6:45 pm - 7:30 pm'], ['38', '12', 'something worth fighting for , part 1', 'ryan craig', 'matthew evans', '20 june 2009 , 6:45 pm - 7:30 pm']]

wru division three south west

https://en.wikipedia.org/wiki/WRU_Division_Three_South_West

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13564702-3.html.csv

aggregation

the sum score of the teams who won 5 or less games in the wru division three south west was 75 points .

{'scope': 'subset', 'col': '12', 'type': 'sum', 'result': '75', 'subset': {'col': '3', 'criterion': 'less_than_eq', 'value': '5'}}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'won', '5'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; won ; 5 }', 'tointer': 'select the rows whose won record is less than or equal to 5 .'}, 'points'], 'result': '75', 'ind': 1, 'tostr': 'sum { filter_less_eq { all_rows ; won ; 5 } ; points }'}, '75'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_less_eq { all_rows ; won ; 5 } ; points } ; 75 } = true', 'tointer': 'select the rows whose won record is less than or equal to 5 . the sum of the points record of these rows is 75 .'}

round_eq { sum { filter_less_eq { all_rows ; won ; 5 } ; points } ; 75 } = true

select the rows whose won record is less than or equal to 5 . the sum of the points record of these rows is 75 .

3

3

{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_less_eq_0': 0, 'all_rows_4': 4, 'won_5': 5, '5_6': 6, 'points_7': 7, '75_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_4': 'all_rows', 'won_5': 'won', '5_6': '5', 'points_7': 'points', '75_8': '75'}

{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_less_eq_0': [1], 'all_rows_4': [0], 'won_5': [0], '5_6': [0], 'points_7': [1], '75_8': [2]}

['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points']

[['club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'tries for', 'tries against', 'try bonus', 'losing bonus', 'points'], ['skewen rfc', '22', '21', '0', '1', '680', '183', '96', '13', '12', '1', '97'], ['tondu rfc', '22', '19', '1', '2', '618', '243', '83', '24', '13', '1', '92'], ['kenfig hill rfc', '22', '16', '0', '6', '654', '321', '92', '33', '11', '2', '77'], ['glynneath rfc', '22', '15', '1', '6', '593', '229', '78', '23', '10', '2', '74'], ['seven sisters rfc', '22', '12', '0', '10', '444', '377', '54', '41', '4', '3', '55'], ['ystalyfera rfc', '22', '11', '0', '11', '401', '537', '42', '74', '4', '3', '51'], ['bryncoch rfc', '22', '9', '0', '13', '418', '582', '46', '80', '4', '1', '41'], ['nantyffyllon rfc', '22', '8', '0', '14', '254', '505', '31', '63', '1', '2', '35'], ['cwmavon rfc', '22', '6', '1', '15', '338', '483', '43', '61', '3', '5', '34'], ['brynamman rfc', '22', '5', '1', '16', '349', '642', '37', '93', '3', '6', '31'], ['briton ferry rfc', '22', '5', '0', '17', '289', '496', '34', '59', '3', '3', '26'], ['maesteg harlequins rfc', '22', '3', '0', '19', '264', '704', '30', '102', '3', '3', '18']]

i 'm a celebrity ... get me out of here ! ( uk tv series )

https://en.wikipedia.org/wiki/I%27m_a_Celebrity...Get_Me_Out_of_Here%21_%28UK_TV_series%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14345690-3.html.csv

unique

on i 'm a celebrity ... get me out of here ! , antony worrall thompson was the only celebrity famous for being a tv chef .

{'scope': 'all', 'row': '5', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'tv chef', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'famous for', 'tv chef'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose famous for record fuzzily matches to tv chef .', 'tostr': 'filter_eq { all_rows ; famous for ; tv chef }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; famous for ; tv chef } }', 'tointer': 'select the rows whose famous for record fuzzily matches to tv chef . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'famous for', 'tv chef'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose famous for record fuzzily matches to tv chef .', 'tostr': 'filter_eq { all_rows ; famous for ; tv chef }'}, 'celebrity'], 'result': 'antony worrall thompson', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; famous for ; tv chef } ; celebrity }'}, 'antony worrall thompson'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; famous for ; tv chef } ; celebrity } ; antony worrall thompson }', 'tointer': 'the celebrity record of this unqiue row is antony worrall thompson .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; famous for ; tv chef } } ; eq { hop { filter_eq { all_rows ; famous for ; tv chef } ; celebrity } ; antony worrall thompson } } = true', 'tointer': 'select the rows whose famous for record fuzzily matches to tv chef . there is only one such row in the table . the celebrity record of this unqiue row is antony worrall thompson .'}

and { only { filter_eq { all_rows ; famous for ; tv chef } } ; eq { hop { filter_eq { all_rows ; famous for ; tv chef } ; celebrity } ; antony worrall thompson } } = true

select the rows whose famous for record fuzzily matches to tv chef . there is only one such row in the table . the celebrity record of this unqiue row is antony worrall thompson .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'famous for_7': 7, 'tv chef_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'celebrity_9': 9, 'antony worrall thompson_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'famous for_7': 'famous for', 'tv chef_8': 'tv chef', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'celebrity_9': 'celebrity', 'antony worrall thompson_10': 'antony worrall thompson'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'famous for_7': [0], 'tv chef_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'celebrity_9': [2], 'antony worrall thompson_10': [3]}

['celebrity', 'famous for', 'entered', 'exited', 'finished']

[['phil tufnell', 'ex - er cricket', 'day 1', 'day 15', '1st'], ['john fashanu', 'ex - footballer', 'day 1', 'day 15', '2nd'], ['linda barker', 'changing rooms designer', 'day 1', 'day 15', '3rd'], ['wayne sleep', 'r dance', 'day 1', 'day14', '4th'], ['antony worrall thompson', 'tv chef', 'day 1', 'day 13', '5th'], ['toyah willcox', '1980s pop star', 'day 1', 'day 12', '6th'], ['catalina guirado', 'model', 'day 1', 'day 11', '7th'], ['chris bisson', 'actor', 'day 1', 'day 10', '8th'], ['danniella westbrook', 'actress ( played sam mitchell in eastenders )', 'day 1', 'day 9', '9th']]

2008 - 09 nbl season

https://en.wikipedia.org/wiki/2008%E2%80%9309_NBL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16653153-16.html.csv

aggregation

the average crowd on november 12 of the 08-09 nbl season was 2663 .

{'scope': 'subset', 'col': '6', 'type': 'average', 'result': '2663', 'subset': {'col': '1', 'criterion': 'equal', 'value': '12 november'}}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '12 november'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 12 november }', 'tointer': 'select the rows whose date record fuzzily matches to 12 november .'}, 'crowd'], 'result': '2663', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; date ; 12 november } ; crowd }'}, '2663'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; date ; 12 november } ; crowd } ; 2663 } = true', 'tointer': 'select the rows whose date record fuzzily matches to 12 november . the average of the crowd record of these rows is 2663 .'}

round_eq { avg { filter_eq { all_rows ; date ; 12 november } ; crowd } ; 2663 } = true

select the rows whose date record fuzzily matches to 12 november . the average of the crowd record of these rows is 2663 .

