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damage to major ships at the battle of jutland | https://en.wikipedia.org/wiki/Damage_to_major_ships_at_the_Battle_of_Jutland | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15968208-6.html.csv | superlative | the ship könig sustained the highest number of 13.5-inch / 1400lb damages to german ships in the battle . | {'scope': 'all', 'col_superlative': '2', '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', '13.5 - inch / 1400lb'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; 13.5 - inch / 1400lb }'}, 'ship'], 'result': 'könig', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; 13.5 - inch / 1400lb } ; ship }'}, 'könig'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; 13.5 - inch / 1400lb } ; ship } ; könig } = true', 'tointer': 'select the row whose 13.5 - inch / 1400lb record of all rows is maximum . the ship record of this row is könig .'} | eq { hop { argmax { all_rows ; 13.5 - inch / 1400lb } ; ship } ; könig } = true | select the row whose 13.5 - inch / 1400lb record of all rows is maximum . the ship record of this row is könig . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, '13.5 - inch / 1400lb_5': 5, 'ship_6': 6, 'könig_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', '13.5 - inch / 1400lb_5': '13.5 - inch / 1400lb', 'ship_6': 'ship', 'könig_7': 'könig'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], '13.5 - inch / 1400lb_5': [0], 'ship_6': [1], 'könig_7': [2]} | ['ship', '13.5 - inch / 1400lb', '13.5 - inch / 1250lb', '12 - inch', 'total'] | [['lützow', '0', '2', '8', '10'], ['derfflinger', '0', '0', '3', '3'], ['seydlitz', '0', '0', '1', '1'], ['könig', '7', '1', '0', '8'], ['markgraf', '0', '1', '0', '1'], ['total', '7', '4', '12', '23']] |
sébastien bourdais | https://en.wikipedia.org/wiki/S%C3%A9bastien_Bourdais | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1019053-2.html.csv | unique | the 2003 race was the only time sebastian bourdais came in fourth place . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '4th', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rank', '4th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rank record fuzzily matches to 4th .', 'tostr': 'filter_eq { all_rows ; rank ; 4th }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; rank ; 4th } }', 'tointer': 'select the rows whose rank record fuzzily matches to 4th . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rank', '4th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rank record fuzzily matches to 4th .', 'tostr': 'filter_eq { all_rows ; rank ; 4th }'}, 'year'], 'result': '2003', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; rank ; 4th } ; year }'}, '2003'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; rank ; 4th } ; year } ; 2003 }', 'tointer': 'the year record of this unqiue row is 2003 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; rank ; 4th } } ; eq { hop { filter_eq { all_rows ; rank ; 4th } ; year } ; 2003 } } = true', 'tointer': 'select the rows whose rank record fuzzily matches to 4th . there is only one such row in the table . the year record of this unqiue row is 2003 .'} | and { only { filter_eq { all_rows ; rank ; 4th } } ; eq { hop { filter_eq { all_rows ; rank ; 4th } ; year } ; 2003 } } = true | select the rows whose rank record fuzzily matches to 4th . there is only one such row in the table . the year record of this unqiue row is 2003 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'rank_7': 7, '4th_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '2003_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'rank_7': 'rank', '4th_8': '4th', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '2003_10': '2003'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'rank_7': [0], '4th_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '2003_10': [3]} | ['year', 'team', 'chassis', 'engine', 'rank', 'points'] | [['2003', 'newman / haas racing', 'lola b02 / 00', 'ford xfe', '4th', '159'], ['2004', 'newman / haas racing', 'lola b02 / 00', 'ford xfe', '1st', '369'], ['2005', 'newman / haas racing', 'lola b02 / 00', 'ford xfe', '1st', '348'], ['2006', 'newman / haas racing', 'lola b02 / 00', 'ford xfe', '1st', '387'], ['2007', 'newman / haas / lanigan racing', 'panoz dp01', 'cosworth xfe', '1st', '364']] |
intel | https://en.wikipedia.org/wiki/Intel | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14617-1.html.csv | majority | the majority of company acquisitions by intel corporation were used as or integrated with software . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'software', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'used as or integrated with', 'software'], 'result': True, 'ind': 0, 'tointer': 'for the used as or integrated with records of all rows , most of them fuzzily match to software .', 'tostr': 'most_eq { all_rows ; used as or integrated with ; software } = true'} | most_eq { all_rows ; used as or integrated with ; software } = true | for the used as or integrated with records of all rows , most of them fuzzily match to software . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'used as or integrated with_3': 3, 'software_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'used as or integrated with_3': 'used as or integrated with', 'software_4': 'software'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'used as or integrated with_3': [0], 'software_4': [0]} | ['number', 'company', 'business', 'country', 'price', 'used as or integrated with'] | [['1', 'mcafee', 'security', 'usa', '7.6 b', 'software'], ['2', 'infineon', 'wireless', 'germany', '1.4 b', 'mobile cpus'], ['3', 'telmap', 'software', 'israel', 'n / a', 'location services'], ['4', 'mashery', 'cloud software', 'usa', '180 m', 'software'], ['5', 'aepona', 'sdn', 'ireland', 'n / a', 'software'], ['6', 'stonesoft', 'security', 'finland', '389 m', 'software'], ['7', 'omek interactive', 'gesture', 'israel', 'n / a', 'software'], ['7', 'indysis', 'natural language processing', 'spain', 'n / a', 'software']] |
golf magazine | https://en.wikipedia.org/wiki/Golf_Magazine | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11063491-1.html.csv | majority | most of the courses were designed before 1950 . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1950', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'designer , year', '1950'], 'result': True, 'ind': 0, 'tointer': 'for the designer , year records of all rows , most of them are less than 1950 .', 'tostr': 'most_less { all_rows ; designer , year ; 1950 } = true'} | most_less { all_rows ; designer , year ; 1950 } = true | for the designer , year records of all rows , most of them are less than 1950 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'designer , year_3': 3, '1950_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'designer , year_3': 'designer , year', '1950_4': '1950'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'designer , year_3': [0], '1950_4': [0]} | ['rank', 'name', 'location', 'state', 'designer , year'] | [['1', 'pine valley', 'pine valley', 'new jersey', 'george crump / harry colt , 1918'], ['2', 'cypress point', 'pebble beach', 'california', 'alister mackenzie , 1918'], ['3', 'augusta national', 'augusta', 'georgia', 'alister mackenzie / bobby jones , 1933'], ['4', 'pebble beach', 'pebble beach', 'california', 'jack neville / douglas grant , 1919'], ['5', 'shinnecock hills', 'southampton', 'new york', 'william flynn , 1931'], ['6', 'oakmont', 'oakmont', 'pennsylvania', 'henry fownes , 1903'], ['7', 'merion ( east )', 'ardmore', 'pennsylvania', 'hugh wilson , 1912'], ['8', 'sand hills', 'mullen', 'nebraska', 'bill coore / ben crenshaw , 1994'], ['9', 'pacific dunes', 'bandon', 'oregon', 'tom doak , 2001'], ['10', 'national golf links of america', 'southampton', 'new york', 'charles b macdonald , 1911']] |
miss world | https://en.wikipedia.org/wiki/Miss_World | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-150343-1.html.csv | unique | only one of the miss world competitions took place in poland . | {'scope': 'all', 'row': '8', 'col': '5', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': 'poland', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'poland'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to poland .', 'tostr': 'filter_eq { all_rows ; location ; poland }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; location ; poland } } = true', 'tointer': 'select the rows whose location record fuzzily matches to poland . there is only one such row in the table .'} | only { filter_eq { all_rows ; location ; poland } } = true | select the rows whose location record fuzzily matches to poland . 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, 'location_4': 4, 'poland_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'location_4': 'location', 'poland_5': 'poland'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'location_4': [0], 'poland_5': [0]} | ['year', 'country / territory', 'miss world', 'national title', 'location'] | [['2013', 'philippines', 'megan young', 'miss world philippines', 'bali , indonesia'], ['2012', 'china pr', 'yu wenxia', 'miss china world', 'ordos city , china'], ['2011', 'venezuela', 'ivian sarcos', 'miss venezuela world', 'london , united kingdom'], ['2010', 'united states', 'alexandria mills', 'miss world united states', 'sanya , china'], ['2009', 'gibraltar', 'kaiane aldorino', 'miss gibraltar', 'johannesburg , south africa'], ['2008', 'russia', 'ksenia sukhinova', 'miss russia', 'johannesburg , south africa'], ['2007', 'china pr', 'zhang zilin', 'miss china world', 'sanya , china'], ['2006', 'czech republic', 'taťána kuchařová', 'miss czech republic', 'warsaw , poland'], ['2005', 'iceland', 'unnur birna vilhjálmsdóttir', 'ungfrú ísland', 'sanya , china'], ['2004', 'peru', 'maría julia mantilla', 'miss world perú', 'sanya , china'], ['2003', 'ireland', 'rosanna davison', 'miss ireland', 'sanya , china'], ['2002', 'turkey', 'azra akın', 'miss turkey', 'london , united kingdom'], ['2001', 'nigeria', 'agbani darego', 'most beautiful girl in nigeria', 'sun city , south africa'], ['2000', 'india', 'priyanka chopra', 'femina miss india', 'london , united kingdom']] |
vivian girls | https://en.wikipedia.org/wiki/Vivian_Girls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18710512-3.html.csv | unique | the single titled ' moped girls ' is the only single that vivian girls released with for us record label . | {'scope': 'all', 'row': '5', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': 'for us', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'record label', 'for us'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose record label record fuzzily matches to for us .', 'tostr': 'filter_eq { all_rows ; record label ; for us }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; record label ; for us } }', 'tointer': 'select the rows whose record label record fuzzily matches to for us . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'record label', 'for us'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose record label record fuzzily matches to for us .', 'tostr': 'filter_eq { all_rows ; record label ; for us }'}, 'single'], 'result': 'moped girls', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; record label ; for us } ; single }'}, 'moped girls'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; record label ; for us } ; single } ; moped girls }', 'tointer': 'the single record of this unqiue row is moped girls .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; record label ; for us } } ; eq { hop { filter_eq { all_rows ; record label ; for us } ; single } ; moped girls } } = true', 'tointer': 'select the rows whose record label record fuzzily matches to for us . there is only one such row in the table . the single record of this unqiue row is moped girls .'} | and { only { filter_eq { all_rows ; record label ; for us } } ; eq { hop { filter_eq { all_rows ; record label ; for us } ; single } ; moped girls } } = true | select the rows whose record label record fuzzily matches to for us . there is only one such row in the table . the single record of this unqiue row is moped girls . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'record label_7': 7, 'for us_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'single_9': 9, 'moped girls_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'record label_7': 'record label', 'for us_8': 'for us', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'single_9': 'single', 'moped girls_10': 'moped girls'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'record label_7': [0], 'for us_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'single_9': [2], 'moped girls_10': [3]} | ['date', 'single', 'backed with', 'record label', 'format', 'other details'] | [['2008', 'wild eyes', 'my baby wants me dead', 'plays with dolls / wild world', '7 single', '4000 copies'], ['2008', 'tell the world', 'i believe in nothing & damaged', 'woodsist', '7 single', '3000 copies'], ['2008', "i ca n't stay", 'blind spot', 'in the red', '7 single', '2000 copies'], ['2008', 'surfin away & second date', "girl do n't tell me ( wilson )", 'wild world', '7 single', '1000 copies'], ['2009', 'moped girls', 'death', 'for us', '7 single', '1500 copies'], ['2010', 'my love will follow me', "he 's gone ( the chantels cover )", 'wild world', '7 single', '2000 copies']] |
best international athlete espy award | https://en.wikipedia.org/wiki/Best_International_Athlete_ESPY_Award | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10587252-1.html.csv | comparative | albert pujols won the best international athlete espy award before usain bolt did . | {'row_1': '1', 'row_2': '4', 'col': '1', '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', 'sportsperson', 'albert pujols'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sportsperson record fuzzily matches to albert pujols .', 'tostr': 'filter_eq { all_rows ; sportsperson ; albert pujols }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; sportsperson ; albert pujols } ; year }', 'tointer': 'select the rows whose sportsperson record fuzzily matches to albert pujols . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sportsperson', 'usain bolt'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose sportsperson record fuzzily matches to usain bolt .', 'tostr': 'filter_eq { all_rows ; sportsperson ; usain bolt }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; sportsperson ; usain bolt } ; year }', 'tointer': 'select the rows whose sportsperson record fuzzily matches to usain bolt . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; sportsperson ; albert pujols } ; year } ; hop { filter_eq { all_rows ; sportsperson ; usain bolt } ; year } } = true', 'tointer': 'select the rows whose sportsperson record fuzzily matches to albert pujols . take the year record of this row . select the rows whose sportsperson record fuzzily matches to usain bolt . take the year record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; sportsperson ; albert pujols } ; year } ; hop { filter_eq { all_rows ; sportsperson ; usain bolt } ; year } } = true | select the rows whose sportsperson record fuzzily matches to albert pujols . take the year record of this row . select the rows whose sportsperson record fuzzily matches to usain bolt . take the year 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, 'sportsperson_7': 7, 'albert pujols_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'sportsperson_11': 11, 'usain bolt_12': 12, 'year_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', 'sportsperson_7': 'sportsperson', 'albert pujols_8': 'albert pujols', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'sportsperson_11': 'sportsperson', 'usain bolt_12': 'usain bolt', 'year_13': 'year'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'sportsperson_7': [0], 'albert pujols_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'sportsperson_11': [1], 'usain bolt_12': [1], 'year_13': [3]} | ['year', 'sportsperson', 'nation of birth', 'team', 'competition , federation , or league', 'sport'] | [['2006', 'albert pujols', 'dominican republic', 'st louis cardinals', 'major league baseball', 'baseball'], ['2007', 'roger federer', 'switzerland', 'not applicable', 'atp tour', 'tennis'], ['2008', 'lorena ochoa', 'mexico', 'not applicable', 'lpga tour', 'golf'], ['2009', 'usain bolt', 'jamaica', 'not applicable', 'not applicable', 'athletics'], ['2012', 'lionel messi', 'argentina', 'fc barcelona / argentina', 'la liga / fifa / uefa / afa', 'soccer']] |
united states house of representatives elections , 1994 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1994 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341522-36.html.csv | count | 3 incumbents were re - elected during the 1994 united states house of representatives elections . | {'scope': 'all', 'criterion': 'equal', 'value': 're - elected', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'status', 're - elected'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose status record fuzzily matches to re - elected .', 'tostr': 'filter_eq { all_rows ; status ; re - elected }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; status ; re - elected } }', 'tointer': 'select the rows whose status record fuzzily matches to re - elected . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; status ; re - elected } } ; 3 } = true', 'tointer': 'select the rows whose status record fuzzily matches to re - elected . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; status ; re - elected } } ; 3 } = true | select the rows whose status record fuzzily matches to re - elected . 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, 'status_5': 5, 're - elected_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', 'status_5': 'status', 're - elected_6': 're - elected', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'status_5': [0], 're - elected_6': [0], '3_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'status', 'opponent'] | [['north carolina1', 'eva m clayton', 'democratic', '1992', 're - elected', 'eva m clayton ( d ) 61.1 % ted tyler ( r ) 38.9 %'], ['north carolina4', 'david price', 'democratic', '1986', 'defeated republican gain', 'fred heineman ( r ) 50.4 % david price ( d ) 49.6 %'], ['north carolina5', 'stephen l neal', 'democratic', '1974', 'retired republican gain', 'richard burr ( r ) 57.3 % a p sands ( d ) 42.7 %'], ['north carolina6', 'howard coble', 'republican', '1984', 're - elected', 'howard coble ( r ) unopposed'], ['north carolina8', 'bill hefner', 'democratic', '1974', 're - elected', 'bill hefner ( d ) 52.4 % sherrill morgan ( r ) 47.6 %'], ['north carolina9', 'alex mcmillan', 'republican', '1984', 'retired republican hold', 'sue wilkins myrick ( r ) 65.0 % rory blake ( d ) 35.0 %']] |
1996 pga championship | https://en.wikipedia.org/wiki/1996_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18096431-5.html.csv | superlative | in the 1996 pga championship , phil michelson had the best score . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; score }'}, 'player'], 'result': 'phil mickelson', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; score } ; player }'}, 'phil mickelson'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; score } ; player } ; phil mickelson } = true', 'tointer': 'select the row whose score record of all rows is minimum . the player record of this row is phil mickelson .'} | eq { hop { argmin { all_rows ; score } ; player } ; phil mickelson } = true | select the row whose score record of all rows is minimum . the player record of this row is phil mickelson . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'score_5': 5, 'player_6': 6, 'phil mickelson_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'score_5': 'score', 'player_6': 'player', 'phil mickelson_7': 'phil mickelson'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'score_5': [0], 'player_6': [1], 'phil mickelson_7': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'phil mickelson', 'united states', '67 + 67 = 134', '- 10'], ['2', 'justin leonard', 'united states', '71 + 66 = 137', '- 7'], ['t3', 'mark brooks', 'united states', '68 + 70 = 138', '- 6'], ['t3', 'kenny perry', 'united states', '66 + 72 = 138', '- 6'], ['t3', 'vijay singh', 'fiji', '69 + 69 = 138', '- 6'], ['t6', 'lee janzen', 'united states', '68 + 71 = 139', '- 5'], ['t6', 'nick price', 'zimbabwe', '68 + 72 = 139', '- 5'], ['t8', 'mike brisky', 'united states', '71 + 69 = 140', '- 4'], ['t8', 'russ cochran', 'united states', '68 + 72 = 140', '- 4'], ['t8', 'david edwards', 'united states', '69 + 71 = 140', '- 4'], ['t8', 'brad faxon', 'united states', '72 + 68 = 140', '- 4'], ['t8', 'jim furyk', 'united states', '70 + 70 = 140', '- 4'], ['t8', 'greg norman', 'australia', '68 + 72 = 140', '- 4'], ['t8', 'jesper parnevik', 'sweden', '73 + 67 = 140', '- 4'], ['t8', 'tommy tolles', 'united states', '69 + 71 = 140', '- 4'], ['t8', 'tom watson', 'united states', '69 + 71 = 140', '- 4'], ['t8', 'ian woosnam', 'wales', '68 + 72 = 140', '- 4']] |
list of bored to death episodes | https://en.wikipedia.org/wiki/List_of_Bored_to_Death_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26136228-3.html.csv | superlative | for the show bored to death , the highest number of us viewers was for the episode titled " escape from the castle ! " . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'us viewers ( millions )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; us viewers ( millions ) }'}, 'title'], 'result': 'escape from the castle !', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; us viewers ( millions ) } ; title }'}, 'escape from the castle !'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; us viewers ( millions ) } ; title } ; escape from the castle ! } = true', 'tointer': 'select the row whose us viewers ( millions ) record of all rows is maximum . the title record of this row is escape from the castle ! .'} | eq { hop { argmax { all_rows ; us viewers ( millions ) } ; title } ; escape from the castle ! } = true | select the row whose us viewers ( millions ) record of all rows is maximum . the title record of this row is escape from the castle ! . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'us viewers (millions)_5': 5, 'title_6': 6, 'escape from the castle!_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'us viewers (millions)_5': 'us viewers ( millions )', 'title_6': 'title', 'escape from the castle!_7': 'escape from the castle !'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'us viewers (millions)_5': [0], 'title_6': [1], 'escape from the castle!_7': [2]} | ['series no', 'episode no', 'title', 'directed by', 'written by', 'original air date', 'us viewers ( millions )'] | [['9', '1', 'escape from the dungeon !', 'alan taylor', 'jonathan ames', 'september 26 , 2010', '1.05'], ['10', '2', 'make it quick , fitzgerald !', 'alan taylor', 'jonathan ames', 'october 3 , 2010', '1.08'], ['11', '3', 'the gowanus canal has gonorrhea !', 'michael lehmann', 'martin gero & jonathan ames', 'october 10 , 2010', '0.86'], ['12', '4', "i 've been living like a demented god !", 'michael lehmann', 'donick cary & jonathan ames', 'october 17 , 2010', '0.82'], ['13', '5', 'forty - two down !', 'troy miller', 'tami sagher & jonathan ames', 'october 24 , 2010', '1.01'], ['14', '6', 'the case of the grievous clerical error !', 'tristram shapeero', 'sam sklaver & jonathan ames', 'october 31 , 2010', '0.69'], ['15', '7', 'escape from the castle !', 'adam bernstein', 'donick cary & jonathan ames', 'november 7 , 2010', '1.10']] |
carla suárez navarro | https://en.wikipedia.org/wiki/Carla_Su%C3%A1rez_Navarro | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15551996-3.html.csv | unique | the tournmanet on may 4 , 2013 was the only tournment were anastasia pavlyuchenkova was the opponent . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'anastasia pavlyuchenkova', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'anastasia pavlyuchenkova'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to anastasia pavlyuchenkova .', 'tostr': 'filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } }', 'tointer': 'select the rows whose opponent record fuzzily matches to anastasia pavlyuchenkova . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'anastasia pavlyuchenkova'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to anastasia pavlyuchenkova .', 'tostr': 'filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova }'}, 'date'], 'result': 'may 4 , 2013', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } ; date }'}, 'may 4 , 2013'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } ; date } ; may 4 , 2013 }', 'tointer': 'the date record of this unqiue row is may 4 , 2013 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } } ; eq { hop { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } ; date } ; may 4 , 2013 } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to anastasia pavlyuchenkova . there is only one such row in the table . the date record of this unqiue row is may 4 , 2013 .'} | and { only { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } } ; eq { hop { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } ; date } ; may 4 , 2013 } } = true | select the rows whose opponent record fuzzily matches to anastasia pavlyuchenkova . there is only one such row in the table . the date record of this unqiue row is may 4 , 2013 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'anastasia pavlyuchenkova_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'may 4 , 2013_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'anastasia pavlyuchenkova_8': 'anastasia pavlyuchenkova', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'may 4 , 2013_10': 'may 4 , 2013'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'opponent_7': [0], 'anastasia pavlyuchenkova_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'may 4 , 2013_10': [3]} | ['outcome', 'date', 'tournament', 'surface', 'opponent', 'score'] | [['runner - up', 'april 12 , 2009', 'andalucia tennis experience , marbella , spain', 'clay', 'jelena janković', '3 - 6 , 6 - 3 , 3 - 6'], ['runner - up', 'april 11 , 2010', 'andalucia tennis experience , marbella , spain', 'clay', 'flavia pennetta', '2 - 6 , 6 - 4 , 3 - 6'], ['runner - up', 'may 5 , 2012', 'estoril open , estoril , portugal', 'clay', 'kaia kanepi', '6 - 3 , 6 - 7 ( 6 - 8 ) , 4 - 6'], ['runner - up', 'march 2 , 2013', 'abierto mexicano telcel , acapulco , mexico', 'clay', 'sara errani', '0 - 6 , 4 - 6'], ['runner - up', 'may 4 , 2013', 'portugal open , oeiras , portugal', 'clay', 'anastasia pavlyuchenkova', '5 - 7 , 2 - 6']] |