3

3

{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, '12 november_6': 6, 'crowd_7': 7, '2663_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', '12 november_6': '12 november', 'crowd_7': 'crowd', '2663_8': '2663'}

{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], '12 november_6': [0], 'crowd_7': [1], '2663_8': [2]}

['date', 'home team', 'score', 'away team', 'venue', 'crowd', 'box score', 'report']

[['12 november', 'south dragons', '97 - 82', 'adelaide 36ers', 'hisense arena', '3318', 'box score', '-'], ['12 november', 'sydney spirit', '115 - 93', 'gold coast blaze', 'state sports centre', '1430', 'box score', '-'], ['12 november', 'cairns taipans', '81 - 93', 'new zealand breakers', 'cairns convention centre', '3243', 'box score', '-'], ['14 november', 'wollongong hawks', '110 - 111', 'gold coast blaze', 'win entertainment centre', '2585', 'box score', '-'], ['15 november', 'perth wildcats', '86 - 78', 'cairns taipans', 'challenge stadium', '4200', 'box score', '-'], ['15 november', 'townsville crocodiles', '108 - 119', 'new zealand breakers', 'townsville entertainment centre', '4152', 'box score', '-'], ['16 november', 'gold coast blaze', '96 - 108', 'adelaide 36ers', 'gold coast convention centre', '2238', 'box score', '-']]

list of cities , towns and villages in vojvodina

https://en.wikipedia.org/wiki/List_of_cities%2C_towns_and_villages_in_Vojvodina

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2562572-37.html.csv

superlative

the settlement of srpska crnja has the highest population between the cities and towns of vojvodina .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'population ( 2011 )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population ( 2011 ) }'}, 'settlement'], 'result': 'srpska crnja', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population ( 2011 ) } ; settlement }'}, 'srpska crnja'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population ( 2011 ) } ; settlement } ; srpska crnja } = true', 'tointer': 'select the row whose population ( 2011 ) record of all rows is maximum . the settlement record of this row is srpska crnja .'}

eq { hop { argmax { all_rows ; population ( 2011 ) } ; settlement } ; srpska crnja } = true

select the row whose population ( 2011 ) record of all rows is maximum . the settlement record of this row is srpska crnja .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population (2011)_5': 5, 'settlement_6': 6, 'srpska crnja_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population (2011)_5': 'population ( 2011 )', 'settlement_6': 'settlement', 'srpska crnja_7': 'srpska crnja'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population (2011)_5': [0], 'settlement_6': [1], 'srpska crnja_7': [2]}

['settlement', 'cyrillic name other names', 'type', 'population ( 2011 )', 'largest ethnic group ( 2002 )', 'dominant religion ( 2002 )']

[['nova crnja', 'нова црња ( hungarian : magyarcsernye )', 'village', '1509', 'hungarians', 'catholic christianity'], ['aleksandrovo', 'александрово', 'village', '2130', 'serbs', 'orthodox christianity'], ['radojevo', 'радојево', 'village', '1056', 'serbs', 'orthodox christianity'], ['srpska crnja', 'српска црња', 'village', '3685', 'serbs', 'orthodox christianity'], ['toba', 'тоба ( hungarian : tóba )', 'village', '518', 'hungarians', 'catholic christianity']]

leeds united a.f.c. - manchester united f.c. rivalry

https://en.wikipedia.org/wiki/Leeds_United_A.F.C.%E2%80%93Manchester_United_F.C._rivalry

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15651485-2.html.csv

comparative

more goals were scored in the october 28 , 2003 match than the match on january 3 , 2010 .

{'row_1': '2', 'row_2': '4', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '28 october 2003'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 28 october 2003 .', 'tostr': 'filter_eq { all_rows ; date ; 28 october 2003 }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; 28 october 2003 } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to 28 october 2003 . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '3 january 2010'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 3 january 2010 .', 'tostr': 'filter_eq { all_rows ; date ; 3 january 2010 }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; 3 january 2010 } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to 3 january 2010 . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; 28 october 2003 } ; score } ; hop { filter_eq { all_rows ; date ; 3 january 2010 } ; score } } = true', 'tointer': 'select the rows whose date record fuzzily matches to 28 october 2003 . take the score record of this row . select the rows whose date record fuzzily matches to 3 january 2010 . take the score record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; date ; 28 october 2003 } ; score } ; hop { filter_eq { all_rows ; date ; 3 january 2010 } ; score } } = true

select the rows whose date record fuzzily matches to 28 october 2003 . take the score record of this row . select the rows whose date record fuzzily matches to 3 january 2010 . take the score record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, '28 october 2003_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, '3 january 2010_12': 12, 'score_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', '28 october 2003_8': '28 october 2003', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', '3 january 2010_12': '3 january 2010', 'score_13': 'score'}

{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], '28 october 2003_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], '3 january 2010_12': [1], 'score_13': [3]}

['date', 'home team', 'score', 'away team', 'venue', 'competition']

[['18 october 2003', 'leeds united', '0 - 1', 'manchester united', 'elland road', 'premier league'], ['28 october 2003', 'leeds united', '2 - 3', 'manchester united', 'elland road', 'league cup'], ['21 february 2004', 'manchester united', '1 - 1', 'leeds united', 'old trafford', 'premier league'], ['3 january 2010', 'manchester united', '0 - 1', 'leeds united', 'old trafford', 'fa cup'], ['20 september 2011', 'leeds united', '0 - 3', 'manchester united', 'elland road', 'league cup']]

franck lagorce

https://en.wikipedia.org/wiki/Franck_Lagorce

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1228355-3.html.csv

aggregation

the total number of laps between the 2002 and 2003 competitions are right under 700 .

{'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '699', 'subset': {'col': '1', 'criterion': 'greater_than_eq', 'value': '2002'}}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'year', '2002'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; year ; 2002 }', 'tointer': 'select the rows whose year record is greater than or equal to 2002 .'}, 'laps'], 'result': '699', 'ind': 1, 'tostr': 'sum { filter_greater_eq { all_rows ; year ; 2002 } ; laps }'}, '699'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_greater_eq { all_rows ; year ; 2002 } ; laps } ; 699 } = true', 'tointer': 'select the rows whose year record is greater than or equal to 2002 . the sum of the laps record of these rows is 699 .'}

round_eq { sum { filter_greater_eq { all_rows ; year ; 2002 } ; laps } ; 699 } = true

select the rows whose year record is greater than or equal to 2002 . the sum of the laps record of these rows is 699 .

3

3

{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, '2002_6': 6, 'laps_7': 7, '699_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '2002_6': '2002', 'laps_7': 'laps', '699_8': '699'}

{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '2002_6': [0], 'laps_7': [1], '699_8': [2]}

['year', 'team', 'co - drivers', 'class', 'laps', 'pos', 'class pos']

[['1994', 'courage compétition', 'henri pescarolo alain ferté', 'lmp1 c90', '142', 'dnf', 'dnf'], ['1995', 'courage compétition', 'henri pescarolo éric bernard', 'wsc', '26', 'dnf', 'dnf'], ['1996', 'la filière elf', 'henri pescarolo emmanuel collard', 'lmp1', '327', '7th', '2nd'], ['1997', 'dams', 'éric bernard jean - christophe boullion', 'gt1', '149', 'dnf', 'dnf'], ['1998', 'nissan motorsports twr', 'john nielsen michael krumm', 'gt1', '342', '5th', '5th'], ['1999', 'amg - mercedes', 'bernd schneider pedro lamy', 'lmgtp', '76', 'dnf', 'dnf'], ['2000', 'team cadillac', 'butch leitzinger andy wallace', 'lmp900', '291', '21st', '11th'], ['2001', 'panoz motorsports', 'david brabham jan magnussen', 'lmp900', '85', 'dnf', 'dnf'], ['2002', 'pescarolo sport', 'sébastien bourdais jean - christophe boullion', 'lmp900', '343', '10th', '9th'], ['2003', 'pescarolo sport', 'stéphane sarrazin jean - christophe boullion', 'lmp900', '356', '8th', '6th']]

2008 indiana fever season

https://en.wikipedia.org/wiki/2008_Indiana_Fever_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17104539-12.html.csv

majority

in the 2008 indiana fever season , when catchings had the high rebounds , he also had the high points most of the time .

{'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'catchings', 'subset': {'col': '6', 'criterion': 'fuzzily_match', 'value': 'catchings'}}

{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', 'catchings'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high rebounds ; catchings }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to catchings .'}, 'high points', 'catchings'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose high rebounds record fuzzily matches to catchings . for the high points records of these rows , most of them fuzzily match to catchings .', 'tostr': 'most_eq { filter_eq { all_rows ; high rebounds ; catchings } ; high points ; catchings } = true'}

most_eq { filter_eq { all_rows ; high rebounds ; catchings } ; high points ; catchings } = true

select the rows whose high rebounds record fuzzily matches to catchings . for the high points records of these rows , most of them fuzzily match to catchings .

2

2

{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'high rebounds_4': 4, 'catchings_5': 5, 'high points_6': 6, 'catchings_7': 7}

{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'high rebounds_4': 'high rebounds', 'catchings_5': 'catchings', 'high points_6': 'high points', 'catchings_7': 'catchings'}

{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'high rebounds_4': [0], 'catchings_5': [0], 'high points_6': [1], 'catchings_7': [1]}

['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record']

[['29', 'september 2', 'washington', 'w 79 - 68', 'catchings ( 26 )', 'catchings ( 9 )', 'douglas ( 4 )', 'verizon center 7244', '14 - 15'], ['30', 'september 5', 'detroit', 'l 68 - 90', 'catchings ( 20 )', 'catchings ( 10 )', 'bevilaqua ( 4 )', 'the palace of auburn hills 9287', '14 - 16'], ['31', 'september 8', 'atlanta', 'w 81 - 77', 'white ( 24 )', 'catchings ( 10 )', 'catchings ( 6 )', 'philips arena 7706', '15 - 16'], ['32', 'september 9', 'minnesota', 'l 86 - 76', 'white ( 21 )', 'sutton - brown ( 11 )', 'catchings ( 7 )', 'target center 6706', '15 - 17'], ['33', 'september 11', 'new york', 'w 74 - 59', 'sutton - brown ( 16 )', 'catchings ( 8 )', 'douglas ( 5 )', 'conseco fieldhouse 7062', '16 - 17']]

1951 - 52 segunda división

https://en.wikipedia.org/wiki/1951%E2%80%9352_Segunda_Divisi%C3%B3n

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17298923-2.html.csv

comparative

the position 1 team in the 1951 - 52 segunda división had more draws than the position 2 team .

{'row_1': '1', 'row_2': '2', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 1 .', 'tostr': 'filter_eq { all_rows ; position ; 1 }'}, 'draws'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; 1 } ; draws }', 'tointer': 'select the rows whose position record fuzzily matches to 1 . take the draws record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose position record fuzzily matches to 2 .', 'tostr': 'filter_eq { all_rows ; position ; 2 }'}, 'draws'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; position ; 2 } ; draws }', 'tointer': 'select the rows whose position record fuzzily matches to 2 . take the draws record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; position ; 1 } ; draws } ; hop { filter_eq { all_rows ; position ; 2 } ; draws } } = true', 'tointer': 'select the rows whose position record fuzzily matches to 1 . take the draws record of this row . select the rows whose position record fuzzily matches to 2 . take the draws record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; position ; 1 } ; draws } ; hop { filter_eq { all_rows ; position ; 2 } ; draws } } = true

select the rows whose position record fuzzily matches to 1 . take the draws record of this row . select the rows whose position record fuzzily matches to 2 . take the draws record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'position_7': 7, '1_8': 8, 'draws_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'position_11': 11, '2_12': 12, 'draws_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'position_7': 'position', '1_8': '1', 'draws_9': 'draws', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'position_11': 'position', '2_12': '2', 'draws_13': 'draws'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'position_7': [0], '1_8': [0], 'draws_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'position_11': [1], '2_12': [1], 'draws_13': [3]}

['position', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference']

[['1', '30', '39', '16', '7', '7', '66', '29', '+ 37'], ['2', '30', '36', '15', '6', '9', '48', '40', '+ 8'], ['3', '30', '33', '11', '11', '8', '55', '44', '+ 11'], ['4', '30', '33', '13', '7', '10', '58', '44', '+ 14'], ['5', '30', '33', '13', '7', '10', '61', '32', '+ 29'], ['6', '30', '33', '12', '9', '9', '49', '41', '+ 8'], ['7', '30', '32', '14', '4', '12', '41', '52', '- 11'], ['8', '30', '32', '13', '6', '11', '55', '45', '+ 10'], ['9', '30', '30', '10', '10', '10', '51', '50', '+ 1'], ['10', '30', '29', '12', '5', '13', '49', '60', '- 11'], ['11', '30', '29', '13', '3', '14', '56', '64', '- 8'], ['12', '30', '28', '12', '4', '14', '51', '57', '- 6'], ['13', '30', '28', '9', '10', '11', '51', '71', '- 20'], ['14', '30', '24', '9', '6', '15', '40', '56', '- 16'], ['15', '30', '21', '7', '7', '16', '49', '64', '- 15'], ['16', '30', '20', '5', '10', '15', '32', '63', '- 31']]

amanda overmyer

https://en.wikipedia.org/wiki/Amanda_Overmyer

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15796072-1.html.csv

majority

amanda overmyer was safe the majority of the weeks she competed .