mori no asagao | https://en.wikipedia.org/wiki/Mori_no_Asagao | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29039942-1.html.csv | aggregation | the average ratings that mori no asagao had was 3.78 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '3.78', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'ratings ( kanto )'], 'result': '3.78', 'ind': 0, 'tostr': 'avg { all_rows ; ratings ( kanto ) }'}, '3.78'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; ratings ( kanto ) } ; 3.78 } = true', 'tointer': 'the average of the ratings ( kanto ) record of all rows is 3.78 .'} | round_eq { avg { all_rows ; ratings ( kanto ) } ; 3.78 } = true | the average of the ratings ( kanto ) record of all rows is 3.78 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'ratings (kanto)_4': 4, '3.78_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'ratings (kanto)_4': 'ratings ( kanto )', '3.78_5': '3.78'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'ratings (kanto)_4': [0], '3.78_5': [1]} | ['episode', 'title', 'writer', 'director', 'original airdate', 'ratings ( kanto )'] | [['2', 'instruction execution ( 死刑執行命令 )', 'daisuke habara', 'akimitsu sasaki', 'oct 25 , 2010 22.00 - 22.54', '3.8'], ['3', 'give flowers to the condemned ( 死刑囚へ贈る花 )', 'shizuka oki', 'makito murakami', 'nov 1 , 2010 22.00 - 22.54', '4.6'], ['4', 'wedding bride prison ( 獄中結婚の花嫁 )', 'daisuke habara', 'makito murakami', 'nov 8 , 2010 22.00 - 22.54', '4.3'], ['6', 'gray man 33 years of false accusation ( 冤罪33年の白髪男 )', 'daisuke habara', 'munenobu yamauchi', 'nov 22 , 2010 22.00 - 22.54', '3.2'], ['8', 'visits last miracle ( 最期の面会の奇跡 )', 'shizuka oki', 'tomoyuki furumaya', 'dec 6 , 2010 22.00 - 22.54', '3.0']] |
norway in the eurovision song contest 1999 | https://en.wikipedia.org/wiki/Norway_in_the_Eurovision_Song_Contest_1999 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11437346-1.html.csv | superlative | in norway in the eurovision song contest 1999 , the artist that performed living my life without you received the highest points . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'song'], 'result': 'living my life without you', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; song }'}, 'living my life without you'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; song } ; living my life without you } = true', 'tointer': 'select the row whose points record of all rows is maximum . the song record of this row is living my life without you .'} | eq { hop { argmax { all_rows ; points } ; song } ; living my life without you } = true | select the row whose points record of all rows is maximum . the song record of this row is living my life without you . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'song_6': 6, 'living my life without you_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'song_6': 'song', 'living my life without you_7': 'living my life without you'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'song_6': [1], 'living my life without you_7': [2]} | ['draw', 'artist', 'song', 'points', 'place'] | [['1', 'hãvard , dag arnold and ingvild gryting', "i 'll be your friend", '14', '6'], ['2', 'mette hartmann', 'the night before the morning after', '17', '5'], ['3', 'dag brandth', 'untold', '7', '8'], ['4', 'stephen ackles', 'lost again', '19', '4'], ['5', 'midnight sons', 'stay', '35', '2'], ['6', 'tor endresen', 'lover', '28', '3'], ['7', 'toril moe', 'you used to be mine', '14', '6'], ['8', 'stig van eijk', 'living my life without you', '62', '1']] |
kumar sanu | https://en.wikipedia.org/wiki/Kumar_Sanu | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1368369-1.html.csv | majority | all of kumar sanu 's songs were released in the decade of the 1990s . | {'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'fuzzily_match', 'value': '199', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'year', '199'], 'result': True, 'ind': 0, 'tointer': 'for the year records of all rows , all of them fuzzily match to 199 .', 'tostr': 'all_eq { all_rows ; year ; 199 } = true'} | all_eq { all_rows ; year ; 199 } = true | for the year records of all rows , all of them fuzzily match to 199 . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year_3': 3, '199_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year_3': 'year', '199_4': '199'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'year_3': [0], '199_4': [0]} | ['year', 'song', 'film', 'music director ( s )', 'lyricist'] | [['1991', 'ab tere bin', 'aashiqui', 'nadeem - shravan', 'sameer'], ['1992', 'mera dil bhi kitna pagal hai', 'saajan', 'nadeem - shravan', 'sameer'], ['1993', 'sochenge tumhe pyaar', 'deewana', 'nadeem - shravan', 'sameer'], ['1994', 'yeh kaali kaali aankhen', 'baazigar', 'anu malik', 'rani malik'], ['1995', 'ek ladki ko dekha', '1942 : a love story', 'rd burman', 'javed akhtar']] |
list of best - selling music artists | https://en.wikipedia.org/wiki/List_of_best-selling_music_artists | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1291598-3.html.csv | superlative | the release year of frank sinatra 's first charted record is earliest release year among all best - selling music artist 's records . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'release - year of first charted record'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; release - year of first charted record }'}, 'artist'], 'result': 'frank sinatra', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; release - year of first charted record } ; artist }'}, 'frank sinatra'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; release - year of first charted record } ; artist } ; frank sinatra } = true', 'tointer': 'select the row whose release - year of first charted record record of all rows is minimum . the artist record of this row is frank sinatra .'} | eq { hop { argmin { all_rows ; release - year of first charted record } ; artist } ; frank sinatra } = true | select the row whose release - year of first charted record record of all rows is minimum . the artist record of this row is frank sinatra . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'release - year of first charted record_5': 5, 'artist_6': 6, 'frank sinatra_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'release - year of first charted record_5': 'release - year of first charted record', 'artist_6': 'artist', 'frank sinatra_7': 'frank sinatra'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'release - year of first charted record_5': [0], 'artist_6': [1], 'frank sinatra_7': [2]} | ['artist', 'country of origin', 'period active', 'release - year of first charted record', 'genre', 'claimed sales'] | [['eagles', 'united states', '1971 - present', '1972', 'soft rock / country rock', '150 million'], ['rihanna', 'barbados united states', '2005 - present', '2005', 'r & b / pop / dance / hip - hop', '150 million'], ['u2', 'ireland', '1976 - present', '1980', 'rock', '150 million'], ['billy joel', 'united states', '1964 - present', '1971', 'pop / rock', '150 million'], ['phil collins', 'united kingdom', '1980 - 2011', '1981', 'adult contemporary', '150 million'], ['aerosmith', 'united states', '1970 - present', '1973', 'hard rock', '150 million'], ['frank sinatra', 'united states', '1935 - 1995', '1939', 'pop / swing', '150 million'], ['barbra streisand', 'united states', '1960 - present', '1963', 'pop / adult contemporary', '145 million'], ['garth brooks', 'united states', '1989 - present', '1989', 'country', '130 million'], ['genesis', 'united kingdom', '1967 - 1999 2006 - 2011', '1969', 'progressive rock / pop rock', '130 million'], ['donna summer', 'united states', '1968 - 2012', '1974', 'pop / disco / r & b', '130 million'], ['neil diamond', 'united states', '1966 - present', '1966', 'pop / rock', '125 million'], ['bruce springsteen', 'united states', '1972 - present', '1973', 'rock', '120 million'], ['bee gees', 'united kingdom', '1963 - 2003 2009 - 2012', '1963', 'pop / disco', '120 million'], ['julio iglesias', 'spain', '1968 - present', '1968', 'latin', '120 million'], ['dire straits', 'united kingdom', '1977 - 1995', '1978', 'rock / pop', '120 million']] |
1971 isle of man tt | https://en.wikipedia.org/wiki/1971_Isle_of_Man_TT | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10638654-3.html.csv | aggregation | riders in the 1971 isle of man tt went on average 87.75 mph . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '87.75', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'speed'], 'result': '87.75', 'ind': 0, 'tostr': 'avg { all_rows ; speed }'}, '87.75'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; speed } ; 87.75 } = true', 'tointer': 'the average of the speed record of all rows is 87.75 .'} | round_eq { avg { all_rows ; speed } ; 87.75 } = true | the average of the speed record of all rows is 87.75 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'speed_4': 4, '87.75_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'speed_4': 'speed', '87.75_5': '87.75'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'speed_4': [0], '87.75_5': [1]} | ['place', 'rider', 'country', 'machine', 'speed', 'time', 'points'] | [['1', 'tony jefferies', 'united kingdom', 'yamsel', '89.81 mph', '2:05.48.6', '15'], ['2', 'gordon pantall', 'united kingdom', 'yamaha', '89.55 mph', '2:06.25.0', '12'], ['3', 'bill smith', 'united kingdom', 'honda', '89.81 mph', '2:07.40.8', '10'], ['4', 'john williams', 'united kingdom', 'ajs', '88.94 mph', '2:07.17.0', '8'], ['5', 'mick chatterton', 'united kingdom', 'yamaha', '87.38 mph', '2:09.33.6', '6'], ['6', 'gerry mateer', 'united kingdom', 'aermacchi', '87.18 mph', '2:09.51.8', '5'], ['7', 'mick grant', 'united kingdom', 'yamaha', '86.50 mph', '2:28.30.6', '4'], ['8', 'billy guthrie', 'united kingdom', 'yamaha', '86.31 mph', '2:11.09.0', '3'], ['9', 'gã ¼ nter bartusch', 'east germany', 'mz', '86.15 mph', '2:11.24.2', '2'], ['10', 'peter berwick', 'united kingdom', 'suzuki', '85.90 mph', '2:11.47.2', '1']] |
alex caffi | https://en.wikipedia.org/wiki/Alex_Caffi | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1218386-1.html.csv | comparative | alex caffi scored more points in 1989 than he did in 1992 . | {'row_1': '6', 'row_2': '12', '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', 'year', '1989'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1989 .', 'tostr': 'filter_eq { all_rows ; year ; 1989 }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1989 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1989 . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1992'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1992 .', 'tostr': 'filter_eq { all_rows ; year ; 1992 }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1992 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1992 . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 1989 } ; points } ; hop { filter_eq { all_rows ; year ; 1992 } ; points } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1989 . take the points record of this row . select the rows whose year record fuzzily matches to 1992 . take the points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; year ; 1989 } ; points } ; hop { filter_eq { all_rows ; year ; 1992 } ; points } } = true | select the rows whose year record fuzzily matches to 1989 . take the points record of this row . select the rows whose year record fuzzily matches to 1992 . take the points 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, 'year_7': 7, '1989_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1992_12': 12, 'points_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', 'year_7': 'year', '1989_8': '1989', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1992_12': '1992', 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1989_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1992_12': [1], 'points_13': [3]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1986', 'osella squadra corse', 'osella fa1 g', 'alfa romeo v8', '0'], ['1987', 'osella squadra corse', 'osella fa1i', 'alfa romeo v8', '0'], ['1987', 'osella squadra corse', 'osella fa1 g', 'alfa romeo v8', '0'], ['1988', 'scuderia italia', 'dallara 3087', 'cosworth v8', '0'], ['1988', 'scuderia italia', 'dallara 188', 'cosworth v8', '0'], ['1989', 'scuderia italia', 'dallara 189', 'cosworth v8', '4'], ['1990', 'footwork arrows racing', 'arrows a11b', 'cosworth v8', '2'], ['1991', 'footwork grand prix international', 'footwork a11c', 'porsche v12', '0'], ['1991', 'footwork grand prix international', 'footwork fa12', 'porsche v12', '0'], ['1991', 'footwork grand prix international', 'footwork fa12c', 'cosworth v8', '0'], ['1992', 'andrea moda formula', 'coloni c4b', 'judd v10', '0'], ['1992', 'andrea moda formula', 'moda s921', 'judd v10', '0']] |
ace ( tennis ) | https://en.wikipedia.org/wiki/Ace_%28tennis%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1612222-1.html.csv | count | among tennis events with a record number of aces , two of the events completed in less than 5 sets were played at wimbledon . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'wimbledon', 'result': '2', 'col': '4', 'subset': {'col': '5', 'criterion': 'less_than', 'value': '5'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'sets', '5'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; sets ; 5 }', 'tointer': 'select the rows whose sets record is less than 5 .'}, 'event', 'wimbledon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose sets record is less than 5 . among these rows , select the rows whose event record fuzzily matches to wimbledon .', 'tostr': 'filter_eq { filter_less { all_rows ; sets ; 5 } ; event ; wimbledon }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_less { all_rows ; sets ; 5 } ; event ; wimbledon } }', 'tointer': 'select the rows whose sets record is less than 5 . among these rows , select the rows whose event record fuzzily matches to wimbledon . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_less { all_rows ; sets ; 5 } ; event ; wimbledon } } ; 2 } = true', 'tointer': 'select the rows whose sets record is less than 5 . among these rows , select the rows whose event record fuzzily matches to wimbledon . the number of such rows is 2 .'} | eq { count { filter_eq { filter_less { all_rows ; sets ; 5 } ; event ; wimbledon } } ; 2 } = true | select the rows whose sets record is less than 5 . among these rows , select the rows whose event record fuzzily matches to wimbledon . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'sets_6': 6, '5_7': 7, 'event_8': 8, 'wimbledon_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'sets_6': 'sets', '5_7': '5', 'event_8': 'event', 'wimbledon_9': 'wimbledon', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'sets_6': [0], '5_7': [0], 'event_8': [1], 'wimbledon_9': [1], '2_10': [3]} | ['aces', 'player', 'opponent', 'event', 'sets'] | [['113', 'john isner', 'nicolas mahut', '2010 wimbledon', '5'], ['103', 'nicolas mahut', 'john isner', '2010 wimbledon', '5'], ['78', 'ivo karlović', 'radek štěpánek', '2009 davis cup', '5'], ['55', 'ivo karlović', 'lleyton hewitt', '2009 roland garros', '5'], ['54', 'gary muller', 'peter lundgren', '1993 wimbledon', '3'], ['51', 'ivo karlović', 'daniele bracciali', '2005 wimbledon', '5'], ['51', 'joachim johansson', 'andre agassi', '2005 australian open', '4'], ['50', 'roger federer', 'andy roddick', '2009 wimbledon', '5'], ['50', 'chris guccione', 'olivier patience', '2005 wimbledon', '3'], ['50', 'grégory carraz', 'tomáš zíb', '2004 andrézieux challenger', '3'], ['49', 'richard krajicek', 'yevgeny kafelnikov', '1999 us open', '5'], ['48', 'marc rosset', 'arnaud clément', '2001 davis cup', '5'], ['48', 'ivo karlović', 'ivan dodig', '2011 australian open', '5'], ['48', 'nicolás almagro', 'olivier rochus', '2012 wimbledon', '5']] |
irrigation in bolivia | https://en.wikipedia.org/wiki/Irrigation_in_Bolivia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17118006-1.html.csv | superlative | of these departments , cochabamba has the highest amount of irrigation . | {'scope': 'all', 'col_superlative': '6', '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', 'total'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total }'}, 'department'], 'result': 'cochabamba', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total } ; department }'}, 'cochabamba'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total } ; department } ; cochabamba } = true', 'tointer': 'select the row whose total record of all rows is maximum . the department record of this row is cochabamba .'} | eq { hop { argmax { all_rows ; total } ; department } ; cochabamba } = true | select the row whose total record of all rows is maximum . the department record of this row is cochabamba . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, 'department_6': 6, 'cochabamba_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'total_5': 'total', 'department_6': 'department', 'cochabamba_7': 'cochabamba'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], 'department_6': [1], 'cochabamba_7': [2]} | ['department', 'micro ( 10ha )', 'small ( 100ha )', 'medium ( 500ha )', 'big ( > 500ha )', 'total'] | [['chuquisaca', '1653', '11370', '4261', '3884', '21168'], ['cochabamba', '1938', '22225', '27403', '35968', '81925'], ['la paz', '1703', '21047', '6052', '7192', '35994'], ['oruro', '940', '3638', '440', '9021', '14039'], ['potosã\xad', '3240', '10146', '2254', '600', '16240'], ['santa cruz', '269', '5456', '8434', '1080', '15239'], ['tarija', '785', '12755', '17101', '5710', '36351'], ['total', '10528', '86638', '65944', '63454', '226564']] |
1966 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1966_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17277136-5.html.csv | majority | in the 1996 u.s. open , all of the players are from the united states . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , all of them fuzzily match to united states .', 'tostr': 'all_eq { all_rows ; country ; united states } = true'} | all_eq { all_rows ; country ; united states } = true | for the country records of all rows , all of them fuzzily match to united states . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united states_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united states_4': 'united states'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united states_4': [0]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'arnold palmer', 'united states', '71 + 66 + 70 = 207', '3'], ['2', 'billy casper', 'united states', '69 + 68 + 73 = 210', 'e'], ['3', 'jack nicklaus', 'united states', '71 + 71 + 69 = 211', '+ 1'], ['t4', 'phil rodgers', 'united states', '70 + 70 + 73 = 213', '+ 3'], ['t4', 'dave marr', 'united states', '71 + 74 + 68 = 213', '+ 3'], ['6', 'rives mcbee', 'united states', '76 + 64 + 74 = 214', '+ 2'], ['t7', 'tony lema', 'united states', '71 + 74 + 70 = 215', '+ 5'], ['t7', 'bob goalby', 'united states', '71 + 73 + 71 = 215', '+ 5'], ['t7', 'al mengert', 'united states', '67 + 77 + 71 = 215', '+ 5'], ['10', 'johnny miller ( a )', 'united states', '70 + 72 + 74 = 216', '+ 6']] |
bulgaria in the eurovision song contest 2009 | https://en.wikipedia.org/wiki/Bulgaria_in_the_Eurovision_Song_Contest_2009 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18593648-14.html.csv | superlative | artist krassimir avramov placed highest in the eurovision song contest of 2009 . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '11', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'place'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; place }'}, 'artist'], 'result': 'krassimir avramov', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; place } ; artist }'}, 'krassimir avramov'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; place } ; artist } ; krassimir avramov } = true', 'tointer': 'select the row whose place record of all rows is minimum . the artist record of this row is krassimir avramov .'} | eq { hop { argmin { all_rows ; place } ; artist } ; krassimir avramov } = true | select the row whose place record of all rows is minimum . the artist record of this row is krassimir avramov . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'place_5': 5, 'artist_6': 6, 'krassimir avramov_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'place_5': 'place', 'artist_6': 'artist', 'krassimir avramov_7': 'krassimir avramov'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'place_5': [0], 'artist_6': [1], 'krassimir avramov_7': [2]} | ['draw', 'artist', 'song', 'televote / sms', 'place'] | [['1', 'stefan ilchev', 'get up', '2.95 %', '7'], ['2', 'moto', 'razstoyaniya', '0.37 %', '12'], ['3', 'poli genova', 'one lifetime is not enough', '11.74 %', '2'], ['4', 'danny milev', 'nyama vreme', '2.39 %', '9'], ['5', 'ivelina', 'ready for love', '2.53 %', '8'], ['6', 'grafa', 'vrag', '3.91 %', '5'], ['7', 'sahara', "do n't kiss for the money", '3.20 %', '6'], ['8', 'mariana popova', 'crazy', '8.45 %', '3'], ['9', 'jura tone feat lady b', 'chance to love you', '2.03 %', '10'], ['10', 'stefan dobrev', 'everlasting', '1.16 %', '11'], ['11', 'krassimir avramov', 'illusion', '55.52 %', '1'], ['12', 'nora', "it 's not right", '5.75 %', '4']] |
dakota athletic conference | https://en.wikipedia.org/wiki/Dakota_Athletic_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-262505-1.html.csv | comparative | minot state university has a greater enrollment than dakota state university does . | {'row_1': '6', 'row_2': '2', 'col': '6', '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', 'institution', 'minot state university'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose institution record fuzzily matches to minot state university .', 'tostr': 'filter_eq { all_rows ; institution ; minot state university }'}, 'enrollment'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; institution ; minot state university } ; enrollment }', 'tointer': 'select the rows whose institution record fuzzily matches to minot state university . take the enrollment record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'institution', 'dakota state university'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose institution record fuzzily matches to dakota state university .', 'tostr': 'filter_eq { all_rows ; institution ; dakota state university }'}, 'enrollment'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; institution ; dakota state university } ; enrollment }', 'tointer': 'select the rows whose institution record fuzzily matches to dakota state university . take the enrollment record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; institution ; minot state university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; dakota state university } ; enrollment } } = true', 'tointer': 'select the rows whose institution record fuzzily matches to minot state university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to dakota state university . take the enrollment record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; institution ; minot state university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; dakota state university } ; enrollment } } = true | select the rows whose institution record fuzzily matches to minot state university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to dakota state university . take the enrollment 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, 'institution_7': 7, 'minot state university_8': 8, 'enrollment_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'institution_11': 11, 'dakota state university_12': 12, 'enrollment_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', 'institution_7': 'institution', 'minot state university_8': 'minot state university', 'enrollment_9': 'enrollment', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'institution_11': 'institution', 'dakota state university_12': 'dakota state university', 'enrollment_13': 'enrollment'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'institution_7': [0], 'minot state university_8': [0], 'enrollment_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'institution_11': [1], 'dakota state university_12': [1], 'enrollment_13': [3]} | ['institution', 'nickname', 'location', 'founded', 'type', 'enrollment', 'current conference'] | [['black hills state university', 'yellow jackets', 'spearfish , south dakota', '1881', 'public', '4739', 'rmac ( ncaa division ii )'], ['dakota state university', 'trojans', 'madison , south dakota', '1881', 'public', '2282', 'naia independent'], ['dickinson state university', 'blue hawks', 'dickinson , north dakota', '1916', 'public', '2572', 'frontier conference'], ['jamestown college', 'jimmies', 'jamestown , north dakota', '1883', 'private', '900', 'naia independent'], ['mayville state university', 'comets', 'mayville , north dakota', '1889', 'public', '780', 'naia independent'], ['minot state university', 'beavers', 'minot , north dakota', '1913', 'public', '3851', 'nsic ( ncaa division ii )'], ['si tanka university at huron', 'screaming eagles', 'huron , south dakota', '1883', 'private', 'n / a', 'school closed in 2005'], ['south dakota school of mines and technology', 'hardrockers', 'rapid city , south dakota', '1885', 'public', '2345', 'ncaa d - ii independent'], ['university of mary', 'marauders', 'bismarck , north dakota', '1959', 'private', '2758', 'nsic ( ncaa division ii )']] |
1960 vfl season | https://en.wikipedia.org/wiki/1960_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10775890-6.html.csv | majority | most of the games had an attendance of over 20,000 . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '20000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'crowd', '20000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , most of them are greater than 20000 .', 'tostr': 'most_greater { all_rows ; crowd ; 20000 } = true'} | most_greater { all_rows ; crowd ; 20000 } = true | for the crowd records of all rows , most of them are greater than 20000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '20000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '20000_4': '20000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '20000_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '17.22 ( 124 )', 'richmond', '4.8 ( 32 )', 'mcg', '27249', '28 may 1960'], ['footscray', '6.11 ( 47 )', 'st kilda', '10.5 ( 65 )', 'western oval', '22126', '28 may 1960'], ['north melbourne', '7.6 ( 48 )', 'hawthorn', '9.8 ( 62 )', 'arden street oval', '8600', '28 may 1960'], ['fitzroy', '8.7 ( 55 )', 'essendon', '6.14 ( 50 )', 'brunswick street oval', '25632', '28 may 1960'], ['south melbourne', '12.8 ( 80 )', 'collingwood', '11.12 ( 78 )', 'lake oval', '27000', '28 may 1960'], ['geelong', '17.17 ( 119 )', 'carlton', '10.14 ( 74 )', 'kardinia park', '16589', '28 may 1960']] |
1965 - 66 segunda división | https://en.wikipedia.org/wiki/1965%E2%80%9366_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17832085-4.html.csv | superlative | in the 1965 - 66 segunda división , the largest number of points was for hércules cf. | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', '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', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'club'], 'result': 'hércules cf', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; club }'}, 'hércules cf'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; club } ; hércules cf } = true', 'tointer': 'select the row whose points record of all rows is maximum . the club record of this row is hércules cf .'} | eq { hop { argmax { all_rows ; points } ; club } ; hércules cf } = true | select the row whose points record of all rows is maximum . the club record of this row is hércules cf . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'club_6': 6, 'hércules cf_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'club_6': 'club', 'hércules cf_7': 'hércules cf'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'club_6': [1], 'hércules cf_7': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'hércules cf', '30', '39', '16', '7', '7', '44', '24', '+ 20'], ['2', 'granada cf', '30', '37', '16', '5', '9', '40', '29', '+ 11'], ['3', 'algeciras cf', '30', '35', '14', '7', '9', '42', '29', '+ 13'], ['4', 'real valladolid', '30', '35', '13', '9', '8', '49', '32', '+ 17'], ['5', 'levante ud', '30', '34', '13', '8', '9', '42', '22', '+ 20'], ['6', 'cd mestalla', '30', '33', '10', '13', '7', '45', '44', '+ 1'], ['7', 'cf calvo sotelo', '30', '32', '13', '6', '11', '33', '36', '- 3'], ['8', 'cd tenerife', '30', '32', '13', '6', '11', '40', '34', '+ 6'], ['9', 'rayo vallecano', '30', '31', '12', '7', '11', '37', '26', '+ 11'], ['10', 'real murcia', '30', '29', '12', '5', '13', '31', '37', '- 6'], ['11', 'recreativo de huelva', '30', '29', '11', '7', '12', '31', '30', '+ 1'], ['12', 'cádiz cf', '30', '27', '10', '7', '13', '25', '28', '- 3'], ['13', 'cd constancia', '30', '26', '10', '6', '14', '34', '49', '- 15'], ['14', 'atlético ceuta', '30', '25', '11', '3', '16', '35', '47', '- 12'], ['15', 'melilla cf', '30', '20', '7', '6', '17', '26', '51', '- 25'], ['16', 'cd badajoz', '30', '16', '4', '8', '18', '22', '58', '- 36']] |