{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'safe', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'safe'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to safe .', 'tostr': 'most_eq { all_rows ; result ; safe } = true'}

most_eq { all_rows ; result ; safe } = true

for the result records of all rows , most of them fuzzily match to safe .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'safe_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'safe_4': 'safe'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'safe_4': [0]}

['week', 'theme', 'song choice', 'original artist', 'order', 'result']

[['hollywood', 'n / a', 'light my fire', 'the doors', 'n / a', 'advanced'], ['hollywood', 'n / a', 'piece of my heart', 'erma franklin', 'n / a', 'advanced'], ['top 24 ( 12 women )', '1960s', "baby , please do n't go", 'big joe williams', '4', 'safe'], ['top 20 ( 10 women )', '1970s', 'carry on wayward son', 'kansas', '6', 'safe'], ['top 16 ( 8 women )', '1980s', 'i hate myself for loving you', 'joan jett and the blackhearts', '3', 'safe'], ['top 12', 'lennonmccartney', "you ca n't do that", 'the beatles', '9', 'safe']]

united states house of representatives elections , 1998

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1998

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341453-7.html.csv

aggregation

the average winning candidate won with 76.08 % of the votes .

{'scope': 'all', 'col': '6', 'type': 'average', 'result': '76.075 %', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'candidates'], 'result': '76.075 %', 'ind': 0, 'tostr': 'avg { all_rows ; candidates }'}, '76.075 %'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; candidates } ; 76.075 % } = true', 'tointer': 'the average of the candidates record of all rows is 76.075 % .'}

round_eq { avg { all_rows ; candidates } ; 76.075 % } = true

the average of the candidates record of all rows is 76.075 % .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'candidates_4': 4, '76.075%_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'candidates_4': 'candidates', '76.075%_5': '76.075 %'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'candidates_4': [0], '76.075%_5': [1]}

['district', 'incumbent', 'party', 'first elected', 'results', 'candidates']

[['california 7', 'george miller', 'democratic', '1974', 're - elected', 'george miller ( d ) 77 % norman reece ( r ) 23 %'], ['california 18', 'gary condit', 'democratic', '1989', 're - elected', 'gary condit ( d ) 86.7 % linda degroat ( l ) 13.2 %'], ['california 20', 'cal dooley', 'democratic', '1990', 're - elected', 'cal dooley ( d ) 60.7 % cliff unruh ( r ) 39.3 %'], ['california 21', 'bill thomas', 'republican', '1978', 're - elected', 'bill thomas ( r ) 78.9 % john evans ( ref ) 21 %'], ['california 23', 'elton gallegly', 'republican', '1986', 're - elected', 'elton gallegly ( r ) 60 % daniel gonzalez ( d ) 39.4 %'], ['california 25', 'howard mckeon', 'republican', '1992', 're - elected', 'howard mckeon ( r ) 74.7 % bruce acker ( l ) 25.3 %'], ['california 30', 'xavier becerra', 'democratic', '1992', 're - elected', 'xavier becerra ( d ) 81.3 % patricia parker ( r ) 18.8 %'], ['california 35', 'maxine waters', 'democratic', '1990', 're - elected', 'maxine waters ( d ) 89.3 % gordon mego ( ai ) 10.7 %']]

list of television show franchises

https://en.wikipedia.org/wiki/List_of_television_show_franchises

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12438767-1.html.csv

count

warren mitchell starred as alf garnett in a total of three television show franchises .

{'scope': 'all', 'criterion': 'equal', 'value': 'warren mitchell as alf garnett', 'result': '3', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'starring', 'warren mitchell as alf garnett'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose starring record fuzzily matches to warren mitchell as alf garnett .', 'tostr': 'filter_eq { all_rows ; starring ; warren mitchell as alf garnett }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; starring ; warren mitchell as alf garnett } }', 'tointer': 'select the rows whose starring record fuzzily matches to warren mitchell as alf garnett . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; starring ; warren mitchell as alf garnett } } ; 3 } = true', 'tointer': 'select the rows whose starring record fuzzily matches to warren mitchell as alf garnett . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; starring ; warren mitchell as alf garnett } } ; 3 } = true

select the rows whose starring record fuzzily matches to warren mitchell as alf garnett . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'starring_5': 5, 'warren mitchell as alf garnett_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'starring_5': 'starring', 'warren mitchell as alf garnett_6': 'warren mitchell as alf garnett', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'starring_5': [0], 'warren mitchell as alf garnett_6': [0], '3_7': [2]}

['region / country', 'local name', 'network', 'dates aired', 'starring']

[['united kingdom', 'till death us do part', 'bbc one', '1965 - 1968 , 1970 , 1972 - 1975', 'warren mitchell as alf garnett'], ['united kingdom', 'till death', 'itv', '1981', 'warren mitchell as alf garnett'], ['united kingdom', 'in sickness and in health', 'bbc one', '1985 - 1992', 'warren mitchell as alf garnett'], ['united states', 'all in the family', 'cbs', '1971 - 1979', "carroll o'connor as archie bunker"], ['united states', "archie bunker 's place", 'cbs', '1979 - 1983', "carroll o'connor as archie bunker"], ['germany', 'ein herz und eine seele', 'wdr , ard', '1973 - 1976', 'heinz schubert as alfred tetzlaff']]

dancing on ice ( series 4 )

https://en.wikipedia.org/wiki/Dancing_on_Ice_%28series_4%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19744915-4.html.csv

ordinal

the second highest score that jury nicky gave was conceded to melinda & fred .

{'row': '2', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'nicky', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; nicky ; 2 }'}, 'couple'], 'result': 'melinda & fred', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; nicky ; 2 } ; couple }'}, 'melinda & fred'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; nicky ; 2 } ; couple } ; melinda & fred } = true', 'tointer': 'select the row whose nicky record of all rows is 2nd maximum . the couple record of this row is melinda & fred .'}

eq { hop { nth_argmax { all_rows ; nicky ; 2 } ; couple } ; melinda & fred } = true

select the row whose nicky record of all rows is 2nd maximum . the couple record of this row is melinda & fred .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'nicky_5': 5, '2_6': 6, 'couple_7': 7, 'melinda & fred_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'nicky_5': 'nicky', '2_6': '2', 'couple_7': 'couple', 'melinda & fred_8': 'melinda & fred'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'nicky_5': [0], '2_6': [0], 'couple_7': [1], 'melinda & fred_8': [2]}

['order', 'couple', 'karen', 'nicky', 'jason', 'ruthie', 'robin', 'total', 'skating song', 'scoreboard', 'public vote']

[['1', 'roxanne & daniel', '3.5', '3.0', '2.5', '3.0', '3.5', '15.0', 'take a bow - rihanna', '4th', '13.563 %'], ['2', 'melinda & fred', '3.5', '3.5', '2.5', '3.5', '3.5', '15.5', 'love song - sara bareilles', '3rd', '2.922 %'], ['3', 'coleen & stuart', '2.5', '2.5', '2.0', '2.5', '3.0', '12.5', 'dream a little dream of me - ella fitzgerald', '6th', '61.801 %'], ['4', 'zöe & matt', '4.0', '4.0', '3.0', '3.5', '4.0', '18.5', 'i wan na dance with somebody - whitney houston', '2nd', '7.593 %'], ['5', 'gemma & andrei', '2.5', '3.0', '2.0', '2.5', '3.0', '13.5', 'the power of love - jennifer rush', '5th', '3.901 %']]

pádraig harrington

https://en.wikipedia.org/wiki/P%C3%A1draig_Harrington

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1157761-9.html.csv

unique

the masters tournament is the only tournament where pádraig harrington was in the top-25 6 times .

{'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '6', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'top - 25', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose top - 25 record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; top - 25 ; 6 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; top - 25 ; 6 } }', 'tointer': 'select the rows whose top - 25 record is equal to 6 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'top - 25', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose top - 25 record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; top - 25 ; 6 }'}, 'tournament'], 'result': 'masters tournament', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; top - 25 ; 6 } ; tournament }'}, 'masters tournament'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; top - 25 ; 6 } ; tournament } ; masters tournament }', 'tointer': 'the tournament record of this unqiue row is masters tournament .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; top - 25 ; 6 } } ; eq { hop { filter_eq { all_rows ; top - 25 ; 6 } ; tournament } ; masters tournament } } = true', 'tointer': 'select the rows whose top - 25 record is equal to 6 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .'}

and { only { filter_eq { all_rows ; top - 25 ; 6 } } ; eq { hop { filter_eq { all_rows ; top - 25 ; 6 } ; tournament } ; masters tournament } } = true

select the rows whose top - 25 record is equal to 6 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'top - 25_7': 7, '6_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'masters tournament_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'top - 25_7': 'top - 25', '6_8': '6', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'masters tournament_10': 'masters tournament'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'top - 25_7': [0], '6_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'masters tournament_10': [3]}

['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made']

[['masters tournament', '0', '2', '4', '6', '14', '9'], ['us open', '0', '3', '5', '7', '16', '12'], ['the open championship', '2', '4', '4', '7', '17', '12'], ['pga championship', '1', '1', '2', '4', '14', '9'], ['totals', '3', '10', '15', '24', '61', '42']]

list of dr. floyd episodes

https://en.wikipedia.org/wiki/List_of_Dr._Floyd_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10621888-3.html.csv

ordinal

for dr. floyd episodes , the one with the 2nd longest run time is episode number 311 .

{'row': '11', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'run time', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; run time ; 2 }'}, 'episode number'], 'result': '311', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; run time ; 2 } ; episode number }'}, '311'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; run time ; 2 } ; episode number } ; 311 } = true', 'tointer': 'select the row whose run time record of all rows is 2nd maximum . the episode number record of this row is 311 .'}

eq { hop { nth_argmax { all_rows ; run time ; 2 } ; episode number } ; 311 } = true

select the row whose run time record of all rows is 2nd maximum . the episode number record of this row is 311 .

3

3

{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'run time_5': 5, '2_6': 6, 'episode number_7': 7, '311_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'run time_5': 'run time', '2_6': '2', 'episode number_7': 'episode number', '311_8': '311'}

{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'run time_5': [0], '2_6': [0], 'episode number_7': [1], '311_8': [2]}

['episode number', 'title', 'podcast date', 'run time', 'historical references']

[['301', 'home sweet home !', 'august 1 , 2005', '6:07', 'none'], ['302', 'the adventures of lewis & clark !', 'august 8 , 2005', '4:16', 'meriwether lewis & william clark'], ['303', 'call of the wild !', 'august 14 , 2005', '4:49', 'meriwether lewis & william clark'], ['304', 'the greatest show on earth !', 'august 21 , 2005', '5:16', 'pt barnum'], ['305', 'hitting the bricks !', 'august 28 , 2005', '5:48', 'pt barnum'], ['306', 'fiji queasy !', 'september 4 , 2005', '4:59', 'pt barnum'], ['307', 'accident in time !', 'september 11 , 2005', '5:04', 'none'], ['308', "all 's wells that ends welles !", 'september 18 , 2005', '5:51', 'hg wells & orson welles'], ['309', 'jump the shark !', 'september 25 , 2005', '5:04', 'jumping the shark'], ['310', 'jump the shark ! part ii !', 'october 2 , 2005', '4:36', 'jumping the shark'], ['311', 'annie are you oakley are you oakley , annie !', 'october 9 , 2005', '6:13', 'annie oakley & buffalo bill cody'], ['312', 'reach for the sky !', 'october 16 , 2005', '5:52', 'annie oakley & buffalo bill cody'], ['313', 'as the worm turns !', 'october 23 , 2005', '6:31', 'none']]

2006 - 07 tottenham hotspur f.c. season

https://en.wikipedia.org/wiki/2006%E2%80%9307_Tottenham_Hotspur_F.C._season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17583318-4.html.csv

comparative

pascal chimbonda had more appearances in the 2006 - 07 tottenham hotspur f.c. season than robbie keane .

{'row_1': '3', 'row_2': '5', 'col': '7', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'pascal chimbonda'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to pascal chimbonda .', 'tostr': 'filter_eq { all_rows ; player ; pascal chimbonda }'}, 'total'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; pascal chimbonda } ; total }', 'tointer': 'select the rows whose player record fuzzily matches to pascal chimbonda . take the total record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'robbie keane'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to robbie keane .', 'tostr': 'filter_eq { all_rows ; player ; robbie keane }'}, 'total'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; robbie keane } ; total }', 'tointer': 'select the rows whose player record fuzzily matches to robbie keane . take the total record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; pascal chimbonda } ; total } ; hop { filter_eq { all_rows ; player ; robbie keane } ; total } } = true', 'tointer': 'select the rows whose player record fuzzily matches to pascal chimbonda . take the total record of this row . select the rows whose player record fuzzily matches to robbie keane . take the total record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; player ; pascal chimbonda } ; total } ; hop { filter_eq { all_rows ; player ; robbie keane } ; total } } = true

select the rows whose player record fuzzily matches to pascal chimbonda . take the total record of this row . select the rows whose player record fuzzily matches to robbie keane . take the total record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, 'pascal chimbonda_8': 8, 'total_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'robbie keane_12': 12, 'total_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', 'pascal chimbonda_8': 'pascal chimbonda', 'total_9': 'total', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'robbie keane_12': 'robbie keane', 'total_13': 'total'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'pascal chimbonda_8': [0], 'total_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'robbie keane_12': [1], 'total_13': [3]}

['player', 'position', 'premier league', 'fa cup', 'league cup', 'uefa cup', 'total']