asean club championship | https://en.wikipedia.org/wiki/ASEAN_Club_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12303563-1.html.csv | comparative | kingfisher east bengal fc had more winning seasons than pahang fa in the asean club championship . | {'row_1': '1', 'row_2': '4', 'col': '3', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'kingfisher east bengal fc'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to kingfisher east bengal fc .', 'tostr': 'filter_eq { all_rows ; nation ; kingfisher east bengal fc }'}, 'winners'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; kingfisher east bengal fc } ; winners }', 'tointer': 'select the rows whose nation record fuzzily matches to kingfisher east bengal fc . take the winners record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'pahang fa'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to pahang fa .', 'tostr': 'filter_eq { all_rows ; nation ; pahang fa }'}, 'winners'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; pahang fa } ; winners }', 'tointer': 'select the rows whose nation record fuzzily matches to pahang fa . take the winners record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; kingfisher east bengal fc } ; winners } ; hop { filter_eq { all_rows ; nation ; pahang fa } ; winners } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to kingfisher east bengal fc . take the winners record of this row . select the rows whose nation record fuzzily matches to pahang fa . take the winners record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; nation ; kingfisher east bengal fc } ; winners } ; hop { filter_eq { all_rows ; nation ; pahang fa } ; winners } } = true | select the rows whose nation record fuzzily matches to kingfisher east bengal fc . take the winners record of this row . select the rows whose nation record fuzzily matches to pahang fa . take the winners 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, 'nation_7': 7, 'kingfisher east bengal fc_8': 8, 'winners_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'pahang fa_12': 12, 'winners_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', 'nation_7': 'nation', 'kingfisher east bengal fc_8': 'kingfisher east bengal fc', 'winners_9': 'winners', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'pahang fa_12': 'pahang fa', 'winners_13': 'winners'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'kingfisher east bengal fc_8': [0], 'winners_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'pahang fa_12': [1], 'winners_13': [3]} | ['', 'nation', 'winners', 'runners - up', '3rd place', '4th place'] | [['1', 'kingfisher east bengal fc', '1', '0', '0', '0'], ['2', 'tampines rovers fc', '1', '0', '0', '0'], ['3', 'bec tero sasana', '0', '1', '0', '0'], ['4', 'pahang fa', '0', '1', '0', '0'], ['5', 'dpmm fc ( duli pengiran muda mahkota fc )', '0', '0', '1', '0'], ['6', 'hoang anh gia lai', '0', '0', '1', '0'], ['7', 'petrokimia putra fc', '0', '0', '1', '0']] |
ivor bueb | https://en.wikipedia.org/wiki/Ivor_Bueb | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1233860-1.html.csv | aggregation | ivor bueb had a sum of 0 points in all his races between 1957 and 1959 in formula one . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '0', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'points'], 'result': '0', 'ind': 0, 'tostr': 'sum { all_rows ; points }'}, '0'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; points } ; 0 } = true', 'tointer': 'the sum of the points record of all rows is 0 .'} | round_eq { sum { all_rows ; points } ; 0 } = true | the sum of the points record of all rows is 0 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'points_4': 4, '0_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'points_4': 'points', '0_5': '0'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'points_4': [0], '0_5': [1]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1957', 'connaught engineering', 'connaught type b', 'alta straight - 4', '0'], ['1957', 'gilby engineering ltd', 'maserati 250f', 'maserati straight - 6', '0'], ['1958', 'bc ecclestone', 'connaught type b', 'alta straight - 4', '0'], ['1958', 'ecurie demi litre', 'lotus 12', 'climax straight - 4', '0'], ['1959', 'british racing partnership', 'cooper t51', 'climax straight - 4', '0'], ['1959', 'british racing partnership', 'cooper t51', 'borgward straight - 4', '0']] |
henlopen conference | https://en.wikipedia.org/wiki/Henlopen_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13054553-17.html.csv | comparative | in the henlopen conference , the senators had a better overall record than the golden knights . | {'row_1': '2', 'row_2': '1', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'senators'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to senators .', 'tostr': 'filter_eq { all_rows ; team ; senators }'}, 'overall record'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; senators } ; overall record }', 'tointer': 'select the rows whose team record fuzzily matches to senators . take the overall record record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'golden knights'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to golden knights .', 'tostr': 'filter_eq { all_rows ; team ; golden knights }'}, 'overall record'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; golden knights } ; overall record }', 'tointer': 'select the rows whose team record fuzzily matches to golden knights . take the overall record record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team ; senators } ; overall record } ; hop { filter_eq { all_rows ; team ; golden knights } ; overall record } } = true', 'tointer': 'select the rows whose team record fuzzily matches to senators . take the overall record record of this row . select the rows whose team record fuzzily matches to golden knights . take the overall record record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; team ; senators } ; overall record } ; hop { filter_eq { all_rows ; team ; golden knights } ; overall record } } = true | select the rows whose team record fuzzily matches to senators . take the overall record record of this row . select the rows whose team record fuzzily matches to golden knights . take the overall record 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, 'team_7': 7, 'senators_8': 8, 'overall record_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'golden knights_12': 12, 'overall record_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', 'team_7': 'team', 'senators_8': 'senators', 'overall record_9': 'overall record', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'golden knights_12': 'golden knights', 'overall record_13': 'overall record'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'senators_8': [0], 'overall record_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'golden knights_12': [1], 'overall record_13': [3]} | ['school', 'team', 'division record', 'overall record', 'season outcome'] | [['sussex central', 'golden knights', '6 - 0', '7 - 4', 'loss in first round of div i playoffs'], ['dover', 'senators', '5 - 1', '8 - 4', 'loss in semi - finals of div i playoffs'], ['cape henlopen', 'vikings', '4 - 2', '8 - 2', 'failed to make playoffs'], ['caesar rodney', 'riders', '3 - 3', '3 - 7', 'failed to make playoffs'], ['smyrna', 'eagles', '2 - 4', '5 - 5', 'failed to make playoffs'], ['sussex tech', 'ravens', '1 - 5', '4 - 6', 'failed to make playoffs'], ['milford', 'buccaneers', '0 - 6', '1 - 9', 'failed to make playoffs']] |
rqw women 's championship | https://en.wikipedia.org/wiki/RQW_Women%27s_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18963089-2.html.csv | ordinal | the third shortest reign in the rqw women 's championship was 196 days . | {'row': '9', 'col': '3', 'order': '3', 'col_other': 'n/a', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'days held', '3'], 'result': '196', 'ind': 0, 'tostr': 'nth_min { all_rows ; days held ; 3 }', 'tointer': 'the 3rd minimum days held record of all rows is 196 .'}, '196'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; days held ; 3 } ; 196 } = true', 'tointer': 'the 3rd minimum days held record of all rows is 196 .'} | eq { nth_min { all_rows ; days held ; 3 } ; 196 } = true | the 3rd minimum days held record of all rows is 196 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'nth_min_0': 0, 'all_rows_3': 3, 'days held_4': 4, '3_5': 5, '196_6': 6} | {'eq_1': 'eq', 'result_2': 'true', 'nth_min_0': 'nth_min', 'all_rows_3': 'all_rows', 'days held_4': 'days held', '3_5': '3', '196_6': '196'} | {'eq_1': [2], 'result_2': [], 'nth_min_0': [1], 'all_rows_3': [0], 'days held_4': [0], '3_5': [0], '196_6': [1]} | ['wrestlers', 'reign', 'days held', 'location', 'event'] | [['erin angel', '1', '111', 'eastleigh , hampshire', 'a night of champions'], ['vacated', '-', '-', '-', '-'], ['eden black', '1', '302', 'horndean , portsmouth', 'summer brawl 2006'], ['wesna', '1', '392', 'live event', 'a night of champions'], ['sweet saraya', '1', '225', 'vienna , austria', 'wrestling weltmeisterschaft'], ['jetta', '1', '300', 'great yarmouth , norfolk', 'waw 15th anniversary'], ['britani knight', '1', '700', 'takeley , essex', 'hew final fight : the christmas spectacular'], ['queen maya', '1', '491', 'costessey , norfolk', 'bellatrix 2'], ['liberty', '1', '196', 'norwich , norfolk', 'bellatrix 5'], ['sammi baynz', '1', '118', 'norwich , norfolk', 'bellatrix 7 - bellatrix vs shimmer']] |
olivier occéan | https://en.wikipedia.org/wiki/Olivier_Occ%C3%A9an | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1109407-1.html.csv | ordinal | the game played on august 22 , 2007 was the second time that olivier occéan scored an international goal . | {'row': '2', 'col': '3', 'order': '2', 'col_other': '1', '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', 'score', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; score ; 2 }'}, 'date'], 'result': 'august 22 , 2007', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; score ; 2 } ; date }'}, 'august 22 , 2007'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; score ; 2 } ; date } ; august 22 , 2007 } = true', 'tointer': 'select the row whose score record of all rows is 2nd minimum . the date record of this row is august 22 , 2007 .'} | eq { hop { nth_argmin { all_rows ; score ; 2 } ; date } ; august 22 , 2007 } = true | select the row whose score record of all rows is 2nd minimum . the date record of this row is august 22 , 2007 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'score_5': 5, '2_6': 6, 'date_7': 7, 'august 22 , 2007_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', 'score_5': 'score', '2_6': '2', 'date_7': 'date', 'august 22 , 2007_8': 'august 22 , 2007'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'score_5': [0], '2_6': [0], 'date_7': [1], 'august 22 , 2007_8': [2]} | ['date', 'venue', 'score', 'result', 'competition'] | [['february 9 , 2005', 'windsor park , belfast , northern ireland', '1 - 0', '1 - 0', 'friendly'], ['august 22 , 2007', 'laugardalsvöllur , reykjavík , iceland', '1 - 1', '1 - 1', 'friendly'], ['october 7 , 2011', 'beausejour stadium , gros islet , saint lucia', '3 - 0', '7 - 0', '2014 fifa world cup qualification'], ['october 7 , 2011', 'beausejour stadium , gros islet , saint lucia', '5 - 0', '7 - 0', '2014 fifa world cup qualification'], ['november 15 , 2011', 'bmo field , toronto , canada', '1 - 0', '4 - 0', '2014 fifa world cup qualification'], ['june 8 , 2012', 'estadio pedro marrero , havana , cuba', '1 - 0', '1 - 0', '2014 fifa world cup qualification']] |
1954 vfl season | https://en.wikipedia.org/wiki/1954_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10773616-8.html.csv | ordinal | in the 1954 vfl season , the game played at victoria park drew in the largest crowd of 40000 . | {'scope': 'all', 'row': '6', 'col': '6', 'order': '1', 'col_other': '5', 'max_or_min': 'max_to_min', 'value_mentioned': 'yes', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_max', 'args': ['all_rows', 'crowd', '1'], 'result': '40000', 'ind': 0, 'tostr': 'nth_max { all_rows ; crowd ; 1 }', 'tointer': 'the 1st maximum crowd record of all rows is 40000 .'}, '40000'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_max { all_rows ; crowd ; 1 } ; 40000 }', 'tointer': 'the 1st maximum crowd record of all rows is 40000 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'crowd', '1'], 'result': None, 'ind': 2, 'tostr': 'nth_argmax { all_rows ; crowd ; 1 }'}, 'venue'], 'result': 'victoria park', 'ind': 3, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 1 } ; venue }'}, 'victoria park'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park }', 'tointer': 'the venue record of the row with 1st maximum crowd record is victoria park .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_max { all_rows ; crowd ; 1 } ; 40000 } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park } } = true', 'tointer': 'the 1st maximum crowd record of all rows is 40000 . the venue record of the row with 1st maximum crowd record is victoria park .'} | and { eq { nth_max { all_rows ; crowd ; 1 } ; 40000 } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park } } = true | the 1st maximum crowd record of all rows is 40000 . the venue record of the row with 1st maximum crowd record is victoria park . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_max_0': 0, 'all_rows_7': 7, 'crowd_8': 8, '1_9': 9, '40000_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmax_2': 2, 'all_rows_11': 11, 'crowd_12': 12, '1_13': 13, 'venue_14': 14, 'victoria park_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_max_0': 'nth_max', 'all_rows_7': 'all_rows', 'crowd_8': 'crowd', '1_9': '1', '40000_10': '40000', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmax_2': 'nth_argmax', 'all_rows_11': 'all_rows', 'crowd_12': 'crowd', '1_13': '1', 'venue_14': 'venue', 'victoria park_15': 'victoria park'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_max_0': [1], 'all_rows_7': [0], 'crowd_8': [0], '1_9': [0], '40000_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmax_2': [3], 'all_rows_11': [2], 'crowd_12': [2], '1_13': [2], 'venue_14': [3], 'victoria park_15': [4]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['richmond', '17.13 ( 115 )', 'st kilda', '10.6 ( 66 )', 'punt road oval', '17000', '5 june 1954'], ['geelong', '11.5 ( 71 )', 'melbourne', '10.20 ( 80 )', 'kardinia park', '17000', '5 june 1954'], ['essendon', '14.14 ( 98 )', 'north melbourne', '10.15 ( 75 )', 'windy hill', '23000', '5 june 1954'], ['south melbourne', '6.13 ( 49 )', 'hawthorn', '8.10 ( 58 )', 'lake oval', '22000', '5 june 1954'], ['footscray', '13.9 ( 87 )', 'fitzroy', '10.14 ( 74 )', 'western oval', '21000', '5 june 1954'], ['collingwood', '11.15 ( 81 )', 'carlton', '10.12 ( 72 )', 'victoria park', '40000', '5 june 1954']] |
2010 - 11 atlanta thrashers season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Atlanta_Thrashers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27537518-6.html.csv | superlative | in the 2010 - 11 atlanta thrashers season , the highest attendance at philips arena took place on december 30 . | {'scope': 'subset', 'col_superlative': '8', 'row_superlative': '15', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2,7', 'subset': {'col': '7', 'criterion': 'equal', 'value': 'philips arena'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'philips arena'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location ; philips arena }', 'tointer': 'select the rows whose location record fuzzily matches to philips arena .'}, 'attendance'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; location ; philips arena } ; attendance }'}, 'date'], 'result': 'december 30', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; location ; philips arena } ; attendance } ; date }'}, 'december 30'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; location ; philips arena } ; attendance } ; date } ; december 30 } = true', 'tointer': 'select the rows whose location record fuzzily matches to philips arena . select the row whose attendance record of these rows is maximum . the date record of this row is december 30 .'} | eq { hop { argmax { filter_eq { all_rows ; location ; philips arena } ; attendance } ; date } ; december 30 } = true | select the rows whose location record fuzzily matches to philips arena . select the row whose attendance record of these rows is maximum . the date record of this row is december 30 . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'location_6': 6, 'philips arena_7': 7, 'attendance_8': 8, 'date_9': 9, 'december 30_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'location_6': 'location', 'philips arena_7': 'philips arena', 'attendance_8': 'attendance', 'date_9': 'date', 'december 30_10': 'december 30'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'location_6': [0], 'philips arena_7': [0], 'attendance_8': [1], 'date_9': [2], 'december 30_10': [3]} | ['game', 'date', 'opponent', 'score', 'first star', 'decision', 'location', 'attendance', 'record', 'points'] | [['26', 'december 2', 'pittsburgh penguins', '2 - 3', 's crosby', 'o pavelec', 'consol energy center', '18223', '13 - 10 - 3', '29'], ['27', 'december 4', 'washington capitals', '3 - 1', 'o pavelec', 'o pavelec', 'verizon center', '18398', '14 - 10 - 3', '31'], ['28', 'december 6', 'nashville predators', '3 - 2 ot', 'z bogosian', 'o pavelec', 'philips arena', '10024', '15 - 10 - 3', '33'], ['29', 'december 10', 'colorado avalanche', '2 - 4', 'm duchene', 'o pavelec', 'philips arena', '14034', '15 - 11 - 3', '33'], ['30', 'december 11', 'new york islanders', '5 - 4', 'j oduya', 'c mason', 'nassau coliseum', '10056', '16 - 11 - 3', '35'], ['31', 'december 13', 'ottawa senators', '4 - 3 ot', 'b little', 'o pavelec', 'scotiabank place', '18184', '17 - 11 - 3', '37'], ['32', 'december 15', 'tampa bay lightning', '1 - 2 so', 's bergenheim', 'o pavelec', 'st pete times forum', '14441', '17 - 11 - 4', '38'], ['33', 'december 16', 'carolina hurricanes', '2 - 3 so', 's samsonov', 'c mason', 'philips arena', '11043', '17 - 11 - 5', '39'], ['34', 'december 18', 'new jersey devils', '7 - 1', 'e boulton', 'o pavelec', 'philips arena', '17024', '18 - 11 - 5', '41'], ['35', 'december 20', 'toronto maple leafs', '6 - 3', 't enstrom', 'o pavelec', 'air canada centre', '19301', '19 - 11 - 5', '43'], ['36', 'december 21', 'st louis blues', '2 - 4', 'a steen', 'c mason', 'philips arena', '14662', '19 - 12 - 5', '43'], ['37', 'december 23', 'boston bruins', '1 - 4', 's thornton', 'o pavelec', 'td garden', '17565', '19 - 13 - 5', '43'], ['38', 'december 26', 'tampa bay lightning', '2 - 3 ot', 'v lecavalier', 'o pavelec', 'philips arena', '14610', '19 - 13 - 6', '44'], ['39', 'december 28', 'pittsburgh penguins', '3 - 6', 's crosby', 'o pavelec', 'consol energy center', '18322', '19 - 14 - 6', '44'], ['40', 'december 30', 'boston bruins', '3 - 2 so', 't enstrom', 'o pavelec', 'philips arena', '17624', '20 - 14 - 6', '46']] |
2008 - 09 ford ranger one day cup season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Ford_Ranger_One_Day_Cup_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18574677-3.html.csv | count | a total of two players in the 2008 - 09 ford ranger one day cup season played 10 matches . | {'scope': 'all', 'criterion': 'equal', 'value': '10', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'matches', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose matches record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; matches ; 10 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; matches ; 10 } }', 'tointer': 'select the rows whose matches record is equal to 10 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; matches ; 10 } } ; 2 } = true', 'tointer': 'select the rows whose matches record is equal to 10 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; matches ; 10 } } ; 2 } = true | select the rows whose matches record is equal to 10 . 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, 'matches_5': 5, '10_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'matches_5': 'matches', '10_6': '10', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'matches_5': [0], '10_6': [0], '2_7': [2]} | ['player', 'matches', 'runs', 'balls', 'strike rate', 'average', '100s'] | [['lee carseldine', '11', '477', '599', '79.62', '43.36', '2'], ['michael klinger', '10', '469', '669', '70.10', '52.11', '1'], ['chris rogers', '10', '448', '590', '75.93', '44.80', '0'], ['rob quiney', '11', '428', '452', '94.69', '38.90', '0'], ['callum ferguson', '9', '406', '424', '95.75', '45.11', '1']] |
2010 - 11 detroit pistons season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Detroit_Pistons_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27755603-7.html.csv | count | richard hamilton had four high points performances for the detroit pistons . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'richard hamilton', 'result': '4', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high points', 'richard hamilton'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high points record fuzzily matches to richard hamilton .', 'tostr': 'filter_eq { all_rows ; high points ; richard hamilton }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high points ; richard hamilton } }', 'tointer': 'select the rows whose high points record fuzzily matches to richard hamilton . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high points ; richard hamilton } } ; 4 } = true', 'tointer': 'select the rows whose high points record fuzzily matches to richard hamilton . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; high points ; richard hamilton } } ; 4 } = true | select the rows whose high points record fuzzily matches to richard hamilton . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high points_5': 5, 'richard hamilton_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high points_5': 'high points', 'richard hamilton_6': 'richard hamilton', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high points_5': [0], 'richard hamilton_6': [0], '4_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['20', 'december 3', 'orlando', 'l 91 - 104 ( ot )', 'tayshaun prince ( 30 )', 'ben gordon ( 9 )', 'rodney stuckey ( 7 )', 'the palace of auburn hills 18433', '6 - 14'], ['21', 'december 5', 'cleveland', 'w 102 - 92 ( ot )', 'richard hamilton ( 27 )', 'ben wallace ( 9 )', 'rodney stuckey ( 11 )', 'the palace of auburn hills 13081', '7 - 14'], ['22', 'december 7', 'houston', 'l 83 - 97 ( ot )', 'rodney stuckey ( 18 )', 'tayshaun prince , ben wallace ( 8 )', 'rodney stuckey ( 5 )', 'toyota center 14798', '7 - 15'], ['23', 'december 8', 'new orleans', 'l 74 - 93 ( ot )', 'ben gordon ( 19 )', 'ben wallace ( 7 )', 'tracy mcgrady ( 3 )', 'new orleans arena 10823', '7 - 16'], ['24', 'december 10', 'minnesota', 'l 99 - 109 ( ot )', 'richard hamilton ( 26 )', 'greg monroe ( 15 )', 'rodney stuckey ( 6 )', 'target center 13988', '7 - 17'], ['25', 'december 11', 'toronto', 'l 116 - 120 ( ot )', 'rodney stuckey , ben wallace ( 23 )', 'ben wallace ( 14 )', 'rodney stuckey ( 12 )', 'the palace of auburn hills 13343', '7 - 18'], ['26', 'december 14', 'atlanta', 'w 103 - 80 ( ot )', 'richard hamilton ( 24 )', 'charlie villanueva ( 11 )', 'rodney stuckey ( 10 )', 'the palace of auburn hills 12526', '8 - 18'], ['27', 'december 17', 'la clippers', 'l 88 - 109 ( ot )', 'charlie villanueva ( 18 )', 'charlie villanueva ( 9 )', 'tracy mcgrady ( 5 )', 'the palace of auburn hills 16046', '8 - 19'], ['28', 'december 19', 'new orleans', 'w 111 - 108 ( ot )', 'tayshaun prince ( 28 )', 'tayshaun prince ( 12 )', 'will bynum ( 9 )', 'the palace of auburn hills 16452', '9 - 19'], ['29', 'december 22', 'toronto', 'w 115 - 93 ( ot )', 'richard hamilton ( 35 )', 'tracy mcgrady ( 7 )', 'tracy mcgrady ( 7 )', 'air canada centre 15303', '10 - 19'], ['30', 'december 26', 'chicago', 'l 92 - 95 ( ot )', 'tayshaun prince ( 17 )', 'charlie villanueva ( 10 )', 'tayshaun prince ( 6 )', 'the palace of auburn hills 20765', '10 - 20'], ['31', 'december 27', 'charlotte', 'l 100 - 105 ( ot )', 'charlie villanueva ( 25 )', 'chris wilcox ( 8 )', 'will bynum ( 7 )', 'time warner cable arena 14418', '10 - 21'], ['32', 'december 29', 'boston', 'w 104 - 92 ( ot )', 'tracy mcgrady ( 21 )', 'chris wilcox ( 8 )', 'tracy mcgrady ( 8 )', 'the palace of auburn hills 22076', '11 - 21']] |
2009 beach volleyball world championships | https://en.wikipedia.org/wiki/2009_Beach_Volleyball_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18246956-16.html.csv | unique | in the 2009 beach volleyball world championships , set 1 was 10:21 only once and the score then was 0-2 . | {'scope': 'all', 'row': '7', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': '10:21', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'set 1', '10:21'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose set 1 record fuzzily matches to 10:21 .', 'tostr': 'filter_eq { all_rows ; set 1 ; 10:21 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; set 1 ; 10:21 } }', 'tointer': 'select the rows whose set 1 record fuzzily matches to 10:21 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'set 1', '10:21'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose set 1 record fuzzily matches to 10:21 .', 'tostr': 'filter_eq { all_rows ; set 1 ; 10:21 }'}, 'score'], 'result': '0 - 2', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; set 1 ; 10:21 } ; score }'}, '0 - 2'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; set 1 ; 10:21 } ; score } ; 0 - 2 }', 'tointer': 'the score record of this unqiue row is 0 - 2 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; set 1 ; 10:21 } } ; eq { hop { filter_eq { all_rows ; set 1 ; 10:21 } ; score } ; 0 - 2 } } = true', 'tointer': 'select the rows whose set 1 record fuzzily matches to 10:21 . there is only one such row in the table . the score record of this unqiue row is 0 - 2 .'} | and { only { filter_eq { all_rows ; set 1 ; 10:21 } } ; eq { hop { filter_eq { all_rows ; set 1 ; 10:21 } ; score } ; 0 - 2 } } = true | select the rows whose set 1 record fuzzily matches to 10:21 . there is only one such row in the table . the score record of this unqiue row is 0 - 2 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'set 1_7': 7, '10:21_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'score_9': 9, '0 - 2_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'set 1_7': 'set 1', '10:21_8': '10:21', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'score_9': 'score', '0 - 2_10': '0 - 2'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'set 1_7': [0], '10:21_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'score_9': [2], '0 - 2_10': [3]} | ['date', 'score', 'set 1', 'set 2', 'total'] | [['3 july', '0 - 2', '11:21', '11:21', '22:42'], ['3 july', '0 - 2', '11:21', '12:21', '23:42'], ['3 july', '0 - 2', '24:26', '13:21', '37:47'], ['3 july', '2 - 0', '21:18', '21:16', '42:34'], ['3 july', '2 - 0', '21:18', '24:22', '45:40'], ['3 july', '2 - 0', '21:12', '21:18', '42:30'], ['3 july', '0 - 2', '10:21', '13:21', '23:42'], ['3 july', '2 - 0', '21:17', '21:12', '42:29']] |