[['michael dawson', 'defender', '37', '6', '4', '10', '57'], ['paul robinson', 'goalkeeper', '38', '4', '3', '9', '54'], ['pascal chimbonda', 'defender', '33', '4', '4', '10', '51'], ['jermain defoe', 'forward', '33', '5', '5', '5', '48'], ['robbie keane', 'forward', '27', '5', '3', '9', '44']]

csi ( franchise )

https://en.wikipedia.org/wiki/CSI_%28franchise%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10819266-8.html.csv

majority

the majority of csi seasons aired in the wednesday 10 pm / 9c time slot .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'wednesday 10 pm / 9c', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'time slot ( est )', 'wednesday 10 pm / 9c'], 'result': True, 'ind': 0, 'tointer': 'for the time slot ( est ) records of all rows , most of them fuzzily match to wednesday 10 pm / 9c .', 'tostr': 'most_eq { all_rows ; time slot ( est ) ; wednesday 10 pm / 9c } = true'}

most_eq { all_rows ; time slot ( est ) ; wednesday 10 pm / 9c } = true

for the time slot ( est ) records of all rows , most of them fuzzily match to wednesday 10 pm / 9c .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'time slot (est)_3': 3, 'wednesday 10 pm / 9c_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'time slot (est)_3': 'time slot ( est )', 'wednesday 10 pm / 9c_4': 'wednesday 10 pm / 9c'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'time slot (est)_3': [0], 'wednesday 10 pm / 9c_4': [0]}

['season', 'episodes', 'time slot ( est )', 'season premiere', 'season finale', 'tv season', 'rank', 'viewers ( in millions )']

[['1', '23', 'wednesday 10 pm / 9c', 'september 22 , 2004', 'may 18 , 2005', '2004 - 2005', '21', '13.59'], ['2', '24', 'wednesday 10 pm / 9c', 'september 28 , 2005', 'may 17 , 2006', '2005 - 2006', '22', '14.04'], ['3', '24', 'wednesday 10 pm / 9c', 'september 20 , 2006', 'may 16 , 2007', '2006 - 2007', '25', '13.92'], ['4', '21', 'wednesday 10 pm / 9c', 'september 26 , 2007', 'may 21 , 2008', '2007 - 2008', '28', '11.71'], ['5', '25', 'wednesday 10 pm / 9c', 'september 24 , 2008', 'may 14 , 2009', '2008 - 2009', '17', '13.50'], ['6', '23', 'wednesday 10 pm / 9c', 'september 23 , 2009', 'may 26 , 2010', '2009 - 2010', '23', '12.66'], ['7', '22', 'friday 9 pm / 8c', 'september 24 , 2010', 'may 13 , 2011', '2010 - 2011', '37', '10.73'], ['8', '18', 'friday 9 pm / 8c', 'september 23 , 2011', 'may 11 , 2012', '2011 - 2012', '38', '10.34']]

united states house of representatives elections , 1828

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1828

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668243-17.html.csv

count

three incumbents from new york districts that ran in the 1828 united states house of representatives elections were first seated in 1824 .

{'scope': 'all', 'criterion': 'equal', 'value': '1824', 'result': '3', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'first elected', '1824'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first elected record is equal to 1824 .', 'tostr': 'filter_eq { all_rows ; first elected ; 1824 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; first elected ; 1824 } }', 'tointer': 'select the rows whose first elected record is equal to 1824 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; first elected ; 1824 } } ; 3 } = true', 'tointer': 'select the rows whose first elected record is equal to 1824 . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; first elected ; 1824 } } ; 3 } = true

select the rows whose first elected record is equal to 1824 . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '1824_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '1824_6': '1824', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '1824_6': [0], '3_7': [2]}

['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']

[['new york 1', 'silas wood', 'anti - jacksonian', '1818', 'lost re - election jacksonian gain', 'james lent ( j ) 52.3 % silas wood ( aj ) 47.7 %'], ['new york 6', 'john hallock , jr', 'jacksonian', '1824', 'retired jacksonian hold', 'hector craig ( j ) 55.7 % samuel j wilkin ( aj ) 44.3 %'], ['new york 8', 'james strong', 'anti - jacksonian', '1818 1822', 're - elected', 'james strong ( aj ) 50.9 % james vanderpoel ( j ) 49.1 %'], ['new york 11', 'selah r hobbie', 'jacksonian', '1826', 'retired jacksonian hold', 'perkins king ( j ) 61.6 % jacob haight ( aj ) 38.4 %'], ['new york 15', 'michael hoffman', 'jacksonian', '1824', 're - elected', 'michael hoffman ( j ) 100 %'], ['new york 17', 'john w taylor', 'anti - jacksonian', '1812', 're - elected', 'john w taylor ( aj ) 54.9 % john cramer ( j ) 45.1 %'], ['new york 21', 'john c clark', 'jacksonian', '1826', 'retired jacksonian hold', 'robert monell ( j ) 63.6 % tilly lynde 36.4 %'], ['new york 22', 'john g stower', 'jacksonian', '1824', 'lost re - election anti - jacksonian gain', 'thomas beekman ( aj ) 53.4 % john g stower ( j ) 46.6 %'], ['new york 25', 'david woodcock', 'anti - jacksonian', '1821 1826', 'lost re - election jacksonian gain', 'thomas maxwell ( j ) 60.1 % david woodcock ( aj ) 39.9 %']]

2010 - 11 atlanta hawks season

https://en.wikipedia.org/wiki/2010%E2%80%9311_Atlanta_Hawks_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27734577-2.html.csv

aggregation

the 2010-2011 atlanta hawks scored an average of 90.25 points in their first four pre-season losses .

{'scope': 'subset', 'col': '4', 'type': 'average', 'result': '90.25', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'l'}}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', 'l'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; score ; l }', 'tointer': 'select the rows whose score record fuzzily matches to l .'}, 'score'], 'result': '90.25', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; score ; l } ; score }'}, '90.25'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; score ; l } ; score } ; 90.25 } = true', 'tointer': 'select the rows whose score record fuzzily matches to l . the average of the score record of these rows is 90.25 .'}

round_eq { avg { filter_eq { all_rows ; score ; l } ; score } ; 90.25 } = true

select the rows whose score record fuzzily matches to l . the average of the score record of these rows is 90.25 .