1997 jacksonville jaguars season | https://en.wikipedia.org/wiki/1997_Jacksonville_Jaguars_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16780011-2.html.csv | count | in the 1997 jacksonville jaguars season , 4 games with attendance over 70,000 people were shown on nbc . | {'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'nbc', 'result': '4', 'col': '5', 'subset': {'col': '6', 'criterion': 'greater_than', 'value': '70000'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'attendance', '70000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; attendance ; 70000 }', 'tointer': 'select the rows whose attendance record is greater than 70000 .'}, 'tv time', 'nbc'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose attendance record is greater than 70000 . among these rows , select the rows whose tv time record fuzzily matches to nbc .', 'tostr': 'filter_eq { filter_greater { all_rows ; attendance ; 70000 } ; tv time ; nbc }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_eq { filter_greater { all_rows ; attendance ; 70000 } ; tv time ; nbc } }', 'tointer': 'select the rows whose attendance record is greater than 70000 . among these rows , select the rows whose tv time record fuzzily matches to nbc . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_greater { all_rows ; attendance ; 70000 } ; tv time ; nbc } } ; 4 } = true', 'tointer': 'select the rows whose attendance record is greater than 70000 . among these rows , select the rows whose tv time record fuzzily matches to nbc . the number of such rows is 4 .'} | eq { count { filter_eq { filter_greater { all_rows ; attendance ; 70000 } ; tv time ; nbc } } ; 4 } = true | select the rows whose attendance record is greater than 70000 . among these rows , select the rows whose tv time record fuzzily matches to nbc . the number of such rows is 4 . | 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, '70000_7': 7, 'tv time_8': 8, 'nbc_9': 9, '4_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', '70000_7': '70000', 'tv time_8': 'tv time', 'nbc_9': 'nbc', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'attendance_6': [0], '70000_7': [0], 'tv time_8': [1], 'nbc_9': [1], '4_10': [3]} | ['week', 'date', 'opponent', 'result', 'tv time', 'attendance'] | [['1', 'august 31 , 1997', 'baltimore ravens', 'w 28 - 27', 'nbc 4:15 pm', '61018'], ['2', 'september 7 , 1997', 'new york giants', 'w 40 - 13', 'fox 4:15 pm', '70581'], ['4', 'september 22 , 1997', 'pittsburgh steelers', 'w 30 - 21', 'abc 9:00 pm', '73016'], ['5', 'september 28 , 1997', 'washington redskins', 'l 24 - 12', 'nbc 1:00 pm', '74421'], ['6', 'october 5 , 1997', 'cincinnati bengals', 'w 21 - 13', 'nbc 1:00 pm', '67128'], ['7', 'october 12 , 1997', 'philadelphia eagles', 'w 38 - 21', 'fox 1:00 pm', '69150'], ['8', 'october 19 , 1997', 'dallas cowboys', 'l 26 - 22', 'nbc 1:00 pm', '64464'], ['9', 'october 26 , 1997', 'pittsburgh steelers', 'l 23 - 17 ( ot )', 'nbc 4:15 pm', '57011'], ['10', 'november 2 , 1997', 'tennessee oilers', 'w 30 - 24', 'nbc 4:15 pm', '27208'], ['11', 'november 9 , 1997', 'kansas city chiefs', 'w 24 - 10', 'nbc 1:00 pm', '70444'], ['12', 'november 16 , 1997', 'tennessee oilers', 'w 17 - 9', 'nbc 1:00 pm', '70070'], ['13', 'november 23 , 1997', 'cincinnati bengals', 'l 31 - 26', 'nbc 1:00 pm', '55158'], ['14', 'november 30 , 1997', 'baltimore ravens', 'w 29 - 27', 'nbc 1:00 pm', '63712'], ['15', 'december 7 , 1997', 'new england patriots', 'l 26 - 20', 'nbc 1:00 pm', '73446'], ['16', 'december 14 , 1997', 'buffalo bills', 'w 20 - 14', 'nbc 1:00 pm', '41231'], ['17', 'december 21 , 1997', 'oakland raiders', 'w 20 - 9', 'nbc 4:15 pm', '40032']] |
2010 - 11 chicago bulls season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Chicago_Bulls_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27712180-13.html.csv | aggregation | in the 2010-11 chicago bulls season , there were 68,742 fans who attended games at united center . | {'scope': 'subset', 'col': '8', 'type': 'sum', 'result': '68,742', 'subset': {'col': '8', 'criterion': 'equal', 'value': 'united center'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'united center'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location attendance ; united center }', 'tointer': 'select the rows whose location attendance record fuzzily matches to united center .'}, 'location attendance'], 'result': '68,742', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; location attendance ; united center } ; location attendance }'}, '68,742'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; location attendance ; united center } ; location attendance } ; 68,742 } = true', 'tointer': 'select the rows whose location attendance record fuzzily matches to united center . the sum of the location attendance record of these rows is 68,742 .'} | round_eq { sum { filter_eq { all_rows ; location attendance ; united center } ; location attendance } ; 68,742 } = true | select the rows whose location attendance record fuzzily matches to united center . the sum of the location attendance record of these rows is 68,742 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, 'united center_6': 6, 'location attendance_7': 7, '68,742_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', 'united center_6': 'united center', 'location attendance_7': 'location attendance', '68,742_8': '68,742'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], 'united center_6': [0], 'location attendance_7': [1], '68,742_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'may 2', 'atlanta', 'l 95 - 103 ( ot )', 'derrick rose ( 24 )', 'joakim noah ( 9 )', 'derrick rose ( 10 )', 'united center 22890', '0 - 1'], ['2', 'may 4', 'atlanta', 'w 86 - 73 ( ot )', 'derrick rose ( 25 )', 'joakim noah ( 14 )', 'derrick rose ( 10 )', 'united center 22872', '1 - 1'], ['3', 'may 6', 'atlanta', 'w 99 - 82 ( ot )', 'derrick rose ( 44 )', 'joakim noah ( 15 )', 'derrick rose ( 7 )', 'philips arena 19521', '2 - 1'], ['4', 'may 8', 'atlanta', 'l 88 - 100 ( ot )', 'derrick rose ( 34 )', 'joakim noah ( 11 )', 'derrick rose ( 10 )', 'philips arena 19263', '2 - 2'], ['5', 'may 10', 'atlanta', 'w 95 - 83 ( ot )', 'derrick rose ( 33 )', 'carlos boozer ( 12 )', 'derrick rose ( 9 )', 'united center 22980', '3 - 2']] |
2008 - 09 belgian first division | https://en.wikipedia.org/wiki/2008%E2%80%9309_Belgian_First_Division | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17260623-1.html.csv | count | two of the football clubs in the belgian first division are located in bruges . | {'scope': 'all', 'criterion': 'equal', 'value': 'bruges', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'bruges'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to bruges .', 'tostr': 'filter_eq { all_rows ; location ; bruges }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; bruges } }', 'tointer': 'select the rows whose location record fuzzily matches to bruges . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; bruges } } ; 2 } = true', 'tointer': 'select the rows whose location record fuzzily matches to bruges . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; location ; bruges } } ; 2 } = true | select the rows whose location record fuzzily matches to bruges . 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, 'location_5': 5, 'bruges_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', 'location_5': 'location', 'bruges_6': 'bruges', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'bruges_6': [0], '2_7': [2]} | ['club', 'location', 'current manager', 'team captain', 'stadium', 'capacity'] | [['standard liège', 'liège', 'lászló bölöni', 'steven defour', 'stade maurice dufrasne', '30000'], ['rsc anderlecht', 'anderlecht', 'ariel jacobs', 'olivier deschacht', 'constant vanden stock stadium', '28063'], ['club brugge kv', 'bruges', 'jacky mathijssen', 'philippe clement', 'jan breydel stadium', '29415'], ['cercle brugge ksv', 'bruges', 'glen de boeck', 'denis viane', 'jan breydel stadium', '29415'], ['kfc germinal beerschot', 'antwerp', 'aimé anthuenis', 'daniel cruz', 'olympisch stadion', '12148'], ['kaa gent', 'ghent', "michel preud ' homme", 'bryan ruiz', 'jules ottenstadion', '12919'], ['sv zulte waregem', 'waregem', 'francky dury', 'ludwin van nieuwenhuyze', 'regenboogstadion', '8500'], ['r charleroi sc', 'charleroi', 'john collins', 'frank defays', 'stade du pays de charleroi', '25000'], ['kvc westerlo', 'westerlo', 'jan ceulemans', 'jef delen', 'het kuipje', '8200'], ['krc genk', 'genk', 'pierre denier and hans visser ( caretakers )', 'hans cornelis', 'cristal arena', '24900'], ['re mouscron', 'mouscron', 'enzo scifo', 'gonzague van dooren', 'stade le canonnier', '11500'], ['ksc lokeren oost - vlaanderen', 'lokeren', 'aleksandar janković', 'olivier doll', 'daknamstadion', '10000'], ['kv mechelen', 'mechelen', 'peter maes', 'jonas ivens', 'veolia - stadion', '14145'], ['ksv roeselare', 'roeselare', 'dennis van wijk', 'stefaan tanghe', 'schiervelde stadion', '9036'], ['fc verbroedering dender eh', 'denderleeuw', 'johan boskamp', 'steven de petter', 'florent beeckmanstadion', '6800'], ['raec mons', 'mons', 'christophe dessy ( caretaker )', 'roberto mirri', 'stade charles tondreau', '9504'], ['kv kortrijk', 'kortrijk', 'hein vanhaezebrouck', 'stéphane demets', 'guldensporen stadion', '8770'], ['afc tubize', 'tubize', 'albert cartier', 'gregory neels', 'stade leburton', '4000']] |
washington redskins draft history | https://en.wikipedia.org/wiki/Washington_Redskins_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17100961-73.html.csv | ordinal | in the washington redskins draft history , the third to last pick was jeff grau . | {'row': '8', 'col': '3', 'order': '3', 'col_other': '4', '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', 'overall', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; overall ; 3 }'}, 'name'], 'result': 'jeff grau', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; overall ; 3 } ; name }'}, 'jeff grau'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; overall ; 3 } ; name } ; jeff grau } = true', 'tointer': 'select the row whose overall record of all rows is 3rd maximum . the name record of this row is jeff grau .'} | eq { hop { nth_argmax { all_rows ; overall ; 3 } ; name } ; jeff grau } = true | select the row whose overall record of all rows is 3rd maximum . the name record of this row is jeff grau . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'overall_5': 5, '3_6': 6, 'name_7': 7, 'jeff grau_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', 'overall_5': 'overall', '3_6': '3', 'name_7': 'name', 'jeff grau_8': 'jeff grau'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'overall_5': [0], '3_6': [0], 'name_7': [1], 'jeff grau_8': [2]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '32', '32', 'patrick ramsey', 'qb', 'tulane'], ['2', '24', '56', 'ladell betts', 'rb', 'iowa'], ['3', '14', '79', 'rashad bauman', 'cb', 'oregon'], ['3', '22', '87', 'cliff russell', 'wr', 'utah'], ['5', '24', '159', 'andre lott', 's', 'tennessee'], ['5', '25', '160', 'robert royal', 'te', 'louisiana state'], ['6', '20', '192', 'reggie coleman', 'ot', 'tennessee'], ['7', '19', '230', 'jeff grau', 'ls', 'ucla'], ['7', '23', '234', 'greg scott', 'de', 'hampton'], ['7', '46', '257', 'rock cartwright', 'fb', 'kansas state']] |
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 | unique | out of tiny lund 's races from 1959 to 1973 , tiny only raced with team buck baker once . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'buck baker', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'buck baker'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to buck baker .', 'tostr': 'filter_eq { all_rows ; team ; buck baker }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; team ; buck baker } } = true', 'tointer': 'select the rows whose team record fuzzily matches to buck baker . there is only one such row in the table .'} | only { filter_eq { all_rows ; team ; buck baker } } = true | select the rows whose team record fuzzily matches to buck baker . 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, 'team_4': 4, 'buck baker_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'team_4': 'team', 'buck baker_5': 'buck baker'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'team_4': [0], 'buck baker_5': [0]} | ['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']] |
grand slam ( tennis ) | https://en.wikipedia.org/wiki/Grand_Slam_%28tennis%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-197638-6.html.csv | unique | don budge was the only player listed under the age of 24 who won a grand slam . | {'scope': 'all', 'row': '2', 'col': '3', 'col_other': '2', 'criterion': 'less_than', 'value': '24', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'age', '24'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose age record is less than 24 .', 'tostr': 'filter_less { all_rows ; age ; 24 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; age ; 24 } }', 'tointer': 'select the rows whose age record is less than 24 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'age', '24'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose age record is less than 24 .', 'tostr': 'filter_less { all_rows ; age ; 24 }'}, 'player'], 'result': 'don budge', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; age ; 24 } ; player }'}, 'don budge'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; age ; 24 } ; player } ; don budge }', 'tointer': 'the player record of this unqiue row is don budge .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; age ; 24 } } ; eq { hop { filter_less { all_rows ; age ; 24 } ; player } ; don budge } } = true', 'tointer': 'select the rows whose age record is less than 24 . there is only one such row in the table . the player record of this unqiue row is don budge .'} | and { only { filter_less { all_rows ; age ; 24 } } ; eq { hop { filter_less { all_rows ; age ; 24 } ; player } ; don budge } } = true | select the rows whose age record is less than 24 . there is only one such row in the table . the player record of this unqiue row is don budge . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'age_7': 7, '24_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'don budge_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'age_7': 'age', '24_8': '24', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'don budge_10': 'don budge'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'age_7': [0], '24_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'don budge_10': [3]} | ['', 'player', 'age', 'australian open', 'french open', 'wimbledon', 'us open'] | [['1', 'fred perry', '26', '1934', '1935', '1934', '1933'], ['2', 'don budge', '23', '1938', '1938', '1937', '1937'], ['3', 'rod laver', '24', '1960', '1962', '1961', '1962'], ['4', 'roy emerson', '27', '1961', '1963', '1964', '1961'], ['5', 'andre agassi', '29', '1995', '1999', '1992', '1994'], ['6', 'roger federer', '27', '2004', '2009', '2003', '2004']] |
1930 vfl season | https://en.wikipedia.org/wiki/1930_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10767641-4.html.csv | superlative | the game played at the junction oval venue drew the highest crowd attendance . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'venue'], 'result': 'junction oval', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; venue }'}, 'junction oval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; venue } ; junction oval } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the venue record of this row is junction oval .'} | eq { hop { argmax { all_rows ; crowd } ; venue } ; junction oval } = true | select the row whose crowd record of all rows is maximum . the venue record of this row is junction oval . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, 'venue_6': 6, 'junction oval_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', 'venue_6': 'venue', 'junction oval_7': 'junction oval'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], 'venue_6': [1], 'junction oval_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '18.15 ( 123 )', 'north melbourne', '15.12 ( 102 )', 'mcg', '8662', '24 may 1930'], ['footscray', '9.10 ( 64 )', 'richmond', '14.7 ( 91 )', 'western oval', '20000', '24 may 1930'], ['essendon', '14.12 ( 96 )', 'hawthorn', '8.13 ( 61 )', 'windy hill', '15000', '24 may 1930'], ['collingwood', '10.12 ( 72 )', 'geelong', '12.18 ( 90 )', 'victoria park', '17000', '24 may 1930'], ['carlton', '20.18 ( 138 )', 'south melbourne', '11.18 ( 84 )', 'princes park', '21000', '24 may 1930'], ['st kilda', '15.18 ( 108 )', 'fitzroy', '8.10 ( 58 )', 'junction oval', '26000', '24 may 1930']] |
usa today all - usa high school baseball team | https://en.wikipedia.org/wiki/USA_Today_All-USA_high_school_baseball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11677100-3.html.csv | unique | the only player from the usa high school baseball team that was from texas was kerry wood . | {'scope': 'all', 'row': '3', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'tx', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'hometown', 'tx'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose hometown record fuzzily matches to tx .', 'tostr': 'filter_eq { all_rows ; hometown ; tx }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; hometown ; tx } }', 'tointer': 'select the rows whose hometown record fuzzily matches to tx . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'hometown', 'tx'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose hometown record fuzzily matches to tx .', 'tostr': 'filter_eq { all_rows ; hometown ; tx }'}, 'player'], 'result': 'kerry wood', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; hometown ; tx } ; player }'}, 'kerry wood'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; hometown ; tx } ; player } ; kerry wood }', 'tointer': 'the player record of this unqiue row is kerry wood .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; hometown ; tx } } ; eq { hop { filter_eq { all_rows ; hometown ; tx } ; player } ; kerry wood } } = true', 'tointer': 'select the rows whose hometown record fuzzily matches to tx . there is only one such row in the table . the player record of this unqiue row is kerry wood .'} | and { only { filter_eq { all_rows ; hometown ; tx } } ; eq { hop { filter_eq { all_rows ; hometown ; tx } ; player } ; kerry wood } } = true | select the rows whose hometown record fuzzily matches to tx . there is only one such row in the table . the player record of this unqiue row is kerry wood . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'hometown_7': 7, 'tx_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'kerry wood_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'hometown_7': 'hometown', 'tx_8': 'tx', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'kerry wood_10': 'kerry wood'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'hometown_7': [0], 'tx_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'kerry wood_10': [3]} | ['player', 'position', 'school', 'hometown', 'mlb draft'] | [['ben davis', 'catcher', 'malvern prep', 'malvern , pa', '1st round - 2nd pick of 1995 draft ( padres )'], ['chad hutchinson', 'pitcher', 'torrey pines high school', 'san diego , ca', 'attended stanford'], ['kerry wood', 'pitcher', 'grand prairie high school', 'grand prairie , tx', '1st round - 4th pick of 1995 draft ( cubs )'], ['michael barrett', 'infielder', 'pace academy', 'atlanta , ga', '1st round - 28th pick of 1995 draft ( expos )'], ['chad hermansen', 'infielder', 'green valley high school', 'henderson , nv', '1st round - 10th pick of 1995 draft ( pirates )'], ['jay hood', 'infielder', 'germantown high school', 'germantown , tn', 'attended georgia tech'], ['nate rolison', 'infielder', 'petal high school', 'petal , ms', '2nd round - 36th pick of 1995 draft ( marlins )'], ['shion newton', 'outfielder', 'boys and girls high school', 'brooklyn , ny', '9th round - 6th pick of 1995 draft ( pirates )'], ['reggie taylor', 'outfielder', 'newberry high school', 'newberry , sc', '1st round - 14th pick of 1995 draft ( phillies )']] |
fiba europe under - 16 championship | https://en.wikipedia.org/wiki/FIBA_Europe_Under-16_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17837875-2.html.csv | count | a total of four countries won exactly three gold medals in the fiba europe under - 16 championship . | {'scope': 'all', 'criterion': 'equal', 'value': '3', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'gold', '3'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is equal to 3 .', 'tostr': 'filter_eq { all_rows ; gold ; 3 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; gold ; 3 } }', 'tointer': 'select the rows whose gold record is equal to 3 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; gold ; 3 } } ; 4 } = true', 'tointer': 'select the rows whose gold record is equal to 3 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; gold ; 3 } } ; 4 } = true | select the rows whose gold record is equal to 3 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'gold_5': 5, '3_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'gold_5': 'gold', '3_6': '3', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'gold_5': [0], '3_6': [0], '4_7': [2]} | ['rank', 'gold', 'silver', 'bronze', 'total'] | [['1', '5', '3', '3', '11'], ['2', '3', '6', '5', '14'], ['3', '3', '4', '5', '12'], ['4', '3', '1', '4', '8'], ['5', '3', '0', '0', '3'], ['6', '2', '3', '2', '7'], ['7', '1', '4', '2', '7'], ['8', '1', '2', '1', '4'], ['9', '1', '2', '0', '3'], ['10', '0', '2', '1', '3'], ['11', '0', '0', '2', '2'], ['12', '0', '0', '1', '1']] |
prva hnl | https://en.wikipedia.org/wiki/Prva_HNL | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1510519-1.html.csv | ordinal | rijeka a , b had the 3rd highest number of seasons in the top division in prva hnl . | {'row': '7', 'col': '4', 'order': '3', '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', 'number of seasons in top division', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; number of seasons in top division ; 3 }'}, 'club'], 'result': 'rijeka a , b', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; number of seasons in top division ; 3 } ; club }'}, 'rijeka a , b'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; number of seasons in top division ; 3 } ; club } ; rijeka a , b } = true', 'tointer': 'select the row whose number of seasons in top division record of all rows is 3rd maximum . the club record of this row is rijeka a , b .'} | eq { hop { nth_argmax { all_rows ; number of seasons in top division ; 3 } ; club } ; rijeka a , b } = true | select the row whose number of seasons in top division record of all rows is 3rd maximum . the club record of this row is rijeka a , b . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'number of seasons in top division_5': 5, '3_6': 6, 'club_7': 7, 'rijeka a , b_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', 'number of seasons in top division_5': 'number of seasons in top division', '3_6': '3', 'club_7': 'club', 'rijeka a , b_8': 'rijeka a , b'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'number of seasons in top division_5': [0], '3_6': [0], 'club_7': [1], 'rijeka a , b_8': [2]} | ['club', 'position in 2012 - 13', 'first season in top division', 'number of seasons in top division', 'number of seasons in prva hnl', 'first season of current spell in top division', 'top division titles', 'last top division title'] | [['dinamo zagreb a , b', '001 1st', '1946 - 47', '68', '23', '1946 - 47', '19 c', '2012 - 13'], ['hajduk split a , b', '004 4th', '1923', '86', '23', '1923', '15 d', '2004 - 05'], ['hrvatski dragovoljac', 'zzz 1st in 2 . hnl', '1995 - 96', '9', '9', '2013 - 14', '0', 'n / a'], ['istra 1961', '006 6th', '2004 - 05', '8', '8', '2009 - 10', '0', 'n / a'], ['lokomotiva b', '002 2nd', '1946 - 47', '15', '5', '2009 - 10', '0', 'n / a'], ['osijek a , b', '007 7th', '1953 - 54', '38', '23', '1981 - 82', '0', 'n / a'], ['rijeka a , b', '003 3rd', '1946 - 47', '52', '23', '1974 - 75', '0', 'n / a'], ['slaven belupo b', '008 8th', '1997 - 98', '17', '17', '1997 - 98', '0', 'n / a'], ['rnk split b', '005 5th', '1957 - 58', '6', '4', '2010 - 11', '0', 'n / a']] |
1952 vfl season | https://en.wikipedia.org/wiki/1952_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10750694-19.html.csv | superlative | richmond was the team who had the highest points scored by a home team . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '5', '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', 'home team score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; home team score }'}, 'home team'], 'result': 'richmond', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; home team score } ; home team }'}, 'richmond'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; home team score } ; home team } ; richmond } = true', 'tointer': 'select the row whose home team score record of all rows is maximum . the home team record of this row is richmond .'} | eq { hop { argmax { all_rows ; home team score } ; home team } ; richmond } = true | select the row whose home team score record of all rows is maximum . the home team record of this row is richmond . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'home team score_5': 5, 'home team_6': 6, 'richmond_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'home team score_5': 'home team score', 'home team_6': 'home team', 'richmond_7': 'richmond'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'home team score_5': [0], 'home team_6': [1], 'richmond_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['hawthorn', '8.11 ( 59 )', 'north melbourne', '12.10 ( 82 )', 'glenferrie oval', '6000', '30 august 1952'], ['footscray', '13.13 ( 91 )', 'south melbourne', '8.13 ( 61 )', 'western oval', '20723', '30 august 1952'], ['collingwood', '13.14 ( 92 )', 'melbourne', '10.11 ( 71 )', 'victoria park', '18753', '30 august 1952'], ['st kilda', '10.12 ( 72 )', 'fitzroy', '8.18 ( 66 )', 'junction oval', '9000', '30 august 1952'], ['richmond', '15.11 ( 101 )', 'essendon', '11.10 ( 76 )', 'punt road oval', '28000', '30 august 1952'], ['geelong', '10.17 ( 77 )', 'carlton', '3.14 ( 32 )', 'kardinia park', '49107', '30 august 1952']] |
1922 in brazilian football | https://en.wikipedia.org/wiki/1922_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15389424-1.html.csv | comparative | aa das palmeiras had a higher number of drawn football matches than minas gerais . | {'row_1': '5', 'row_2': '7', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'aa das palmeiras'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to aa das palmeiras .', 'tostr': 'filter_eq { all_rows ; team ; aa das palmeiras }'}, 'drawn'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; aa das palmeiras } ; drawn }', 'tointer': 'select the rows whose team record fuzzily matches to aa das palmeiras . take the drawn record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'minas gerais'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to minas gerais .', 'tostr': 'filter_eq { all_rows ; team ; minas gerais }'}, 'drawn'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; minas gerais } ; drawn }', 'tointer': 'select the rows whose team record fuzzily matches to minas gerais . take the drawn record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team ; aa das palmeiras } ; drawn } ; hop { filter_eq { all_rows ; team ; minas gerais } ; drawn } } = true', 'tointer': 'select the rows whose team record fuzzily matches to aa das palmeiras . take the drawn record of this row . select the rows whose team record fuzzily matches to minas gerais . take the drawn record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; team ; aa das palmeiras } ; drawn } ; hop { filter_eq { all_rows ; team ; minas gerais } ; drawn } } = true | select the rows whose team record fuzzily matches to aa das palmeiras . take the drawn record of this row . select the rows whose team record fuzzily matches to minas gerais . take the drawn 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, 'team_7': 7, 'aa das palmeiras_8': 8, 'drawn_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'minas gerais_12': 12, 'drawn_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', 'team_7': 'team', 'aa das palmeiras_8': 'aa das palmeiras', 'drawn_9': 'drawn', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'minas gerais_12': 'minas gerais', 'drawn_13': 'drawn'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'aa das palmeiras_8': [0], 'drawn_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'minas gerais_12': [1], 'drawn_13': [3]} | ['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference'] | [['1', 'corinthians', '30', '18', '2', '2', '19', '53'], ['2', 'palestra itália - sp', '29', '18', '1', '3', '24', '24'], ['3', 'sírio', '26', '18', '4', '3', '27', '17'], ['4', 'paulistano', '22', '18', '2', '6', '34', '17'], ['5', 'aa das palmeiras', '18', '18', '4', '7', '29', '8'], ['6', 'ypiranga - sp', '15', '18', '5', '8', '34', '- 2'], ['7', 'minas gerais', '14', '18', '2', '10', '54', '- 29'], ['8', 'aa são bento', '13', '18', '1', '11', '32', '- 7']] |
1947 vfl season | https://en.wikipedia.org/wiki/1947_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809444-2.html.csv | aggregation | the average crowd size for round 2 of the 1947 vfl season was about 18,000 people . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '18000', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '18000', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '18000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 18000 } = true', 'tointer': 'the average of the crowd record of all rows is 18000 .'} | round_eq { avg { all_rows ; crowd } ; 18000 } = true | the average of the crowd record of all rows is 18000 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '18000_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '18000_5': '18000'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '18000_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '8.11 ( 59 )', 'st kilda', '8.13 ( 61 )', 'arden street oval', '8000', '26 april 1947'], ['fitzroy', '13.22 ( 100 )', 'richmond', '11.11 ( 77 )', 'brunswick street oval', '22000', '26 april 1947'], ['melbourne', '14.25 ( 109 )', 'geelong', '11.7 ( 73 )', 'mcg', '12000', '26 april 1947'], ['footscray', '15.13 ( 103 )', 'essendon', '13.11 ( 89 )', 'western oval', '22000', '26 april 1947'], ['hawthorn', '13.9 ( 87 )', 'collingwood', '19.20 ( 134 )', 'glenferrie oval', '15000', '26 april 1947'], ['south melbourne', '12.12 ( 84 )', 'carlton', '9.16 ( 70 )', 'lake oval', '30000', '26 april 1947']] |
2008 italian motorcycle grand prix | https://en.wikipedia.org/wiki/2008_Italian_motorcycle_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16212245-1.html.csv | aggregation | in the 2008 italian motorcycle grand prix , the average number of laps completed by the competitors was 19 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '19', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'laps'], 'result': '19', 'ind': 0, 'tostr': 'avg { all_rows ; laps }'}, '19'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; laps } ; 19 } = true', 'tointer': 'the average of the laps record of all rows is 19 .'} | round_eq { avg { all_rows ; laps } ; 19 } = true | the average of the laps record of all rows is 19 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'laps_4': 4, '19_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'laps_4': 'laps', '19_5': '19'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'laps_4': [0], '19_5': [1]} | ['rider', 'manufacturer', 'laps', 'time', 'grid'] | [['valentino rossi', 'yamaha', '23', '42:31.153', '1'], ['casey stoner', 'ducati', '23', '+ 2.201', '4'], ['dani pedrosa', 'honda', '23', '+ 4.867', '2'], ['alex de angelis', 'honda', '23', '+ 6.313', '10'], ['colin edwards', 'yamaha', '23', '+ 12.530', '5'], ['james toseland', 'yamaha', '23', '+ 13.806', '8'], ['loris capirossi', 'suzuki', '23', '+ 14.447', '3'], ['andrea dovizioso', 'honda', '23', '+ 15.319', '13'], ['shinya nakano', 'honda', '23', '+ 15.327', '9'], ['chris vermeulen', 'suzuki', '23', '+ 30.785', '11'], ['sylvain guintoli', 'ducati', '23', '+ 39.621', '17'], ['toni elias', 'ducati', '23', '+ 50.021', '16'], ['nicky hayden', 'honda', '23', '+ 50.440', '6'], ['tadayuki okada', 'honda', '23', '+ 58.849', '15'], ['anthony west', 'kawasaki', '23', '+ 1:00.736', '19'], ['jorge lorenzo', 'yamaha', '6', 'accident', '7'], ['john hopkins', 'kawasaki', '6', 'accident', '14'], ['randy de puniet', 'honda', '5', 'accident', '12'], ['marco melandri', 'ducati', '5', 'accident', '18']] |
central province ( kenya ) | https://en.wikipedia.org/wiki/Central_Province_%28Kenya%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1404414-2.html.csv | aggregation | the counties in the central province of kenya had a combined population of 4383743 in 2009 . | {'scope': 'all', 'col': '5', 'type': 'sum', 'result': '4383743', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'population census 2009'], 'result': '4383743', 'ind': 0, 'tostr': 'sum { all_rows ; population census 2009 }'}, '4383743'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; population census 2009 } ; 4383743 } = true', 'tointer': 'the sum of the population census 2009 record of all rows is 4383743 .'} | round_eq { sum { all_rows ; population census 2009 } ; 4383743 } = true | the sum of the population census 2009 record of all rows is 4383743 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'population census 2009_4': 4, '4383743_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'population census 2009_4': 'population census 2009', '4383743_5': '4383743'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'population census 2009_4': [0], '4383743_5': [1]} | ['code', 'county', 'former province', 'area ( km 2 )', 'population census 2009', 'capital'] | [['18', 'nyandarua', 'central', '3107.7', '596268', 'ol kalou'], ['19', 'nyeri', 'central', '2361.0', '693558', 'nyeri'], ['20', 'kirinyaga', 'central', '1205.4', '528054', 'kerugoya / kutus'], ['21', "murang ' a", 'central', '2325.8', '942581', "murang ' a"], ['22', 'kiambu', 'central', '2449.2', '1623282', 'kiambu']] |
locomotives of the london and north eastern railway | https://en.wikipedia.org/wiki/Locomotives_of_the_London_and_North_Eastern_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1169568-2.html.csv | aggregation | a total of 427 locomotives of the london and north eastern railway were built . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '427', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'quantity'], 'result': '427', 'ind': 0, 'tostr': 'sum { all_rows ; quantity }'}, '427'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; quantity } ; 427 } = true', 'tointer': 'the sum of the quantity record of all rows is 427 .'} | round_eq { sum { all_rows ; quantity } ; 427 } = true | the sum of the quantity record of all rows is 427 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'quantity_4': 4, '427_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'quantity_4': 'quantity', '427_5': '427'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'quantity_4': [0], '427_5': [1]} | ['class', 'type', 'quantity', 'date', 'lner class'] | [['2', '4 - 4 - 0', '25', '1887 - 1892', 'd7'], ['3', '2 - 4 - 2t', '39', '1889 - 1892', 'f1'], ['6ai', '0 - 6 - 0', '12', '1888', 'j8'], ['6d', '2 - 4 - 0', '3', '1887', 'e2'], ['6db', '4 - 4 - 0', '3', '1888', 'd8'], ['9', '0 - 6 - 0', '6', '1888 - 89', 'j13'], ['9a', '0 - 6 - 2t', '55', '1889 - 92', 'n4'], ['9b & 9e', '0 - 6 - 0', '31', '1891 - 95', 'j9'], ['9c & 9f', '0 - 6 - 2t', '129', '1891 - 1901', 'n5'], ['9d , 9h & 9 m', '0 - 6 - 0', '124', '1892 - 1902', 'j10']] |
1983 - 84 houston cougars men 's basketball team | https://en.wikipedia.org/wiki/1983%E2%80%9384_Houston_Cougars_men%27s_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22496374-1.html.csv | aggregation | for the 1983 - 84 houston cougars men 's basketball team , the average weight for players who are sophomores was 192.5 . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': '192.5', 'subset': {'col': '6', 'criterion': 'equal', 'value': 'sophomore'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', 'sophomore'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; sophomore }', 'tointer': 'select the rows whose year record fuzzily matches to sophomore .'}, 'weight'], 'result': '192.5', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; year ; sophomore } ; weight }'}, '192.5'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; year ; sophomore } ; weight } ; 192.5 } = true', 'tointer': 'select the rows whose year record fuzzily matches to sophomore . the average of the weight record of these rows is 192.5 .'} | round_eq { avg { filter_eq { all_rows ; year ; sophomore } ; weight } ; 192.5 } = true | select the rows whose year record fuzzily matches to sophomore . the average of the weight record of these rows is 192.5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, 'sophomore_6': 6, 'weight_7': 7, '192.5_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', 'sophomore_6': 'sophomore', 'weight_7': 'weight', '192.5_8': '192.5'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], 'sophomore_6': [0], 'weight_7': [1], '192.5_8': [2]} | ['name', '-', 'position', 'height', 'weight', 'year', 'home town', 'high school'] | [['marvin alexander', '22', 'guard - forward', '6 - 4', '190', 'junior', 'houston , tx', 'worthing'], ['benny anders', '32', 'guard - forward', '6 - 5', '188', 'junior', 'bernice , la', 'bernice'], ['greg anderson', '54', 'forward - center', '6 - 9', '220', 'freshman', 'houston , tx', 'worthing'], ['stacey belcher', '50', 'forward', '6 - 6', '210', 'freshman', 'houston , tx', 'yates'], ['braxton clark', '52', 'forward', '6 - 8', '230', 'junior', 'san francisco , ca', 'mission / de anza cc'], ['eric dickens', '14', 'guard', '6 - 1', '170', 'junior', 'houston , tx', 'madison'], ['alvin franklin', '20', 'guard', '6 - 2', '185', 'sophomore', 'la marque , tx', 'la marque'], ['reid gettys', '44', 'guard - forward', '6 - 6', '200', 'junior', 'houston , tx', 'memorial'], ['derek giles', '10', 'guard', '6 - 3', '175', 'senior', 'queens , ny', 'bayside'], ['hakeem olajuwon', '34', 'center', '7 - 0', '255', 'junior', 'lagos , nigeria', 'muslim teachers college'], ['gary orsak', '30', 'forward', '6 - 7', '220', 'junior', 'alvin , texas', 'alvin'], ['renaldo thomas', '12', 'guard', '6 - 4', '200', 'sophomore', 'gary , in', 'roosevelt'], ['james weaver', '24', 'guard', '6 - 4', '190', 'freshman', 'nederland , tx', 'nederland'], ['rickie winslow', '40', 'forward', '6 - 8', '215', 'freshman', 'houston , tx', 'yates']] |
rogue trader | https://en.wikipedia.org/wiki/Rogue_trader | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15438337-1.html.csv | unique | boris picano - nacci is the only rogue trader who was charged a fine . | {'scope': 'all', 'row': '5', 'col': '7', 'col_other': '1', 'criterion': 'equal', 'value': 'fine', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sentence', 'fine'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sentence record fuzzily matches to fine .', 'tostr': 'filter_eq { all_rows ; sentence ; fine }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; sentence ; fine } }', 'tointer': 'select the rows whose sentence record fuzzily matches to fine . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sentence', 'fine'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sentence record fuzzily matches to fine .', 'tostr': 'filter_eq { all_rows ; sentence ; fine }'}, 'name'], 'result': 'boris picano - nacci', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; sentence ; fine } ; name }'}, 'boris picano - nacci'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; sentence ; fine } ; name } ; boris picano - nacci }', 'tointer': 'the name record of this unqiue row is boris picano - nacci .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; sentence ; fine } } ; eq { hop { filter_eq { all_rows ; sentence ; fine } ; name } ; boris picano - nacci } } = true', 'tointer': 'select the rows whose sentence record fuzzily matches to fine . there is only one such row in the table . the name record of this unqiue row is boris picano - nacci .'} | and { only { filter_eq { all_rows ; sentence ; fine } } ; eq { hop { filter_eq { all_rows ; sentence ; fine } ; name } ; boris picano - nacci } } = true | select the rows whose sentence record fuzzily matches to fine . there is only one such row in the table . the name record of this unqiue row is boris picano - nacci . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'sentence_7': 7, 'fine_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'boris picano - nacci_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'sentence_7': 'sentence', 'fine_8': 'fine', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'boris picano - nacci_10': 'boris picano - nacci'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'sentence_7': [0], 'fine_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'boris picano - nacci_10': [3]} | ['name', 'country', 'date ( s )', 'loss', 'institution', 'market activity', 'sentence'] | [['yasuo hamanaka', 'tokyo , japan', '1996', '2.6 billion', 'sumitomo corporation', 'copper', '8 years prison'], ['kweku adoboli', 'london , united kingdom', '2011', '2.3 billion', 'ubs', 's & p 500 , dax , and eurostoxx futures', '7 years in prison'], ['nick leeson', 'united kingdom', '1995', '1.3 billion ( 827 million )', 'barings bank', 'nikkei index futures', '6.5 years prison'], ['toshihide iguchi', 'osaka , japan / new york city , united states', '1995', '1.1 billion', 'resona holdings', 'us treasury bonds', '4 years prison'], ['boris picano - nacci', 'paris , france', '2008', '980.3 million ( 751 million )', "groupe caisse d'epargne", 'equity derivatives', '315 million fine ( 2 year suspended sentence )'], ['john rusnak', 'maryland , united states', '2002', '691 million', 'allied irish banks', 'foreign exchange options', '7.5 years prison']] |
mack hellings | https://en.wikipedia.org/wiki/Mack_Hellings | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1252130-1.html.csv | superlative | 1948 was the year in which mack hellings drove the highest amount of laps in his career . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'laps'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; laps }'}, 'year'], 'result': '1948', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; laps } ; year }'}, '1948'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; laps } ; year } ; 1948 } = true', 'tointer': 'select the row whose laps record of all rows is maximum . the year record of this row is 1948 .'} | eq { hop { argmax { all_rows ; laps } ; year } ; 1948 } = true | select the row whose laps record of all rows is maximum . the year record of this row is 1948 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'laps_5': 5, 'year_6': 6, '1948_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'laps_5': 'laps', 'year_6': 'year', '1948_7': '1948'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'laps_5': [0], 'year_6': [1], '1948_7': [2]} | ['year', 'start', 'qual', 'rank', 'finish', 'laps'] | [['1948', '21', '127.968', '6', '5', '200'], ['1949', '14', '128.260', '11', '16', '172'], ['1950', '26', '130.687', '20', '13', '132'], ['1951', '23', '132.925', '22', '31', '18']] |
list of tallest buildings in quebec city | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Quebec_City | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11111275-1.html.csv | superlative | place hauteville has more floors than any of the other tallest buildings in quebec city . | {'scope': 'all', 'col_superlative': '4', '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', 'floors'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; floors }'}, 'name'], 'result': 'place hauteville', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; floors } ; name }'}, 'place hauteville'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; floors } ; name } ; place hauteville } = true', 'tointer': 'select the row whose floors record of all rows is maximum . the name record of this row is place hauteville .'} | eq { hop { argmax { all_rows ; floors } ; name } ; place hauteville } = true | select the row whose floors record of all rows is maximum . the name record of this row is place hauteville . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'floors_5': 5, 'name_6': 6, 'place hauteville_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'floors_5': 'floors', 'name_6': 'name', 'place hauteville_7': 'place hauteville'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'floors_5': [0], 'name_6': [1], 'place hauteville_7': [2]} | ['rank', 'name', 'height m ( ft )', 'floors', 'year'] | [['1', 'édifice marie - guyart', '-', '33', '1972'], ['2', 'complexe jules dallaire ii', '-', '28', '2013'], ['3', 'place hauteville', '-', '34', '1974'], ['4', 'hôtel loews le concorde', '-', '31', '1974'], ['5', 'hôtel hilton québec', '-', '28', '1974'], ['6', 'édifice price', '-', '18', '1930'], ['7', 'place de la capitale', '-', '21', '1974'], ['8', 'le samuel - holland i', '-', '24', '1981'], ['9', 'chteau frontenac', '-', '18', '1893'], ['10', "édifice d'youville", '-', '21', '1969'], ['11', 'complexe jules - dallaire i', '-', '17', '2010']] |
list of csi : ny characters | https://en.wikipedia.org/wiki/List_of_CSI%3A_NY_characters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11240028-5.html.csv | majority | a majority of the notable villains from the csi : ny show were charged with a crime of murder . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'murder', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'crime', 'murder'], 'result': True, 'ind': 0, 'tointer': 'for the crime records of all rows , most of them fuzzily match to murder .', 'tostr': 'most_eq { all_rows ; crime ; murder } = true'} | most_eq { all_rows ; crime ; murder } = true | for the crime records of all rows , most of them fuzzily match to murder . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crime_3': 3, 'murder_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crime_3': 'crime', 'murder_4': 'murder'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'crime_3': [0], 'murder_4': [0]} | ['character', 'portrayed by', 'crime', 'first appearance', 'last appearance'] | [['sonny sassone', 'michael deluise', 'murder ( 2 counts )', 'tanglewood', 'run silent , run deep'], ['frankie mala', 'ed quinn', 'attempted murder ( attacked stella )', 'grand murder at central station', 'all access'], ['henry darius', 'james badge dale', 'murder ( 15 counts )', 'felony flight ( csi : miami crossover )', 'manhattan manhunt'], ['dj pratt', 'chad williams', 'murder / rape ( 1 / 2 counts ) ( killed aiden )', 'summer in the city', 'heroes'], ['shane casey', 'edward furlong', 'murder ( 8 counts )', 'hung out to dry', 'the 34th floor'], ['clay dobson', 'joey lawrence', 'murder ( 3 counts )', 'past imperfect', 'comes around'], ['andrew drew bedford ( aka 333 stalker )', 'kerr smith', 'attempted murder ( 6 counts )', 'the deep', 'the thing about heroes'], ['suspect x', 'kam heskin', 'murder ( 6 counts )', 'down the rabbit hole', 'doa for a day'], ['cabbie killer', 'ryan locke', 'murder ( 6 counts )', 'like water for murder', 'taxi'], ['ethan scott ( aka joe )', 'elias koteas', 'murder ( 2 counts )', 'hostage', 'veritas'], ['sebastian diakos', 'adoni maropis', 'murder ( 2 counts )', 'the cost of living', 'point of no return'], ['george kolovos', 'paul papadakis', 'murder ( 1 count )', 'the cost of living', 'grounds for deception'], ['hollis eckhart ( aka the compass killer )', 'skeet ulrich', 'murder ( 3 counts )', "lat 40 degree 47 ' n / long 73 degree 58 ' w", 'manhattanhenge'], ['raymond harris', 'clifton collins , jr', 'murder ( 2 counts )', 'nothing for something', 'life sentence'], ['john curtis', 'jason wiles', 'rape ( 5 counts )', 'crushed', 'means to an end']] |
2007 - 08 los angeles clippers season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Los_Angeles_Clippers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11965402-4.html.csv | ordinal | the los angeles clippers ' game on december 21 recorded their highest attendance of the 2007 - 08 season . | {'row': '11', 'col': '6', 'order': '1', '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', 'attendance', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 1 }'}, 'date'], 'result': 'december 21 , 2007', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 1 } ; date }'}, 'december 21 , 2007'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; december 21 , 2007 } = true', 'tointer': 'select the row whose attendance record of all rows is 1st maximum . the date record of this row is december 21 , 2007 .'} | eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; december 21 , 2007 } = true | select the row whose attendance record of all rows is 1st maximum . the date record of this row is december 21 , 2007 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '1_6': 6, 'date_7': 7, 'december 21 , 2007_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', 'attendance_5': 'attendance', '1_6': '1', 'date_7': 'date', 'december 21 , 2007_8': 'december 21 , 2007'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '1_6': [0], 'date_7': [1], 'december 21 , 2007_8': [2]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record'] | [['december 2 , 2007', 'pacers', '101 - 95', 'clippers', 'chris kaman ( 22 )', '13741', '6 - 9'], ['december 4 , 2007', 'bucks', '87 - 78', 'clippers', 'corey maggette ( 20 )', '16004', '6 - 10'], ['december 5 , 2007', 'clippers', '88 - 95', 'sonics', 'corey maggette ( 23 )', '10961', '6 - 11'], ['december 7 , 2007', 'clippers', '97 - 87', 'kings', 'chris kaman ( 26 )', '13094', '7 - 11'], ['december 9 , 2007', 'miami heat', '100 - 94', 'clippers', 'corey maggette ( 24 )', '16335', '7 - 12'], ['december 11 , 2007', 'clippers', '91 - 82', 'nets', 'two way tie ( 18 )', '13433', '8 - 12'], ['december 12 , 2007', 'clippers', '103 - 108', 'bobcats', 'corey maggette ( 23 )', '10751', '8 - 13'], ['december 14 , 2007', 'clippers', '98 - 91', 'grizzlies', 'two way tie ( 23 )', '10819', '9 - 13'], ['december 16 , 2007', 'clippers', '92 - 113', 'lakers', 'corey maggette ( 27 )', '18997', '9 - 14'], ['december 18 , 2007', 'raptors', '80 - 77', 'clippers', 'corey maggette ( 22 )', '14455', '9 - 15'], ['december 21 , 2007', 'clippers', '89 - 102', 'mavericks', 'chris kaman ( 24 )', '20246', '9 - 16'], ['december 22 , 2007', 'clippers', '90 - 99', 'spurs', 'al thornton ( 25 )', '18797', '9 - 17'], ['december 27 , 2007', 'suns', '108 - 88', 'clippers', 'corey maggette ( 21 )', '18422', '9 - 18'], ['december 28 , 2007', 'clippers', '88 - 94', 'suns', 'chris kaman ( 22 )', '17871', '9 - 19'], ['december 31 , 2007', 'timberwolves', '82 - 91', 'clippers', 'cuttino mobley ( 18 )', '14404', '10 - 19']] |
1991 - 92 segunda división | https://en.wikipedia.org/wiki/1991%E2%80%9392_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12097374-2.html.csv | ordinal | barcelona b had the 2nd most goals for of all the teams in the 1991-92 segunda division . | {'row': '6', 'col': '8', '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', 'goals for', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; goals for ; 2 }'}, 'club'], 'result': 'barcelona b', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; goals for ; 2 } ; club }'}, 'barcelona b'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; goals for ; 2 } ; club } ; barcelona b } = true', 'tointer': 'select the row whose goals for record of all rows is 2nd maximum . the club record of this row is barcelona b .'} | eq { hop { nth_argmax { all_rows ; goals for ; 2 } ; club } ; barcelona b } = true | select the row whose goals for record of all rows is 2nd maximum . the club record of this row is barcelona b . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'goals for_5': 5, '2_6': 6, 'club_7': 7, 'barcelona b_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', 'goals for_5': 'goals for', '2_6': '2', 'club_7': 'club', 'barcelona b_8': 'barcelona b'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'goals for_5': [0], '2_6': [0], 'club_7': [1], 'barcelona b_8': [2]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'celta de vigo', '38', '53 + 15', '22', '9', '7', '61', '26', '+ 35'], ['2', 'rayo vallecano', '38', '48 + 10', '20', '8', '10', '52', '27', '+ 25'], ['3', 'ue figueres', '38', '47 + 9', '16', '15', '7', '43', '27', '+ 16'], ['4', 'real betis', '38', '46 + 8', '18', '10', '10', '54', '43', '+ 11'], ['5', 'ue lleida', '38', '43 + 5', '17', '9', '12', '52', '36', '+ 16'], ['6', 'barcelona b', '38', '41 + 3', '17', '7', '14', '59', '53', '+ 6'], ['7', 'cp mérida', '38', '41 + 3', '16', '9', '13', '58', '48', '+ 10'], ['8', 'sd compostela', '38', '41 + 3', '15', '11', '12', '36', '32', '+ 4'], ['9', 'ce sabadell fc', '38', '38', '16', '6', '16', '34', '35', '- 1'], ['10', 'racing de santander', '38', '37 - 1', '15', '7', '16', '39', '44', '- 5'], ['11', 'real murcia 1', '38', '36 - 2', '11', '14', '13', '32', '36', '- 4'], ['12', 'sd eibar', '38', '36 - 2', '11', '14', '13', '19', '22', '- 3'], ['13', 'bilbao athletic', '38', '35 - 3', '12', '11', '15', '36', '40', '- 4'], ['14', 'palamós cf', '38', '35 - 3', '13', '9', '16', '39', '46', '- 7'], ['15', 'cd castellón', '38', '35 - 3', '13', '9', '16', '42', '48', '- 6'], ['16', 'real madrid b', '38', '34 - 4', '11', '12', '15', '44', '55', '- 11'], ['17', 'sestao 1', '38', '32 - 6', '11', '10', '17', '26', '44', '- 18'], ['18', 'cd málaga 2', '38', '30 - 8', '10', '10', '18', '25', '45', '- 20'], ['19', 'real avilés', '38', '27 - 11', '9', '9', '20', '31', '56', '- 25'], ['20', 'ud las palmas', '38', '25 - 13', '8', '9', '21', '39', '58', '- 19']] |