3

3

{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'score_5': 5, 'l_6': 6, 'score_7': 7, '90.25_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'score_5': 'score', 'l_6': 'l', 'score_7': 'score', '90.25_8': '90.25'}

{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'score_5': [0], 'l_6': [0], 'score_7': [1], '90.25_8': [2]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['1', 'october 7', 'memphis', 'l 111 - 115 ( ot )', 'jeff teague ( 20 )', 'marvin williams ( 10 )', 'jeff teague ( 6 )', 'philips arena 7132', '0 - 1'], ['2', 'october 11', 'detroit', 'l 85 - 94 ( ot )', 'jordan crawford ( 20 )', 'josh smith ( 7 )', 'jordan crawford ( 7 )', 'the palace of auburn hills 10591', '0 - 2'], ['3', 'october 12', 'washington', 'l 92 - 107 ( ot )', 'jordan crawford ( 30 )', 'zaza pachulia ( 6 )', 'jordan crawford ( 5 )', 'verizon center 9230', '0 - 3'], ['5', 'october 18', 'orlando', 'l 73 - 102 ( ot )', 'josh powell ( 13 )', 'josh smith ( 7 )', 'joe johnson ( 4 )', 'philips arena 7571', '1 - 4'], ['6', 'october 21', 'miami', 'w 98 - 89 ( ot )', 'joe johnson ( 27 )', 'marvin williams ( 11 )', 'joe johnson ( 6 )', 'philips arena 15197', '2 - 4']]

marlboro challenge

https://en.wikipedia.org/wiki/Marlboro_Challenge

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15736385-1.html.csv

majority

most of the marlboro challenges took place in the month of october .

{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'october', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'date', 'october'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them fuzzily match to october .', 'tostr': 'most_eq { all_rows ; date ; october } = true'}

most_eq { all_rows ; date ; october } = true

for the date records of all rows , most of them fuzzily match to october .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'october_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'october_4': 'october'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'october_4': [0]}

['season', 'date', 'location', 'driver', 'chassis', 'engine', 'team']

[['1987', 'october 31', 'tamiami park', 'bobby rahal', 'lola', 'cosworth', 'truesports'], ['1988', 'november 5', 'tamiami park', 'michael andretti', 'lola', 'cosworth', 'kraco racing'], ['1989', 'october 14', 'laguna seca', 'al unser , jr', 'lola', 'chevrolet', 'galles racing'], ['1990', 'october 6', 'nazareth', 'rick mears', 'penske', 'chevrolet', 'penske racing'], ['1991', 'october 19', 'laguna seca', 'michael andretti', 'lola', 'chevrolet', 'newman / haas racing'], ['1992', 'october 3', 'nazareth', 'emerson fittipaldi', 'penske', 'chevrolet', 'penske racing']]

2007 - 08 la liga

https://en.wikipedia.org/wiki/2007%E2%80%9308_La_Liga

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11834742-5.html.csv

unique

the only player with 60 or more goals is stefano sorrentino .

{'scope': 'all', 'row': '9', 'col': '2', 'col_other': '1', 'criterion': 'greater_than', 'value': '59', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'goals', '59'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals record is greater than 59 .', 'tostr': 'filter_greater { all_rows ; goals ; 59 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; goals ; 59 } }', 'tointer': 'select the rows whose goals record is greater than 59 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'goals', '59'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals record is greater than 59 .', 'tostr': 'filter_greater { all_rows ; goals ; 59 }'}, 'goalkeeper'], 'result': 'stefano sorrentino', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; goals ; 59 } ; goalkeeper }'}, 'stefano sorrentino'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; goals ; 59 } ; goalkeeper } ; stefano sorrentino }', 'tointer': 'the goalkeeper record of this unqiue row is stefano sorrentino .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; goals ; 59 } } ; eq { hop { filter_greater { all_rows ; goals ; 59 } ; goalkeeper } ; stefano sorrentino } } = true', 'tointer': 'select the rows whose goals record is greater than 59 . there is only one such row in the table . the goalkeeper record of this unqiue row is stefano sorrentino .'}

and { only { filter_greater { all_rows ; goals ; 59 } } ; eq { hop { filter_greater { all_rows ; goals ; 59 } ; goalkeeper } ; stefano sorrentino } } = true

select the rows whose goals record is greater than 59 . there is only one such row in the table . the goalkeeper record of this unqiue row is stefano sorrentino .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'goals_7': 7, '59_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'goalkeeper_9': 9, 'stefano sorrentino_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'goals_7': 'goals', '59_8': '59', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'goalkeeper_9': 'goalkeeper', 'stefano sorrentino_10': 'stefano sorrentino'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'goals_7': [0], '59_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'goalkeeper_9': [2], 'stefano sorrentino_10': [3]}

['goalkeeper', 'goals', 'matches', 'average', 'team']

[['iker casillas', '32', '36', '0.89', 'real madrid'], ['víctor valdés', '35', '35', '1', 'fc barcelona'], ['toño', '31', '30', '1.03', 'racing de santander'], ['ricardo lópez felipe', '38', '36', '1.06', 'ca osasuna'], ['miguel ángel moyà', '34', '29', '1.17', 'rcd mallorca'], ['roberto abbondanzieri', '42', '34', '1.24', 'getafe cf'], ['carlos kameni', '38', '29', '1.31', 'rcd espanyol'], ['andrés palop', '41', '30', '1.37', 'sevilla fc'], ['stefano sorrentino', '60', '38', '1.58', 'recreativo de huelva'], ['césar sánchez', '56', '35', '1.6', 'zaragoza']]

list of how it 's made episodes

https://en.wikipedia.org/wiki/List_of_How_It%27s_Made_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15187735-9.html.csv

comparative

how it 's made did a segment about outboard motors before they did one about snowblowers .

{'row_1': '10', 'row_2': '12', 'col': '2', 'col_other': '5', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment b', 'outboard motors'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment b record fuzzily matches to outboard motors .', 'tostr': 'filter_eq { all_rows ; segment b ; outboard motors }'}, 'episode'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment b ; outboard motors } ; episode }', 'tointer': 'select the rows whose segment b record fuzzily matches to outboard motors . take the episode record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment b', 's snowblower'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose segment b record fuzzily matches to s snowblower .', 'tostr': 'filter_eq { all_rows ; segment b ; s snowblower }'}, 'episode'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; segment b ; s snowblower } ; episode }', 'tointer': 'select the rows whose segment b record fuzzily matches to s snowblower . take the episode record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; segment b ; outboard motors } ; episode } ; hop { filter_eq { all_rows ; segment b ; s snowblower } ; episode } } = true', 'tointer': 'select the rows whose segment b record fuzzily matches to outboard motors . take the episode record of this row . select the rows whose segment b record fuzzily matches to s snowblower . take the episode record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; segment b ; outboard motors } ; episode } ; hop { filter_eq { all_rows ; segment b ; s snowblower } ; episode } } = true

select the rows whose segment b record fuzzily matches to outboard motors . take the episode record of this row . select the rows whose segment b record fuzzily matches to s snowblower . take the episode record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'segment b_7': 7, 'outboard motors_8': 8, 'episode_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'segment b_11': 11, 's snowblower_12': 12, 'episode_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'segment b_7': 'segment b', 'outboard motors_8': 'outboard motors', 'episode_9': 'episode', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'segment b_11': 'segment b', 's snowblower_12': 's snowblower', 'episode_13': 'episode'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'segment b_7': [0], 'outboard motors_8': [0], 'episode_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'segment b_11': [1], 's snowblower_12': [1], 'episode_13': [3]}