canadian open ( badminton ) | https://en.wikipedia.org/wiki/Canadian_Open_%28badminton%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12552861-1.html.csv | majority | most of the badminton performances were in the 1950s . | {'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1960', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'year', '1960'], 'result': True, 'ind': 0, 'tointer': 'for the year records of all rows , most of them are less than 1960 .', 'tostr': 'most_less { all_rows ; year ; 1960 } = true'} | most_less { all_rows ; year ; 1960 } = true | for the year records of all rows , most of them are less than 1960 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year_3': 3, '1960_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year_3': 'year', '1960_4': '1960'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'year_3': [0], '1960_4': [0]} | ['year', "men 's singles", "women 's singles", "men 's doubles", "women 's doubles", 'mixed doubles'] | [['1957', 'dave f mctaggart', 'judy devlin', 'don k smythe h budd porter', 'sue devlin judy devlin', 'robert b williams ethel marshall'], ['1958', 'dave f mctaggart', 'jean miller', 'don k smythe h budd porter', 'marjorie shedd joan hennessy', 'william purcell marjorie shedd'], ['1959', 'tan joe hok', 'judy devlin', 'lim say hup teh kew san', 'sue devlin judy devlin', 'don p davis judy devlin'], ['1960', 'tan joe hok', 'marjorie shedd', 'lim say hup teh kew san', 'lois alston beulah armendariz', 'finn kobberø jean miller'], ['1961', 'erland kops', 'marjorie shedd', 'finn kobberø jörgen hammergaard hansen', 'marjorie shedd dorothy tinline', 'finn kobberø jean miller']] |
1995 australian touring car championship | https://en.wikipedia.org/wiki/1995_Australian_Touring_Car_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16452451-2.html.csv | count | glenn seton won a total of four rounds in the 1995 australian touring car championship . | {'scope': 'all', 'criterion': 'equal', 'value': 'glenn seton', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'round winner', 'glenn seton'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose round winner record fuzzily matches to glenn seton .', 'tostr': 'filter_eq { all_rows ; round winner ; glenn seton }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; round winner ; glenn seton } }', 'tointer': 'select the rows whose round winner record fuzzily matches to glenn seton . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; round winner ; glenn seton } } ; 4 } = true', 'tointer': 'select the rows whose round winner record fuzzily matches to glenn seton . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; round winner ; glenn seton } } ; 4 } = true | select the rows whose round winner record fuzzily matches to glenn seton . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'round winner_5': 5, 'glenn seton_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'round winner_5': 'round winner', 'glenn seton_6': 'glenn seton', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'round winner_5': [0], 'glenn seton_6': [0], '4_7': [2]} | ['circuit', 'location / state', 'date', 'round winner', 'team'] | [['sandown international raceway', 'melbourne , victoria', '3 - 5 feb', 'larry perkins', 'castrol perkins motorsport'], ['symmons plains raceway', 'launceston , tasmania', '24 - 26 feb', 'john bowe', 'dick johnson racing'], ['mount panorama circuit', 'bathurst , new south wales', '10 - 12 mar', 'john bowe', 'dick johnson racing'], ['phillip island grand prix circuit', 'phillip island , victoria', '7 - 9 apr', 'glenn seton', 'glenn seton racing'], ['lakeside international raceway', 'brisbane , queensland', '21 - 23 apr', 'glenn seton', 'glenn seton racing'], ['winton motor raceway', 'benalla , victoria', '19 - 21 may', 'john bowe', 'dick johnson racing'], ['eastern creek raceway', 'sydney , new south wales', '26 - 28 may', 'mark skaife', 'gibson motor sport'], ['mallala motor sport park', 'mallala , south australia', '7 - 9 jul', 'glenn seton', 'glenn seton racing'], ['barbagallo raceway', 'perth , western australia', '14 - 16 jul', 'glenn seton', 'glenn seton racing'], ['oran park raceway', 'sydney , new south wales', '4 - 6 aug', 'john bowe', 'dick johnson racing']] |
catanduanes | https://en.wikipedia.org/wiki/Catanduanes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-255829-1.html.csv | superlative | caramoran is the municipality with the highest hectares of area in catanduanes . | {'scope': 'all', 'col_superlative': '3', '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', 'area ( hectares )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; area ( hectares ) }'}, 'municipality'], 'result': 'caramoran', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; area ( hectares ) } ; municipality }'}, 'caramoran'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; area ( hectares ) } ; municipality } ; caramoran } = true', 'tointer': 'select the row whose area ( hectares ) record of all rows is maximum . the municipality record of this row is caramoran .'} | eq { hop { argmax { all_rows ; area ( hectares ) } ; municipality } ; caramoran } = true | select the row whose area ( hectares ) record of all rows is maximum . the municipality record of this row is caramoran . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'area (hectares)_5': 5, 'municipality_6': 6, 'caramoran_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'area (hectares)_5': 'area ( hectares )', 'municipality_6': 'municipality', 'caramoran_7': 'caramoran'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'area (hectares)_5': [0], 'municipality_6': [1], 'caramoran_7': [2]} | ['municipality', 'no of barangays', 'area ( hectares )', 'population ( 2007 )', 'population ( 2010 )', 'pop density ( per km 2 )'] | [['bagamanoc', '18', '8074', '10183', '11370', '140.8'], ['baras', '29', '10950', '11787', '12243', '111.8'], ['bato', '27', '4862', '18738', '19984', '411.0'], ['caramoran', '27', '26374', '25618', '28063', '106.4'], ['gigmoto', '9', '18182', '7569', '8003', '44.0'], ['pandan', '26', '11990', '19005', '19393', '161.7'], ['panganiban ( payo )', '23', '7996', '9290', '9738', '121.8'], ['san andres ( calolbon )', '38', '16731', '33781', '35779', '213.8'], ['san miguel', '24', '12994', '12966', '14107', '108.6'], ['viga', '31', '15823', '19266', '20669', '130.6']] |
1992 - 93 vancouver canucks season | https://en.wikipedia.org/wiki/1992%E2%80%9393_Vancouver_Canucks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11128774-6.html.csv | unique | the vancouver canucks scored 8 points in only one game in february 1993 . | {'scope': 'all', 'row': '9', 'col': '3', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': '8 -', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '8 -'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 8 - .', 'tostr': 'filter_eq { all_rows ; score ; 8 - }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; score ; 8 - } }', 'tointer': 'select the rows whose score record fuzzily matches to 8 - . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '8 -'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 8 - .', 'tostr': 'filter_eq { all_rows ; score ; 8 - }'}, 'date'], 'result': 'february 22', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; score ; 8 - } ; date }'}, 'february 22'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; 8 - } ; date } ; february 22 }', 'tointer': 'the date record of this unqiue row is february 22 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; score ; 8 - } } ; eq { hop { filter_eq { all_rows ; score ; 8 - } ; date } ; february 22 } } = true', 'tointer': 'select the rows whose score record fuzzily matches to 8 - . there is only one such row in the table . the date record of this unqiue row is february 22 .'} | and { only { filter_eq { all_rows ; score ; 8 - } } ; eq { hop { filter_eq { all_rows ; score ; 8 - } ; date } ; february 22 } } = true | select the rows whose score record fuzzily matches to 8 - . there is only one such row in the table . the date record of this unqiue row is february 22 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'score_7': 7, '8 -_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'february 22_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'score_7': 'score', '8 -_8': '8 -', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'february 22_10': 'february 22'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'score_7': [0], '8 -_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'february 22_10': [3]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['february 1', 'minnesota', '5 - 4', 'vancouver', 'mclean', '14830', '29 - 15 - 8'], ['february 3', 'tampa bay', '2 - 4', 'vancouver', 'whitmore', '14171', '30 - 15 - 8'], ['february 9', 'vancouver', '5 - 1', 'quebec', 'mclean', '14360', '31 - 15 - 8'], ['february 11', 'vancouver', '2 - 5', 'toronto', 'mclean', '15720', '31 - 16 - 8'], ['february 12', 'vancouver', '3 - 1', 'buffalo', 'whitmore', '16325', '32 - 16 - 8'], ['february 15', 'vancouver', '0 - 3', 'los angeles', 'mclean', '16005', '32 - 17 - 8'], ['february 18', 'philadelphia', '3 - 2', 'vancouver', 'whitmore', '16150', '32 - 18 - 8'], ['february 20', 'winnipeg', '2 - 4', 'vancouver', 'mclean', '16150', '33 - 18 - 8'], ['february 22', 'toronto', '8 - 1', 'vancouver', 'mclean', '16150', '33 - 19 - 8'], ['february 24', 'ny rangers', '4 - 5', 'vancouver', 'whitmore', '16150', '34 - 19 - 8'], ['february 26', 'vancouver', '7 - 4', 'winnipeg', 'mclean', '15398', '35 - 19 - 8']] |
geography of the european union | https://en.wikipedia.org/wiki/Geography_of_the_European_Union | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1653499-1.html.csv | aggregation | the selected major cities in the european union have an average urban area population of 5.1 million . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '5.1', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'population urban area in millions'], 'result': '5.1', 'ind': 0, 'tostr': 'avg { all_rows ; population urban area in millions }'}, '5.1'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; population urban area in millions } ; 5.1 } = true', 'tointer': 'the average of the population urban area in millions record of all rows is 5.1 .'} | round_eq { avg { all_rows ; population urban area in millions } ; 5.1 } = true | the average of the population urban area in millions record of all rows is 5.1 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'population urban area in millions_4': 4, '5.1_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'population urban area in millions_4': 'population urban area in millions', '5.1_5': '5.1'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'population urban area in millions_4': [0], '5.1_5': [1]} | ['city proper', 'population city limits in millions', 'density per km square', 'urban area', 'population urban area in millions', 'metro area', 'population metro area in millions'] | [['london , uk', '7.5', '4761', 'paris , france', '10.1', 'london , uk', '12 - 14'], ['berlin , germany', '3.4', '3815', 'london , uk', '8.5', 'paris , france', '11.7'], ['madrid , spain', '3.1', '1985', 'madrid , spain', '5.5', 'rhine - ruhr , germany', '10.2'], ['rome , italy', '2.7', '5198', 'ruhr , germany', '5.3', 'randstad , netherlands', '7.0'], ['paris , france', '2.2', '24672', 'barcelona , spain', '4.5', 'madrid , spain', '5.8'], ['bucharest , romania', '1.9', '9131', 'milan , italy', '3.8', 'barcelona , spain', '5.3'], ['hamburg , germany', '1.8', '2310', 'berlin , germany', '3.7', 'milan , italy', '4.3'], ['warsaw , poland', '1.7', '3258', 'rotterdam - the hague , netherlands', '3.3', 'berlin , germany', '4.3'], ['budapest , hungary', '1 , 7', '3570', 'athens , greece', '3.2', 'frankfurt rhine - main , germany', '4.1'], ['vienna , austria', '1.7', '3931', 'naples , italy', '2.9', 'athens , greece', '3.9']] |
1988 green bay packers season | https://en.wikipedia.org/wiki/1988_Green_Bay_Packers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14650373-1.html.csv | unique | the only packer 's pick in 1988 that was a safety was chuck cecil . | {'scope': 'all', 'row': '5', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'safety', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'safety'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to safety .', 'tostr': 'filter_eq { all_rows ; position ; safety }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; position ; safety } }', 'tointer': 'select the rows whose position record fuzzily matches to safety . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'safety'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to safety .', 'tostr': 'filter_eq { all_rows ; position ; safety }'}, 'player'], 'result': 'chuck cecil', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; safety } ; player }'}, 'chuck cecil'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; position ; safety } ; player } ; chuck cecil }', 'tointer': 'the player record of this unqiue row is chuck cecil .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; position ; safety } } ; eq { hop { filter_eq { all_rows ; position ; safety } ; player } ; chuck cecil } } = true', 'tointer': 'select the rows whose position record fuzzily matches to safety . there is only one such row in the table . the player record of this unqiue row is chuck cecil .'} | and { only { filter_eq { all_rows ; position ; safety } } ; eq { hop { filter_eq { all_rows ; position ; safety } ; player } ; chuck cecil } } = true | select the rows whose position record fuzzily matches to safety . there is only one such row in the table . the player record of this unqiue row is chuck cecil . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'position_7': 7, 'safety_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'chuck cecil_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'position_7': 'position', 'safety_8': 'safety', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'chuck cecil_10': 'chuck cecil'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'position_7': [0], 'safety_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'chuck cecil_10': [3]} | ['pick', 'nfl team', 'player', 'position', 'college'] | [['7', 'green bay packers', 'sterling sharpe', 'wide receiver', 'south carolina'], ['34', 'green bay packers', 'shawn patterson', 'defensive end', 'arizona state'], ['61', 'green bay packers', 'keith woodside', 'running back', 'texas a & m'], ['88', 'green bay packers', 'rollin putzier', 'nose tackle', 'oregon'], ['89', 'green bay packers', 'chuck cecil', 'safety', 'arizona'], ['144', 'green bay packers', 'nate hill', 'defensive end', 'auburn'], ['173', 'green bay packers', 'gary richard', 'cornerback', 'pittsburgh'], ['200', 'green bay packers', 'patrick collins', 'running back', 'oklahoma']] |
1996 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1996_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162199-5.html.csv | superlative | tom lehman had the lowest total score value in the 1996 u.s. open golf tournament . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'score'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; score }'}, 'player'], 'result': 'tom lehman', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; score } ; player }'}, 'tom lehman'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; score } ; player } ; tom lehman } = true', 'tointer': 'select the row whose score record of all rows is minimum . the player record of this row is tom lehman .'} | eq { hop { argmin { all_rows ; score } ; player } ; tom lehman } = true | select the row whose score record of all rows is minimum . the player record of this row is tom lehman . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'score_5': 5, 'player_6': 6, 'tom lehman_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'score_5': 'score', 'player_6': 'player', 'tom lehman_7': 'tom lehman'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'score_5': [0], 'player_6': [1], 'tom lehman_7': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'tom lehman', 'united states', '71 + 72 + 65 = 208', '- 2'], ['2', 'steve jones', 'united states', '74 + 66 + 69 = 209', '- 1'], ['t3', 'davis love iii', 'united states', '71 + 69 + 70 = 210', 'e'], ['t3', 'john morse', 'united states', '68 + 74 + 68 = 210', 'e'], ['t3', 'frank nobilo', 'new zealand', '69 + 71 + 70 = 210', 'e'], ['t6', 'woody austin', 'united states', '67 + 72 + 72 = 211', '+ 1'], ['t6', 'ernie els', 'south africa', '72 + 67 + 72 = 211', '+ 1'], ['t6', 'jim furyk', 'united states', '72 + 69 + 70 = 211', '+ 1'], ['t6', 'colin montgomerie', 'scotland', '70 + 72 + 69 = 211', '+ 1'], ['t6', 'sam torrance', 'scotland', '71 + 69 + 71 = 211', '+ 1']] |
list of ultras of oceania | https://en.wikipedia.org/wiki/List_of_Ultras_of_Oceania | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18946749-5.html.csv | comparative | mount tabwemasana has a higher elevation in meters than mount veve has . | {'row_1': '3', 'row_2': '5', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'peak', 'mount tabwemasana'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose peak record fuzzily matches to mount tabwemasana .', 'tostr': 'filter_eq { all_rows ; peak ; mount tabwemasana }'}, 'elevation ( m )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; peak ; mount tabwemasana } ; elevation ( m ) }', 'tointer': 'select the rows whose peak record fuzzily matches to mount tabwemasana . take the elevation ( m ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'peak', 'mount veve'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose peak record fuzzily matches to mount veve .', 'tostr': 'filter_eq { all_rows ; peak ; mount veve }'}, 'elevation ( m )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; peak ; mount veve } ; elevation ( m ) }', 'tointer': 'select the rows whose peak record fuzzily matches to mount veve . take the elevation ( m ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; peak ; mount tabwemasana } ; elevation ( m ) } ; hop { filter_eq { all_rows ; peak ; mount veve } ; elevation ( m ) } } = true', 'tointer': 'select the rows whose peak record fuzzily matches to mount tabwemasana . take the elevation ( m ) record of this row . select the rows whose peak record fuzzily matches to mount veve . take the elevation ( m ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; peak ; mount tabwemasana } ; elevation ( m ) } ; hop { filter_eq { all_rows ; peak ; mount veve } ; elevation ( m ) } } = true | select the rows whose peak record fuzzily matches to mount tabwemasana . take the elevation ( m ) record of this row . select the rows whose peak record fuzzily matches to mount veve . take the elevation ( m ) 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, 'peak_7': 7, 'mount tabwemasana_8': 8, 'elevation (m)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'peak_11': 11, 'mount veve_12': 12, 'elevation (m)_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', 'peak_7': 'peak', 'mount tabwemasana_8': 'mount tabwemasana', 'elevation (m)_9': 'elevation ( m )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'peak_11': 'peak', 'mount veve_12': 'mount veve', 'elevation (m)_13': 'elevation ( m )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'peak_7': [0], 'mount tabwemasana_8': [0], 'elevation (m)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'peak_11': [1], 'mount veve_12': [1], 'elevation (m)_13': [3]} | ['rank', 'peak', 'country', 'island', 'elevation ( m )', 'col ( m )'] | [['1', 'mount popomanaseu', 'solomon islands', 'guadalcanal', '2335', '0'], ['2', 'mont orohena', 'french polynesia', 'tahiti', '2241', '0'], ['3', 'mount tabwemasana', 'vanuatu', 'espiritu santo', '1879', '0'], ['4', 'silisili', 'samoa', "savai'i", '1858', '0'], ['5', 'mount veve', 'solomon islands', 'kolombangara', '1768', '0'], ['6', 'mont paniã', 'new caledonia', 'grande terre', '1628', '0']] |
1993 tampa bay buccaneers season | https://en.wikipedia.org/wiki/1993_Tampa_Bay_Buccaneers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11452712-1.html.csv | comparative | in the 1993 tampa bay buccaneers season , rudy harris was picked one round after john lynch . | {'row_1': '5', 'row_2': '4', 'col': '2', 'col_other': '3', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'rudy harris'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to rudy harris .', 'tostr': 'filter_eq { all_rows ; player ; rudy harris }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; rudy harris } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to rudy harris . take the round record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'john lynch'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to john lynch .', 'tostr': 'filter_eq { all_rows ; player ; john lynch }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; john lynch } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to john lynch . take the round record of this row .'}], 'result': '1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; player ; rudy harris } ; round } ; hop { filter_eq { all_rows ; player ; john lynch } ; round } }'}, '1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; player ; rudy harris } ; round } ; hop { filter_eq { all_rows ; player ; john lynch } ; round } } ; 1 } = true', 'tointer': 'select the rows whose player record fuzzily matches to rudy harris . take the round record of this row . select the rows whose player record fuzzily matches to john lynch . take the round record of this row . the first record is 1 larger than the second record .'} | eq { diff { hop { filter_eq { all_rows ; player ; rudy harris } ; round } ; hop { filter_eq { all_rows ; player ; john lynch } ; round } } ; 1 } = true | select the rows whose player record fuzzily matches to rudy harris . take the round record of this row . select the rows whose player record fuzzily matches to john lynch . take the round record of this row . the first record is 1 larger than the second record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'player_8': 8, 'rudy harris_9': 9, 'round_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'player_12': 12, 'john lynch_13': 13, 'round_14': 14, '1_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'player_8': 'player', 'rudy harris_9': 'rudy harris', 'round_10': 'round', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'player_12': 'player', 'john lynch_13': 'john lynch', 'round_14': 'round', '1_15': '1'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'player_8': [0], 'rudy harris_9': [0], 'round_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'player_12': [1], 'john lynch_13': [1], 'round_14': [3], '1_15': [5]} | ['pick', 'round', 'player', 'position', 'school'] | [['6', 'round 1', 'eric curry', 'defensive end', 'alabama'], ['34', 'round 2', 'demetrius dubose', 'linebacker', 'notre dame'], ['60', 'round 3', 'lamar thomas', 'wide receiver', 'miami'], ['82', 'round 3', 'john lynch', 'defensive back', 'stanford'], ['91', 'round 4', 'rudy harris', 'running back', 'clemson'], ['104', 'round 4', 'horace copeland', 'wide receiver', 'miami'], ['145', 'round 6', 'chidi ahanotu', 'defensive tackle', 'california'], ['176', 'round 7', 'tyree davis', 'wide receiver', 'central arkansas'], ['220', 'round 8', 'darrick branch', 'wide receiver', 'hawaii'], ['224', 'round 8', 'daron alcorn', 'kicker', 'akron']] |
1998 icc knockout trophy | https://en.wikipedia.org/wiki/1998_ICC_KnockOut_Trophy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11950720-8.html.csv | unique | the only left handed batter at the 1998 icc knockout trophy from windward islands was nixon mclean . | {'scope': 'subset', 'row': '10', 'col': '6', 'col_other': '2,4', 'criterion': 'equal', 'value': 'windward islands', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'left hand bat'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'batting style', 'left hand bat'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; batting style ; left hand bat }', 'tointer': 'select the rows whose batting style record fuzzily matches to left hand bat .'}, 'first class team', 'windward islands'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose batting style record fuzzily matches to left hand bat . among these rows , select the rows whose first class team record fuzzily matches to windward islands .', 'tostr': 'filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } }', 'tointer': 'select the rows whose batting style record fuzzily matches to left hand bat . among these rows , select the rows whose first class team record fuzzily matches to windward islands . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'batting style', 'left hand bat'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; batting style ; left hand bat }', 'tointer': 'select the rows whose batting style record fuzzily matches to left hand bat .'}, 'first class team', 'windward islands'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose batting style record fuzzily matches to left hand bat . among these rows , select the rows whose first class team record fuzzily matches to windward islands .', 'tostr': 'filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands }'}, 'player'], 'result': 'nixon mclean', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } ; player }'}, 'nixon mclean'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } ; player } ; nixon mclean }', 'tointer': 'the player record of this unqiue row is nixon mclean .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } } ; eq { hop { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } ; player } ; nixon mclean } } = true', 'tointer': 'select the rows whose batting style record fuzzily matches to left hand bat . among these rows , select the rows whose first class team record fuzzily matches to windward islands . there is only one such row in the table . the player record of this unqiue row is nixon mclean .'} | and { only { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } } ; eq { hop { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } ; player } ; nixon mclean } } = true | select the rows whose batting style record fuzzily matches to left hand bat . among these rows , select the rows whose first class team record fuzzily matches to windward islands . there is only one such row in the table . the player record of this unqiue row is nixon mclean . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'batting style_8': 8, 'left hand bat_9': 9, 'first class team_10': 10, 'windward islands_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'player_12': 12, 'nixon mclean_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'batting style_8': 'batting style', 'left hand bat_9': 'left hand bat', 'first class team_10': 'first class team', 'windward islands_11': 'windward islands', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'player_12': 'player', 'nixon mclean_13': 'nixon mclean'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'batting style_8': [0], 'left hand bat_9': [0], 'first class team_10': [1], 'windward islands_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'player_12': [3], 'nixon mclean_13': [4]} | ['no', 'player', 'date of birth', 'batting style', 'bowling style', 'first class team'] | [['59', 'brian lara ( captain )', '2 may 1969', 'left hand bat', 'right arm leg break googly', 'trinidad and tobago'], ['55', 'keith arthurton', '21 february 1965', 'left hand bat', 'left arm orthodox spin', 'leeward islands'], ['66', 'shivnarine chanderpaul', '16 august 1974', 'left hand bat', 'right arm leg break', 'guyana'], ['86', 'mervyn dillon', '5 june 1974', 'right hand bat', 'right arm fast - medium', 'trinidad and tobago'], ['50', 'carl hooper', '15 december 1966', 'right hand bat', 'right arm off break', 'guyana'], ['76', 'ridley jacobs ( wicket - keeper )', '26 november 1967', 'left hand bat', 'wicket - keeper', 'leeward islands'], ['89', 'reon king', '6 october 1975', 'right hand bat', 'right arm fast - medium', 'guyana'], ['58', 'clayton lambert', '10 february 1962', 'left hand bat', 'right arm off break', 'guyana'], ['85', 'rawl lewis', '5 september 1974', 'right hand bat', 'right arm leg break googly', 'windward islands'], ['78', 'nixon mclean', '20 july 1973', 'left hand bat', 'right arm fast', 'windward islands'], ['87', 'neil mcgarrell', '12 july 1972', 'right hand bat', 'left arm orthodox spin', 'guyana'], ['51', 'phil simmons', '18 april 1963', 'right hand bat', 'right arm medium', 'trinidad and tobago'], ['61', 'philo wallace', '2 august 1970', 'right hand bat', 'right arm medium', 'barbados'], ['68', 'stuart williams', '12 august 1969', 'right hand bat', 'right arm medium', 'leeward islands']] |