['series ep', 'episode', 'netflix', 'segment a', 'segment b', 'segment c', 'segment d']

[['9 - 01', '105', 's05e01', 'solid tires', 'cheesecake', 'canoe paddles', 's globe'], ['9 - 02', '106', 's05e02', 'boomerangs', 'barbecues', 'pinball machines', 'strobe lights'], ['9 - 03', '107', 's05e03', 'wooden bowls', 'chainsaws', 'stackable potato chips', 'jet compressor blades'], ['9 - 04', '108', 's05e04', 'steel wool', 'ranges', 'carved candles', 'slot machines'], ['9 - 05', '109', 's05e05', 'ccd semiconductors', 'airline meals', 'paper cups', 's trumpet'], ['9 - 06', '110', 's05e06', 's padlock', 'hair clippers', 'wooden shoes', 'synthetic leather'], ['9 - 07', '111', 's05e07', 'racing shells', 'stainless steel sinks', 'leather', 'pedal steel guitars'], ['9 - 08', '112', 's05e08', 'swords', 'pontoons', 'grandfather clocks', 'fuses'], ['9 - 09', '113', 's05e09', 'bumpers', 'lighting gels & camera filters', 'steam - powered models', 'candy canes'], ['9 - 10', '114', 's05e10', 'umbrellas', 'outboard motors', 'silver cutlery', 'tape measures'], ['9 - 11', '115', 's05e11', 'scalpels', 'oil paint', 'british police helmets', 'ice axes'], ['9 - 12', '116', 's05e12', 'bacon', 's snowblower', 'luxury cars ( part 1 )', 'luxury cars ( part 2 )']]

list of eintracht frankfurt records and statistics

https://en.wikipedia.org/wiki/List_of_Eintracht_Frankfurt_records_and_statistics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15453888-2.html.csv

comparative

alfred pfaff has made more goals in his carrer in the eintracht frankfurt club compared to lothar schämer .

{'row_1': '4', 'row_2': '9', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'alfred pfaff'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to alfred pfaff .', 'tostr': 'filter_eq { all_rows ; name ; alfred pfaff }'}, 'goals'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; alfred pfaff } ; goals }', 'tointer': 'select the rows whose name record fuzzily matches to alfred pfaff . take the goals record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'lothar schämer'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to lothar schämer .', 'tostr': 'filter_eq { all_rows ; name ; lothar schämer }'}, 'goals'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; lothar schämer } ; goals }', 'tointer': 'select the rows whose name record fuzzily matches to lothar schämer . take the goals record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; alfred pfaff } ; goals } ; hop { filter_eq { all_rows ; name ; lothar schämer } ; goals } } = true', 'tointer': 'select the rows whose name record fuzzily matches to alfred pfaff . take the goals record of this row . select the rows whose name record fuzzily matches to lothar schämer . take the goals record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; name ; alfred pfaff } ; goals } ; hop { filter_eq { all_rows ; name ; lothar schämer } ; goals } } = true

select the rows whose name record fuzzily matches to alfred pfaff . take the goals record of this row . select the rows whose name record fuzzily matches to lothar schämer . take the goals record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name_7': 7, 'alfred pfaff_8': 8, 'goals_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'lothar schämer_12': 12, 'goals_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'alfred pfaff_8': 'alfred pfaff', 'goals_9': 'goals', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'lothar schämer_12': 'lothar schämer', 'goals_13': 'goals'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'alfred pfaff_8': [0], 'goals_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'lothar schämer_12': [1], 'goals_13': [3]}

['name', 'career', 'apps', 'goals', 'average']

[['bernd hölzenbein', '1967 - 1981', '512', '201', '0.39'], ['bernd nickel', '1967 - 1983', '522', '175', '0.34'], ['jürgen grabowski', '1965 - 1980', '526', '137', '0.26'], ['alfred pfaff', '1949 - 1961', '324', '111', '0.34'], ['erwin stein', '1959 - 1966', '174', '108', '0.62'], ['tony yeboah', '1990 - 1995', '156', '90', '0.58'], ['richard kress', '1953 - 1964', '326', '82', '0.25'], ['willi huberts', '1963 - 1970', '246', '80', '0.33'], ['lothar schämer', '1960 - 1973', '338', '73', '0.22'], ['wolfgang solz', '1959 - 1968', '208', '72', '0.35']]

1935 vfl season

https://en.wikipedia.org/wiki/1935_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790651-11.html.csv

majority

in the 1935 vfl season , for the games where the home team had a score of at least 11 , most of the crowds were over 10000 .

{'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10000', 'subset': {'col': '2', 'criterion': 'greater_than_eq', 'value': '11'}}

{'func': 'most_greater', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'home team score', '11'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; home team score ; 11 }', 'tointer': 'select the rows whose home team score record is greater than or equal to 11 .'}, 'crowd', '10000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose home team score record is greater than or equal to 11 . for the crowd records of these rows , most of them are greater than 10000 .', 'tostr': 'most_greater { filter_greater_eq { all_rows ; home team score ; 11 } ; crowd ; 10000 } = true'}

most_greater { filter_greater_eq { all_rows ; home team score ; 11 } ; crowd ; 10000 } = true

select the rows whose home team score record is greater than or equal to 11 . for the crowd records of these rows , most of them are greater than 10000 .

2

2

{'most_greater_1': 1, 'result_2': 2, 'filter_greater_eq_0': 0, 'all_rows_3': 3, 'home team score_4': 4, '11_5': 5, 'crowd_6': 6, '10000_7': 7}

{'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_3': 'all_rows', 'home team score_4': 'home team score', '11_5': '11', 'crowd_6': 'crowd', '10000_7': '10000'}

{'most_greater_1': [2], 'result_2': [], 'filter_greater_eq_0': [1], 'all_rows_3': [0], 'home team score_4': [0], '11_5': [0], 'crowd_6': [1], '10000_7': [1]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['melbourne', '11.12 ( 78 )', 'south melbourne', '18.14 ( 122 )', 'mcg', '19086', '6 july 1935'], ['footscray', '15.13 ( 103 )', 'geelong', '16.7 ( 103 )', 'western oval', '11000', '6 july 1935'], ['collingwood', '17.20 ( 122 )', 'hawthorn', '12.3 ( 75 )', 'victoria park', '8000', '6 july 1935'], ['carlton', '19.21 ( 135 )', 'fitzroy', '8.6 ( 54 )', 'princes park', '24000', '6 july 1935'], ['st kilda', '10.14 ( 74 )', 'richmond', '7.8 ( 50 )', 'junction oval', '20000', '6 july 1935'], ['north melbourne', '10.13 ( 73 )', 'essendon', '10.14 ( 74 )', 'arden street oval', '8000', '6 july 1935']]