balloon satellite | https://en.wikipedia.org/wiki/Balloon_satellite | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2150068-1.html.csv | superlative | out of the balloon satellites listed , the echo 2 has the highest mass at 256 kg . | {'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', 'mass ( kg )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; mass ( kg ) }'}, 'satellite'], 'result': 'echo 2', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; mass ( kg ) } ; satellite }'}, 'echo 2'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; mass ( kg ) } ; satellite } ; echo 2 } = true', 'tointer': 'select the row whose mass ( kg ) record of all rows is maximum . the satellite record of this row is echo 2 .'} | eq { hop { argmax { all_rows ; mass ( kg ) } ; satellite } ; echo 2 } = true | select the row whose mass ( kg ) record of all rows is maximum . the satellite record of this row is echo 2 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'mass (kg)_5': 5, 'satellite_6': 6, 'echo 2_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'mass (kg)_5': 'mass ( kg )', 'satellite_6': 'satellite', 'echo 2_7': 'echo 2'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'mass (kg)_5': [0], 'satellite_6': [1], 'echo 2_7': [2]} | ['satellite', 'launch date ( utc )', 'decay', 'mass ( kg )', 'diameter ( m )', 'nssdc id', 'nation', 'usage'] | [['echo 1', '1960 - 08 - 12 09:36:00', '1968 - 05 - 24', '180', '30.48', '1960 - 009a', 'us', 'pcr , ado , spc , tri'], ['explorer 9', '1961 - 02 - 16 13:12:00', '1964 - 04 - 09', '36', '3.66', '1961 - 004a', 'us', 'ado'], ['explorer 19 ( ad - a )', '1963 - 12 - 19 18:43:00', '1981 - 10 - 05', '7.7', '3.66', '1963 - 053a', 'us', 'ado'], ['echo 2', '1964 - 01 - 25 13:55:00', '1969 - 06 - 07', '256', '41', '1964 - 004a', 'us', 'pcr , tri'], ['explorer 24 ( ad - b )', '1964 - 11 - 21 17:17:00', '1968 - 10 - 18', '8.6', '3.6', '1964 - 076a', 'us', 'ado'], ['pageos 1', '1966 - 06 - 24 00:14:00', '1975 - 07 - 12', '56.7', '30.48', '1966 - 056a', 'us', 'tri'], ['explorer 39 ( ad - c )', '1968 - 08 - 08 20:12:00', '1981 - 06 - 22', '9.4', '3.6', '1968 - 066a', 'us', 'ado'], ['mylar balloon', '1971 - 08 - 07 00:11:00', '1981 - 09 - 01', '0.8', '2.13', '1971 - 067f', 'us', 'ado'], ['qi qiu weixing 1', '1990 - 09 - 03 00:53:00', '1991 - 03 - 11', '4', '3', '1990 - 081b', 'prc', 'ado'], ['qi qiu weixing 2', '1990 - 09 - 03 00:53:00', '1991 - 07 - 24', '4', '2.5', '1990 - 081c', 'prc', 'ado']] |
united states house of representatives elections , 1974 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1974 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341690-13.html.csv | count | six of the incumbent candidates were re-elected in the 1974 election . | {'scope': 'all', 'criterion': 'equal', 'value': 're-elected', 'result': '6', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 're-elected'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to re-elected .', 'tostr': 'filter_eq { all_rows ; result ; re-elected }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; re-elected } }', 'tointer': 'select the rows whose result record fuzzily matches to re-elected . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; re-elected } } ; 6 } = true', 'tointer': 'select the rows whose result record fuzzily matches to re-elected . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; result ; re-elected } } ; 6 } = true | select the rows whose result record fuzzily matches to re-elected . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 're-elected_6': 6, '6_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 're-elected_6': 're-elected', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 're-elected_6': [0], '6_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['illinois 3', 'robert p hanrahan', 'republican', '1972', 'lost re - election democratic gain', 'marty russo ( d ) 52.6 % robert p hanrahan ( r ) 47.4 %'], ['illinois 4', 'ed derwinski', 'republican', '1958', 're - elected', 'ed derwinski ( r ) 59.2 % ronald a rodger ( d ) 40.8 %'], ['illinois 6', 'harold r collier', 'republican', '1956', 'retired republican hold', 'henry hyde ( r ) 53.4 % edward v hanrahan ( d ) 46.6 %'], ['illinois 9', 'sidney r yates', 'democratic', '1964', 're - elected', 'sidney r yates ( d ) unopposed'], ['illinois 10', 'samuel h young', 'republican', '1972', 'lost re - election democratic gain', 'abner j mikva ( d ) 50.9 % samuel h young ( r ) 49.1 %'], ['illinois 12', 'phil crane', 'republican', '1969', 're - elected', 'phil crane ( r ) 61.1 % betty c spence ( d ) 38.9 %'], ['illinois 19', 'tom railsback', 'republican', '1966', 're - elected', 'tom railsback ( r ) 65.3 % jim gende ( d ) 34.7 %'], ['illinois 20', 'paul findley', 'republican', '1960', 're - elected', 'paul findley ( r ) 54.8 % peter f mack ( d ) 45.2 %'], ['illinois 23', 'melvin price', 'democratic', '1944', 're - elected', 'melvin price ( d ) 80.5 % scott randolph ( r ) 19.5 %']] |
head of the river ( queensland ) | https://en.wikipedia.org/wiki/Head_of_the_River_%28Queensland%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11318462-36.html.csv | count | for the year 10 1st quad , a total of six races were won by som . | {'scope': 'all', 'criterion': 'equal', 'value': 'som', 'result': '6', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year 10 1st quad', 'som'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year 10 1st quad record fuzzily matches to som .', 'tostr': 'filter_eq { all_rows ; year 10 1st quad ; som }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; year 10 1st quad ; som } }', 'tointer': 'select the rows whose year 10 1st quad record fuzzily matches to som . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; year 10 1st quad ; som } } ; 6 } = true', 'tointer': 'select the rows whose year 10 1st quad record fuzzily matches to som . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; year 10 1st quad ; som } } ; 6 } = true | select the rows whose year 10 1st quad record fuzzily matches to som . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'year 10 1st quad_5': 5, 'som_6': 6, '6_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'year 10 1st quad_5': 'year 10 1st quad', 'som_6': 'som', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'year 10 1st quad_5': [0], 'som_6': [0], '6_7': [2]} | ['crew', 'year 10 1st quad', 'year 10 2nd quad', 'year 10 3rd quad', 'year 10 4th quad', 'year 10 single scull', 'year 9 1st quad', 'year 9 2nd quad', 'year 9 3rd quad', 'year 9 4th quad', 'year 9 single scull', 'year 8 1st quad', 'year 8 2nd quad', 'year 8 3rd quad', 'year 8 4th quad', 'year 8 single scull'] | [['1999', 'stm', 'stm', 'stm', 'som', 'stm', 'bshs', 'sth', 'stm', 'stm', 'som', 'stm', 'stm', 'stu', 'stu', 'stm'], ['2000', 'bshs', 'som', 'stm', 'stm', 'no race', 'stm', 'stm', 'stm', 'stu', 'no race', 'stm', 'som', 'stm', 'stm', 'no race'], ['2001', 'som', 'stm', 'stm', 'stu', 'stm', 'som', 'stm', 'stm', 'som', 'stm', 'stm', 'stm', 'sta', 'stu', 'stm'], ['2002', 'sta', 'stm', 'som', 'som', 'lhc', 'stm', 'sta', 'sta', 'stu', 'stm', 'som', 'som', 'som', 'sta', 'som'], ['2003', 'stm', 'som', 'som', 'stm', 'stm', 'stm', 'som', 'som', 'som', 'som', 'som', 'stu', 'stm', 'som', 'splc'], ['2004', 'som', 'som', 'stm', 'som', 'som', 'stm', 'som', 'sta', 'sta', 'splc', 'som', 'sta', 'som', 'sta', 'som'], ['2005', 'som', 'stm', 'sta', 'sta', 'splc', 'bggs', 'stu', 'stm', 'som', 'sta', 'bggs', 'splc', 'bggs', 'sta', 'stm'], ['2006', 'splc', 'stm', 'bggs', 'bggs', 'splc', 'stm', 'bggs', 'sta', 'bggs', 'stm', 'som', 'som', 'sta', 'bggs', 'sth'], ['2007', 'stm', 'bggs', 'sta', 'sta', 'stm', 'bggs', 'bggs', 'stm', 'bggs', 'sth', 'sta', 'ahs', 'stu', 'bggs', 'ahs'], ['2008', 'sth', 'stm', 'splc', 'som', 'splc', 'sta', 'stu', 'stu', 'stu', 'ahs', 'som', 'som', 'som', 'som', 'sth'], ['2009', 'sta', 'stm', 'stu', 'som', 'som', 'sta', 'som', 'som', 'som', 'som', 'sta', 'ahs', 'stu', 'stu', 'som'], ['2010', 'som', 'som', 'som', 'som', 'som', 'bggs', 'ahs', 'som', 'som', 'som', 'stm', 'som', 'stm', 'stm', 'stm'], ['2011', 'som', 'som', 'ahs', 'som', 'splc', 'stm', 'stm', 'stm', 'stm', 'stm', 'som', 'sth', 'stm', 'som', 'splc'], ['2012', 'som', 'som', 'stm', 'ahs', 'som', 'som', 'som', 'sth', 'splc', 'splc', 'stm', 'splc', 'stu', 'stm', 'stm'], ['2013', 'ahs', 'som', 'sth', 'splc', 'splc', 'som', 'stm', 'sth', 'sth', 'splc', 'stm', 'som', 'som', 'som', 'stm']] |
1972 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1972_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17245554-1.html.csv | count | in the 1972 u.s. open ( golf ) , among the players united states , 3 of them won 300 or more points . | {'scope': 'subset', 'criterion': 'greater_than_eq', 'value': '300', 'result': '3', 'col': '4', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record fuzzily matches to united states .'}, 'total', '300'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than or equal to 300 .', 'tostr': 'filter_greater_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_greater_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than or equal to 300 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } } ; 3 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than or equal to 300 . the number of such rows is 3 .'} | eq { count { filter_greater_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } } ; 3 } = true | select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than or equal to 300 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'country_6': 6, 'united states_7': 7, 'total_8': 8, '300_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_eq_1': 'filter_greater_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'country_6': 'country', 'united states_7': 'united states', 'total_8': 'total', '300_9': '300', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'country_6': [0], 'united states_7': [0], 'total_8': [1], '300_9': [1], '3_10': [3]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['jack nicklaus', 'united states', '1962 , 1967', '290', '+ 2', '1'], ['arnold palmer', 'united states', '1960', '294', '+ 6', '3'], ['lee trevino', 'united states', '1968 , 1971', '295', '+ 7', 't4'], ['billy casper', 'united states', '1959 , 1966', '300', '+ 12', 't11'], ['orville moody', 'united states', '1969', '301', '+ 13', 't15'], ['gary player', 'south africa', '1965', '301', '+ 13', 't15'], ['julius boros', 'united states', '1952 , 1963', '305', '+ 17', 't29'], ['tony jacklin', 'england', '1970', '307', '+ 19', 't40']] |
1998 masters tournament | https://en.wikipedia.org/wiki/1998_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16514546-2.html.csv | majority | most players of the 1998 masters tournament were from the united states . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { all_rows ; country ; united states } = true'} | most_eq { all_rows ; country ; united states } = true | for the country records of all rows , most of them fuzzily match to united states . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united states_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united states_4': 'united states'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united states_4': [0]} | ['place', 'player', 'country', 'score', 'to par'] | [['t1', 'fred couples', 'united states', '69 + 70 = 139', '- 5'], ['t1', 'david duval', 'united states', '71 + 68 = 139', '- 5'], ['3', 'scott hoch', 'united states', '70 + 71 = 141', '- 3'], ['t4', 'paul azinger', 'united states', '71 + 72 = 143', '- 1'], ['t4', 'jay haas', 'united states', '72 + 71 = 143', '- 1'], ['t4', 'phil mickelson', 'united states', '74 + 69 = 143', '- 1'], ['t4', 'josé maría olazábal', 'spain', '70 + 73 = 143', '- 1'], ['t4', 'tiger woods', 'united states', '71 + 72 = 143', '- 1'], ['t9', 'scott mccarron', 'united states', '73 + 71 = 144', 'e'], ['t9', "mark o'meara", 'united states', '74 + 70 = 144', 'e']] |
united states house of representatives elections , 1904 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1904 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1365810-4.html.csv | count | among the incumbents that were re-elected during united states house of representatives elections , 1904 , two of them were first elected in 1902 . | {'scope': 'subset', 'criterion': 'equal', 'value': '1902', 'result': '2', 'col': '4', 'subset': {'col': '5', 'criterion': 'equal', 'value': 're - elected'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 're - elected'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; re - elected }', 'tointer': 'select the rows whose result record fuzzily matches to re - elected .'}, 'first elected', '1902'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1902 .', 'tostr': 'filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1902 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1902 } }', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1902 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1902 } } ; 2 } = true', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1902 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1902 } } ; 2 } = true | select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1902 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'result_6': 6, 're - elected_7': 7, 'first elected_8': 8, '1902_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'result_6': 'result', 're - elected_7': 're - elected', 'first elected_8': 'first elected', '1902_9': '1902', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'result_6': [0], 're - elected_7': [0], 'first elected_8': [1], '1902_9': [1], '2_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result'] | [['south carolina 1', 'george swinton legarã', 'democratic', '1902', 're - elected'], ['south carolina 2', 'george w croft', 'democratic', '1902', 'retired democratic hold'], ['south carolina 3', 'wyatt aiken', 'democratic', '1902', 're - elected'], ['south carolina 4', 'joseph t johnson', 'democratic', '1900', 're - elected'], ['south carolina 5', 'david e finley', 'democratic', '1898', 're - elected'], ['south carolina 6', 'robert b scarborough', 'democratic', '1900', 'retired democratic hold'], ['south carolina 7', 'asbury f lever', 'democratic', '1901 ( special )', 're - elected']] |
looney tunes and merrie melodies filmography ( 1929 - 39 ) | https://en.wikipedia.org/wiki/Looney_Tunes_and_Merrie_Melodies_filmography_%281929%E2%80%9339%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18792938-2.html.csv | majority | the majority of 1929-39 looney tunes and merrie melodies films were with the lt series . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'lt', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'series', 'lt'], 'result': True, 'ind': 0, 'tointer': 'for the series records of all rows , most of them fuzzily match to lt .', 'tostr': 'most_eq { all_rows ; series ; lt } = true'} | most_eq { all_rows ; series ; lt } = true | for the series records of all rows , most of them fuzzily match to lt . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'series_3': 3, 'lt_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'series_3': 'series', 'lt_4': 'lt'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'series_3': [0], 'lt_4': [0]} | ['title', 'series', 'characters', 'production num', 'release date'] | [['big man from the north', 'lt', 'bosko , honey', '4500', '1931 - 01 - xx'], ["ai n't nature grand !", 'lt', 'bosko', '4626', '1931 - 03 - xx'], ["ups 'n downs", 'lt', 'bosko', '4640', '1931 - 03 - xx'], ['dumb patrol', 'lt', 'bosko , honey', '4664', '1931 - 05 - xx'], ['yodeling yokels', 'lt', 'bosko , honey', '4680', '1931 - 06 - xx'], ["bosko 's holiday", 'lt', 'bosko , honey', '4694', '1931 - 07 - xx'], ["the tree 's knees", 'lt', 'bosko', '4725', '1931 - 07 - xx'], ['lady , play your mandolin !', 'mm', 'animals ( cartoon character ) , foxy , roxy', '4645', '1931 - 08 - xx'], ['smile , darn ya , smile !', 'mm', 'foxy , radio , roxy', '4825', '1931 - 09 - 05'], ['bosko shipwrecked', 'lt', 'bosko', '4666', '1931 - 09 - 19'], ['one more time', 'mm', 'foxy , mugs , roxy', '4851', '1931 - 10 - 03'], ['bosko the doughboy', 'lt', 'bosko', '5017', '1931 - 10 - 17'], ["you do n't know what you 're doin '", 'mm', 'fluffy , piggy , the car', '4977', '1931 - 10 - 31'], ["bosko 's soda fountain", 'lt', 'bosko', '5045', '1931 - 11 - 14'], ["hittin ' the trail for hallelujah land", 'mm', 'banjo player , fluffy', '5025', '11 / 28 / 31'], ["bosko 's fox hunt", 'lt', 'bosko , bruno', '5046', '1931 - 12 - 12'], ['red - headed baby', 'mm', 'red - headed baby , toymaker', '5038', '1931 - 12 - 26']] |
1987 - 88 fa cup | https://en.wikipedia.org/wiki/1987%E2%80%9388_FA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17751827-5.html.csv | unique | the game between port vale and watford was the only 0-0 score game in the 1987 - 88 fa cup . | {'scope': 'all', 'row': '7', 'col': '3', 'col_other': '2,4', 'criterion': 'equal', 'value': '0 - 0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '0 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 0 - 0 .', 'tostr': 'filter_eq { all_rows ; score ; 0 - 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; score ; 0 - 0 } }', 'tointer': 'select the rows whose score record fuzzily matches to 0 - 0 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '0 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 0 - 0 .', 'tostr': 'filter_eq { all_rows ; score ; 0 - 0 }'}, 'home team'], 'result': 'port vale', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; score ; 0 - 0 } ; home team }'}, 'port vale'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; home team } ; port vale }', 'tointer': 'the home team record of this unqiue row is port vale .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'score', '0 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record fuzzily matches to 0 - 0 .', 'tostr': 'filter_eq { all_rows ; score ; 0 - 0 }'}, 'away team'], 'result': 'watford', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; score ; 0 - 0 } ; away team }'}, 'watford'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; away team } ; watford }', 'tointer': 'the away team record of this unqiue row is watford .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; home team } ; port vale } ; eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; away team } ; watford } }', 'tointer': 'the home team record of this unqiue row is port vale . the away team record of this unqiue row is watford .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; score ; 0 - 0 } } ; and { eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; home team } ; port vale } ; eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; away team } ; watford } } } = true', 'tointer': 'select the rows whose score record fuzzily matches to 0 - 0 . there is only one such row in the table . the home team record of this unqiue row is port vale . the away team record of this unqiue row is watford .'} | and { only { filter_eq { all_rows ; score ; 0 - 0 } } ; and { eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; home team } ; port vale } ; eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; away team } ; watford } } } = true | select the rows whose score record fuzzily matches to 0 - 0 . there is only one such row in the table . the home team record of this unqiue row is port vale . the away team record of this unqiue row is watford . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'score_10': 10, '0 - 0_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'home team_12': 12, 'port vale_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'away team_14': 14, 'watford_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'score_10': 'score', '0 - 0_11': '0 - 0', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'home team_12': 'home team', 'port vale_13': 'port vale', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'away team_14': 'away team', 'watford_15': 'watford'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'score_10': [0], '0 - 0_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'home team_12': [2], 'port vale_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'away team_14': [4], 'watford_15': [5]} | ['tie no', 'home team', 'score', 'away team', 'date'] | [['1', 'everton', '0 - 1', 'liverpool', '21 february 1988'], ['2', 'newcastle united', '1 - 3', 'wimbledon', '20 february 1988'], ['3', 'manchester city', '3 - 1', 'plymouth argyle', '20 february 1988'], ['4', 'queens park rangers', '1 - 1', 'luton town', '20 february 1988'], ['replay', 'luton town', '1 - 0', 'queens park rangers', '24 february 1988'], ['5', 'portsmouth', '3 - 0', 'bradford city', '20 february 1988'], ['6', 'port vale', '0 - 0', 'watford', '20 february 1988'], ['replay', 'watford', '2 - 0', 'port vale', '23 february 1988'], ['7', 'arsenal', '2 - 1', 'manchester united', '20 february 1988'], ['8', 'birmingham city', '0 - 1', 'nottingham forest', '20 february 1988']] |
1979 formula one season | https://en.wikipedia.org/wiki/1979_Formula_One_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140080-2.html.csv | comparative | in the 1979 formula one season , the german grand prix was 15 days after the british grand prix . | {'row_1': '10', 'row_2': '9', 'col': '2', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '15 days', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'race', 'german grand prix'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose race record fuzzily matches to german grand prix .', 'tostr': 'filter_eq { all_rows ; race ; german grand prix }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; race ; german grand prix } ; date }', 'tointer': 'select the rows whose race record fuzzily matches to german grand prix . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'race', 'british grand prix'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose race record fuzzily matches to british grand prix .', 'tostr': 'filter_eq { all_rows ; race ; british grand prix }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; race ; british grand prix } ; date }', 'tointer': 'select the rows whose race record fuzzily matches to british grand prix . take the date record of this row .'}], 'result': '15 days', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; race ; german grand prix } ; date } ; hop { filter_eq { all_rows ; race ; british grand prix } ; date } }'}, '15 days'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; race ; german grand prix } ; date } ; hop { filter_eq { all_rows ; race ; british grand prix } ; date } } ; 15 days } = true', 'tointer': 'select the rows whose race record fuzzily matches to german grand prix . take the date record of this row . select the rows whose race record fuzzily matches to british grand prix . take the date record of this row . the first record is 15 days larger than the second record .'} | eq { diff { hop { filter_eq { all_rows ; race ; german grand prix } ; date } ; hop { filter_eq { all_rows ; race ; british grand prix } ; date } } ; 15 days } = true | select the rows whose race record fuzzily matches to german grand prix . take the date record of this row . select the rows whose race record fuzzily matches to british grand prix . take the date record of this row . the first record is 15 days larger than the second record . | 6 | 6 | {'str_eq_5': 5, 'result_6': 6, 'diff_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'race_8': 8, 'german grand prix_9': 9, 'date_10': 10, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'race_12': 12, 'british grand prix_13': 13, 'date_14': 14, '15 days_15': 15} | {'str_eq_5': 'str_eq', 'result_6': 'true', 'diff_4': 'diff', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'race_8': 'race', 'german grand prix_9': 'german grand prix', 'date_10': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'race_12': 'race', 'british grand prix_13': 'british grand prix', 'date_14': 'date', '15 days_15': '15 days'} | {'str_eq_5': [6], 'result_6': [], 'diff_4': [5], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'race_8': [0], 'german grand prix_9': [0], 'date_10': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'race_12': [1], 'british grand prix_13': [1], 'date_14': [3], '15 days_15': [5]} | ['race', 'date', 'location', 'pole position', 'fastest lap', 'race winner', 'constructor', 'report'] | [['argentine grand prix', '21 january', 'buenos aires', 'jacques laffite', 'jacques laffite', 'jacques laffite', 'ligier - ford', 'report'], ['brazilian grand prix', '4 february', 'interlagos', 'jacques laffite', 'jacques laffite', 'jacques laffite', 'ligier - ford', 'report'], ['south african grand prix', '3 march', 'kyalami', 'jean - pierre jabouille', 'gilles villeneuve', 'gilles villeneuve', 'ferrari', 'report'], ['united states grand prix west', '8 april', 'long beach', 'gilles villeneuve', 'gilles villeneuve', 'gilles villeneuve', 'ferrari', 'report'], ['spanish grand prix', '29 april', 'jarama', 'jacques laffite', 'gilles villeneuve', 'patrick depailler', 'ligier - ford', 'report'], ['belgian grand prix', '13 may', 'zolder', 'jacques laffite', 'gilles villeneuve', 'jody scheckter', 'ferrari', 'report'], ['monaco grand prix', '27 may', 'monaco', 'jody scheckter', 'patrick depailler', 'jody scheckter', 'ferrari', 'report'], ['french grand prix', '1 july', 'dijon - prenois', 'jean - pierre jabouille', 'rené arnoux', 'jean - pierre jabouille', 'renault', 'report'], ['british grand prix', '14 july', 'silverstone', 'alan jones', 'clay regazzoni', 'clay regazzoni', 'williams - ford', 'report'], ['german grand prix', '29 july', 'hockenheimring', 'jean - pierre jabouille', 'gilles villeneuve', 'alan jones', 'williams - ford', 'report'], ['austrian grand prix', '12 august', 'österreichring', 'rené arnoux', 'rené arnoux', 'alan jones', 'williams - ford', 'report'], ['dutch grand prix', '26 august', 'zandvoort', 'rené arnoux', 'gilles villeneuve', 'alan jones', 'williams - ford', 'report'], ['italian grand prix', '9 september', 'monza', 'jean - pierre jabouille', 'clay regazzoni', 'jody scheckter', 'ferrari', 'report'], ['canadian grand prix', '30 september', 'île notre - dame', 'alan jones', 'alan jones', 'alan jones', 'williams - ford', 'report'], ['united states grand prix', '7 october', 'watkins glen', 'alan jones', 'nelson piquet', 'gilles villeneuve', 'ferrari', 'report']] |
1961 ohio state buckeyes football team | https://en.wikipedia.org/wiki/1961_Ohio_State_Buckeyes_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17814506-1.html.csv | comparative | the 1961 ohio state buckeyes football team was ranked one place better on november 25th than they were on november 18th . | {'row_1': '9', 'row_2': '8', 'col': '3', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 25'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to november 25 .', 'tostr': 'filter_eq { all_rows ; date ; november 25 }'}, 'rank'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; november 25 } ; rank }', 'tointer': 'select the rows whose date record fuzzily matches to november 25 . take the rank record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november 18'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november 18 .', 'tostr': 'filter_eq { all_rows ; date ; november 18 }'}, 'rank'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; november 18 } ; rank }', 'tointer': 'select the rows whose date record fuzzily matches to november 18 . take the rank record of this row .'}], 'result': '-1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; date ; november 25 } ; rank } ; hop { filter_eq { all_rows ; date ; november 18 } ; rank } }'}, '-1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; date ; november 25 } ; rank } ; hop { filter_eq { all_rows ; date ; november 18 } ; rank } } ; -1 } = true', 'tointer': 'select the rows whose date record fuzzily matches to november 25 . take the rank record of this row . select the rows whose date record fuzzily matches to november 18 . take the rank record of this row . the second record is 1 larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; date ; november 25 } ; rank } ; hop { filter_eq { all_rows ; date ; november 18 } ; rank } } ; -1 } = true | select the rows whose date record fuzzily matches to november 25 . take the rank record of this row . select the rows whose date record fuzzily matches to november 18 . take the rank record of this row . the second record is 1 larger than the first record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'date_8': 8, 'november 25_9': 9, 'rank_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'date_12': 12, 'november 18_13': 13, 'rank_14': 14, '-1_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'date_8': 'date', 'november 25_9': 'november 25', 'rank_10': 'rank', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'date_12': 'date', 'november 18_13': 'november 18', 'rank_14': 'rank', '-1_15': '-1'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'date_8': [0], 'november 25_9': [0], 'rank_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'date_12': [1], 'november 18_13': [1], 'rank_14': [3], '-1_15': [5]} | ['date', 'opponent', 'rank', 'site', 'result', 'attendance'] | [['september 30', 'texas christian', '3', 'ohio stadium columbus , oh', 't7 - 7', '82878'], ['october 7', 'ucla', '8', 'ohio stadium columbus , oh', 'w13 - 3', '82992'], ['october 14', 'illinois', '7', 'ohio stadium columbus , oh', 'w44 - 0', '82374'], ['october 21', 'northwestern', '7', 'dyche stadium evanston , il', 'w10 - 0', '43259'], ['october 28', 'wisconsin', '6', 'camp randall stadium madison , wi', 'w30 - 21', '58411'], ['november 4', '9 iowa', '5', 'ohio stadium columbus , oh', 'w29 - 13', '83795'], ['november 11', 'indiana', '3', 'memorial stadium bloomington , in', 'w16 - 7', '27108'], ['november 18', 'oregon', '3', 'ohio stadium columbus , oh', 'w22 - 12', '82073'], ['november 25', 'michigan', '2', 'michigan stadium ann arbor , mi', 'w50 - 20', '80444']] |
1996 - 97 european challenge cup | https://en.wikipedia.org/wiki/1996%E2%80%9397_European_Challenge_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16770037-5.html.csv | superlative | in the 1996-97 european challenge cup swansea got the most points for . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '3', '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', 'points for'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points for }'}, 'team'], 'result': 'swansea', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points for } ; team }'}, 'swansea'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points for } ; team } ; swansea } = true', 'tointer': 'select the row whose points for record of all rows is maximum . the team record of this row is swansea .'} | eq { hop { argmax { all_rows ; points for } ; team } ; swansea } = true | select the row whose points for record of all rows is maximum . the team record of this row is swansea . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points for_5': 5, 'team_6': 6, 'swansea_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points for_5': 'points for', 'team_6': 'team', 'swansea_7': 'swansea'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points for_5': [0], 'team_6': [1], 'swansea_7': [2]} | ['team', 'tries for', 'tries against', 'try diff', 'points for', 'points against', 'points diff'] | [['bourgoin', '27', '4', '+ 23', '196', '66', '+ 130'], ['bordeaux - bègles', '29', '13', '+ 16', '195', '99', '+ 96'], ['swansea', '28', '19', '+ 9', '207', '138', '+ 69'], ['gloucester', '17', '17', '0', '119', '123', '4'], ['ebbw vale', '6', '36', '30', '48', '243', '195'], ['london irish', '12', '30', '18', '90', '186', '96']] |
2009 premier league darts | https://en.wikipedia.org/wiki/2009_Premier_League_Darts | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18968744-17.html.csv | aggregation | in the 2009 premier league darts all members had an average iwat of 26 . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '26', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'lwat'], 'result': '26', 'ind': 0, 'tostr': 'avg { all_rows ; lwat }'}, '26'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; lwat } ; 26 } = true', 'tointer': 'the average of the lwat record of all rows is 26 .'} | round_eq { avg { all_rows ; lwat } ; 26 } = true | the average of the lwat record of all rows is 26 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'lwat_4': 4, '26_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'lwat_4': 'lwat', '26_5': '26'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'lwat_4': [0], '26_5': [1]} | ['name', 'lwat', '100 +', '140 +', '180s'] | [['phil taylor', '29', '194', '128', '56'], ['james wade', '28', '181', '114', '43'], ['raymond van barneveld', '25', '188', '122', '43'], ['mervyn king', '28', '203', '114', '37'], ['terry jenkins', '26', '207', '129', '46'], ['john part', '18', '180', '72', '25'], ['jelle klaasen', '26', '173', '93', '33']] |
giorgio rubino | https://en.wikipedia.org/wiki/Giorgio_Rubino | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12955486-1.html.csv | count | giorgio rubino placed in the 4th position at two different walking championship events . | {'scope': 'all', 'criterion': 'equal', 'value': '4th', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '4th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 4th .', 'tostr': 'filter_eq { all_rows ; position ; 4th }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; 4th } }', 'tointer': 'select the rows whose position record fuzzily matches to 4th . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; 4th } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to 4th . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; position ; 4th } } ; 2 } = true | select the rows whose position record fuzzily matches to 4th . 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, 'position_5': 5, '4th_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', 'position_5': 'position', '4th_6': '4th', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], '4th_6': [0], '2_7': [2]} | ['year', 'competition', 'venue', 'position', 'event'] | [['2003', 'world youth championships', 'sherbrooke , canada', '4th', '10000 m'], ['2004', 'world junior championships', 'grosseto , italy', '10th', '10000 m'], ['2005', 'european junior championships', 'kaunas , lithuania', '3rd', '10000 m'], ['2006', 'world race walking cup', 'la coruã ± a , spain', '17th', '20 km'], ['2006', 'european championships', 'gothenburg , sweden', '8th', '20 km'], ['2007', 'world championships', 'osaka , japan', '5th', '20 km'], ['2008', 'olympic games', 'beijing , pr china', '18th', '20 km'], ['2009', 'european race walking cup', 'metz , france', '1st', '20 km'], ['2009', 'world championships', 'berlin , germany', '4th', '20 km'], ['2009', 'mediterranean games', 'pescara , italy', '2nd', '20 km'], ['2010', 'european championships', 'barcelona , spain', '5th', '20 km']] |
list of england national rugby union team results 1980 - 89 | https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1980%E2%80%9389 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18178608-2.html.csv | superlative | england 's national rugby union team had their highest amount of results against wales . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', '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', 'against'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; against }'}, 'opposing teams'], 'result': 'wales', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; against } ; opposing teams }'}, 'wales'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; against } ; opposing teams } ; wales } = true', 'tointer': 'select the row whose against record of all rows is maximum . the opposing teams record of this row is wales .'} | eq { hop { argmax { all_rows ; against } ; opposing teams } ; wales } = true | select the row whose against record of all rows is maximum . the opposing teams record of this row is wales . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'against_5': 5, 'opposing teams_6': 6, 'wales_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'against_5': 'against', 'opposing teams_6': 'opposing teams', 'wales_7': 'wales'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'against_5': [0], 'opposing teams_6': [1], 'wales_7': [2]} | ['opposing teams', 'against', 'date', 'venue', 'status'] | [['wales', '21', '17 / 01 / 1981', 'cardiff arms park , cardiff', 'five nations'], ['scotland', '17', '21 / 02 / 1981', 'twickenham , london', 'five nations'], ['ireland', '6', '07 / 03 / 1981', 'lansdowne road , dublin', 'five nations'], ['france', '16', '21 / 03 / 1981', 'twickenham , london', 'five nations'], ['argentina', '19', '30 / 05 / 1981', 'ferrocarril stadium , buenos aires', 'first test'], ['argentina', '6', '06 / 06 / 1981', 'ferrocarril stadium , buenos aires', 'second test']] |
1935 in brazilian football | https://en.wikipedia.org/wiki/1935_in_Brazilian_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15358573-1.html.csv | unique | the only team to have sixteen points is ec são caetano . | {'scope': 'all', 'row': '4', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': '16', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'points', '16'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is equal to 16 .', 'tostr': 'filter_eq { all_rows ; points ; 16 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; points ; 16 } }', 'tointer': 'select the rows whose points record is equal to 16 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'points', '16'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is equal to 16 .', 'tostr': 'filter_eq { all_rows ; points ; 16 }'}, 'team'], 'result': 'ec são caetano', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; points ; 16 } ; team }'}, 'ec são caetano'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; points ; 16 } ; team } ; ec são caetano }', 'tointer': 'the team record of this unqiue row is ec são caetano .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; points ; 16 } } ; eq { hop { filter_eq { all_rows ; points ; 16 } ; team } ; ec são caetano } } = true', 'tointer': 'select the rows whose points record is equal to 16 . there is only one such row in the table . the team record of this unqiue row is ec são caetano .'} | and { only { filter_eq { all_rows ; points ; 16 } } ; eq { hop { filter_eq { all_rows ; points ; 16 } ; team } ; ec são caetano } } = true | select the rows whose points record is equal to 16 . there is only one such row in the table . the team record of this unqiue row is ec são caetano . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'points_7': 7, '16_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'ec são caetano_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'points_7': 'points', '16_8': '16', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'ec são caetano_10': 'ec são caetano'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'points_7': [0], '16_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'ec são caetano_10': [3]} | ['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference'] | [['1', 'portuguesa', '22', '14', '2', '2', '17', '48'], ['2', 'ypiranga - sp', '22', '14', '0', '3', '33', '17'], ['3', 'estudantes', '20', '14', '2', '3', '16', '31'], ['4', 'ec são caetano', '16', '14', '2', '5', '31', '- 2'], ['5', 'sírio libanês', '12', '12', '2', '5', '31', '- 9'], ['6', 'jardim américa', '7', '13', '1', '9', '39', '- 16'], ['7', 'humberto primo', '7', '14', '1', '10', '48', '- 28'], ['8', 'ordem e progresso', '2', '13', '0', '12', '54', '- 41']] |
toppserien | https://en.wikipedia.org/wiki/Toppserien | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2522473-1.html.csv | count | six of the current toppserien teams have been in the league for more than 15 seasons . | {'scope': 'all', 'criterion': 'greater_than', 'value': '15', 'result': '6', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'seasons', '15'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose seasons record is greater than 15 .', 'tostr': 'filter_greater { all_rows ; seasons ; 15 }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; seasons ; 15 } }', 'tointer': 'select the rows whose seasons record is greater than 15 . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; seasons ; 15 } } ; 6 } = true', 'tointer': 'select the rows whose seasons record is greater than 15 . the number of such rows is 6 .'} | eq { count { filter_greater { all_rows ; seasons ; 15 } } ; 6 } = true | select the rows whose seasons record is greater than 15 . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'seasons_5': 5, '15_6': 6, '6_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'seasons_5': 'seasons', '15_6': '15', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'seasons_5': [0], '15_6': [0], '6_7': [2]} | ['team', 'home city', 'home ground', 'in toppserien since', 'first appearance', 'seasons'] | [['amazon grimstad', 'grimstad', 'jj ugland stadion', '2006', '2006', '8'], ['arna - bjørnar', 'ytre arna ( bergen )', 'arna idrettspark', '2006', '1997', '16'], ['avaldsnes', 'avaldsnes', 'avaldsnes idrettssenter', '2013', '2013', '1'], ['klepp', 'kleppe ( stavanger )', 'klepp stadion', '1987', '1987', '27'], ['kolbotn', 'kolbotn ( oslo )', 'sofiemyr', '1995', '1995', '19'], ['lsk kvinner', 'lillestrøm ( oslo )', 'lsk - hallen', '1987', '1987', '27'], ['medkila', 'harstad', 'harstad stadion', '2013', '2004', '3'], ['røa', 'oslo', 'røabanen', '2001', '2001', '13'], ['sandviken', 'bergen', 'stemmemyren', '2011', '1987', '21'], ['stabæk', 'bærum ( oslo )', 'nadderud stadion', '2009', '2009', '5'], ['trondheims - ørn', 'trondheim', 'dnb nor arena', '1987', '1987', '27']] |
united states house of representatives elections , 1950 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1950 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342198-13.html.csv | comparative | noah m mason has a first elected year which is earlier than that of mike sid simpson . | {'row_1': '4', 'row_2': '5', 'col': '4', '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', 'incumbent', 'noah m mason'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to noah m mason .', 'tostr': 'filter_eq { all_rows ; incumbent ; noah m mason }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; noah m mason } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to noah m mason . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'sid simpson'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to sid simpson .', 'tostr': 'filter_eq { all_rows ; incumbent ; sid simpson }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; sid simpson } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to sid simpson . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; noah m mason } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; sid simpson } ; first elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to noah m mason . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to sid simpson . take the first elected record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; incumbent ; noah m mason } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; sid simpson } ; first elected } } = true | select the rows whose incumbent record fuzzily matches to noah m mason . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to sid simpson . take the first elected 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, 'incumbent_7': 7, 'noah m mason_8': 8, 'first elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'sid simpson_12': 12, 'first elected_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', 'incumbent_7': 'incumbent', 'noah m mason_8': 'noah m mason', 'first elected_9': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'sid simpson_12': 'sid simpson', 'first elected_13': 'first elected'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'noah m mason_8': [0], 'first elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'sid simpson_12': [1], 'first elected_13': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['illinois 2', "barratt o'hara", 'democratic', '1948', 'lost re - election republican gain', "richard b vail ( r ) 53.6 % barratt o'hara ( d ) 46.4 %"], ['illinois 3', 'neil j linehan', 'democratic', '1948', 'lost re - election republican gain', 'fred e busbey ( r ) 57.2 % neil j linehan ( d ) 42.8 %'], ['illinois 6', "thomas j o'brien", 'democratic', '1942', 're - elected', "thomas j o'brien ( d ) 64.6 % john m fay ( r ) 35.4 %"], ['illinois 15', 'noah m mason', 'republican', '1936', 're - elected', 'noah m mason ( r ) 63.3 % wayne f caskey ( d ) 36.7 %'], ['illinois 20', 'sid simpson', 'republican', '1942', 're - elected', 'sid simpson ( r ) 59.3 % howard manning ( d ) 40.7 %'], ['illinois 25', 'melvin price', 'democratic', '1944', 're - elected', 'melvin price ( d ) 64.9 % roger d jones ( r ) 35.1 %']] |
2007 open championship | https://en.wikipedia.org/wiki/2007_Open_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12278571-2.html.csv | majority | the majority of players in the 2007 open championship golf tournament were from the united states . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { all_rows ; country ; united states } = true'} | most_eq { all_rows ; country ; united states } = true | for the country records of all rows , most of them fuzzily match to united states . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'united states_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'united states_4': 'united states'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'united states_4': [0]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['ernie els', 'south africa', '2002', '279', '- 5', 't4'], ['ben curtis', 'united states', '2003', '281', '- 3', 't8'], ['tiger woods', 'united states', '2000 , 2005 , 2006', '282', '- 2', 't12'], ['mark calcavecchia', 'united states', '1989', '285', '+ 1', 't23'], ['tom lehman', 'united states', '1996', '283', '+ 9', 't51'], ["mark o'meara", 'united states', '1998', '296', '+ 12', 't60'], ['sandy lyle', 'scotland', '1985', '298', '+ 14', 't65']] |
list of tallest buildings in saudi arabia | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Saudi_Arabia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11606138-2.html.csv | aggregation | on average , the tallest buildings in saudi arabia have 70 floors . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '70', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'floors'], 'result': '70', 'ind': 0, 'tostr': 'avg { all_rows ; floors }'}, '70'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; floors } ; 70 } = true', 'tointer': 'the average of the floors record of all rows is 70 .'} | round_eq { avg { all_rows ; floors } ; 70 } = true | the average of the floors record of all rows is 70 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'floors_4': 4, '70_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'floors_4': 'floors', '70_5': '70'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'floors_4': [0], '70_5': [1]} | ['rank', 'name', 'city', 'height', 'floors'] | [['1', 'kingdom tower', 'jeddah', '-', '186'], ['2', 'diamond tower', 'jeddah', '-', '93'], ['3', 'capital market authority headquarters', 'riyadh', '-', '77'], ['4', 'lamar tower 1', 'jeddah', '-', '87'], ['5', 'burj rafal', 'riyadh', '-', '68'], ['6', 'kafd world trade centre', 'riyadh', '-', '67'], ['7', 'lamar tower 2', 'jeddah', '-', '84'], ['8', 'kempinski hotel', 'jeddah', '-', '69'], ['9', 'the headquarters', 'jeddah', '-', '52'], ['10', 'aqua tower', 'jeddah', '-', '59'], ['11', 'gcc bank headquarters', 'riyadh', '-', '53'], ['12', 'abraj al bait maqam tower', 'mecca', '-', '57'], ['13', 'abraj al bait qibla tower', 'mecca', '-', '57'], ['14', 'al majdoul tower', 'riyadh', '-', '54'], ['15', 'west tower at the hq business park', 'riyadh', '-', '54']] |
friends | https://en.wikipedia.org/wiki/Friends | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11315-1.html.csv | aggregation | over 10 seasons , the show friends drew 260.8 / 254.8 viewers ( in millions ) . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '260.8 / 254.8', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'viewers ( in millions )'], 'result': '260.8 / 254.8', 'ind': 0, 'tostr': 'sum { all_rows ; viewers ( in millions ) }'}, '260.8 / 254.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; viewers ( in millions ) } ; 260.8 / 254.8 } = true', 'tointer': 'the sum of the viewers ( in millions ) record of all rows is 260.8 / 254.8 .'} | round_eq { sum { all_rows ; viewers ( in millions ) } ; 260.8 / 254.8 } = true | the sum of the viewers ( in millions ) record of all rows is 260.8 / 254.8 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'viewers (in millions)_4': 4, '260.8/254.8_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'viewers (in millions)_4': 'viewers ( in millions )', '260.8/254.8_5': '260.8 / 254.8'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'viewers (in millions)_4': [0], '260.8/254.8_5': [1]} | ['season', 'season premiere', 'season finale', 'tv season', 'rank', 'viewers ( in millions )'] | [['1', 'september 22 , 1994', 'may 18 , 1995', '1994 - 95', '8', '24.3 / 24.8'], ['2', 'september 21 , 1995', 'may 16 , 1996', '1995 - 96', '3', '29.4 / 31.7'], ['3', 'september 19 , 1996', 'may 15 , 1997', '1996 - 97', '4', '25.0 / 26.3'], ['4', 'september 25 , 1997', 'may 7 , 1998', '1997 - 98', '4', '24.1 / 25.0'], ['5', 'september 24 , 1998', 'may 20 , 1999', '1998 - 99', '2', '23.5 / 24.7'], ['6', 'september 23 , 1999', 'may 18 , 2000', '1999 - 2000', '5', '20.7 / 22.6'], ['7', 'october 12 , 2000', 'may 17 , 2001', '2000 - 01', '4', '20.2 / 22.1'], ['8', 'september 27 , 2001', 'may 16 , 2002', '2001 - 02', '1', '24.5 / 26.7'], ['9', 'september 26 , 2002', 'may 15 , 2003', '2002 - 03', '3', '21.6 / 24.0'], ['10', 'september 25 , 2003', 'may 6 , 2004', '2003 - 04', '5', '22.8 / 26.9']] |
list of england national rugby union team results 1970 - 79 | https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_1970%E2%80%9379 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18178924-2.html.csv | comparative | england scored more points playing against scotland than playing against wales . | {'row_1': '4', 'row_2': '1', 'col': '2', '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', 'opposing teams', 'scotland'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opposing teams record fuzzily matches to scotland .', 'tostr': 'filter_eq { all_rows ; opposing teams ; scotland }'}, 'against'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opposing teams ; scotland } ; against }', 'tointer': 'select the rows whose opposing teams record fuzzily matches to scotland . take the against record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opposing teams', 'wales'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opposing teams record fuzzily matches to wales .', 'tostr': 'filter_eq { all_rows ; opposing teams ; wales }'}, 'against'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opposing teams ; wales } ; against }', 'tointer': 'select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opposing teams ; scotland } ; against } ; hop { filter_eq { all_rows ; opposing teams ; wales } ; against } } = true', 'tointer': 'select the rows whose opposing teams record fuzzily matches to scotland . take the against record of this row . select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; opposing teams ; scotland } ; against } ; hop { filter_eq { all_rows ; opposing teams ; wales } ; against } } = true | select the rows whose opposing teams record fuzzily matches to scotland . take the against record of this row . select the rows whose opposing teams record fuzzily matches to wales . take the against 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, 'opposing teams_7': 7, 'scotland_8': 8, 'against_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opposing teams_11': 11, 'wales_12': 12, 'against_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', 'opposing teams_7': 'opposing teams', 'scotland_8': 'scotland', 'against_9': 'against', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opposing teams_11': 'opposing teams', 'wales_12': 'wales', 'against_13': 'against'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opposing teams_7': [0], 'scotland_8': [0], 'against_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opposing teams_11': [1], 'wales_12': [1], 'against_13': [3]} | ['opposing teams', 'against', 'date', 'venue', 'status'] | [['wales', '3', '16 / 01 / 1971', 'cardiff arms park , cardiff', 'five nations'], ['ireland', '6', '13 / 02 / 1971', 'lansdowne road , dublin', 'five nations'], ['france', '14', '27 / 02 / 1971', 'twickenham , london', 'five nations'], ['scotland', '16', '20 / 03 / 1971', 'twickenham , london', 'five nations'], ['scotland', '26', '27 / 03 / 1971', 'murrayfield , edinburgh', 'rfu centenary match'], ["rfu president 's xv", '28', '17 / 04 / 1971', 'twickenham , london', 'rfu centenary match']] |
montérégie | https://en.wikipedia.org/wiki/Mont%C3%A9r%C3%A9gie | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1011906-1.html.csv | ordinal | the la haute - yamaska county has the 2nd highest density ( pop per km2 ) in the montérégie region . | {'row': '3', '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', 'density ( pop per km2 )', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; density ( pop per km2 ) ; 2 }'}, 'regional county municipality ( rcm )'], 'result': 'la haute - yamaska', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; density ( pop per km2 ) ; 2 } ; regional county municipality ( rcm ) }'}, 'la haute - yamaska'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; density ( pop per km2 ) ; 2 } ; regional county municipality ( rcm ) } ; la haute - yamaska } = true', 'tointer': 'select the row whose density ( pop per km2 ) record of all rows is 2nd maximum . the regional county municipality ( rcm ) record of this row is la haute - yamaska .'} | eq { hop { nth_argmax { all_rows ; density ( pop per km2 ) ; 2 } ; regional county municipality ( rcm ) } ; la haute - yamaska } = true | select the row whose density ( pop per km2 ) record of all rows is 2nd maximum . the regional county municipality ( rcm ) record of this row is la haute - yamaska . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'density (pop per km2)_5': 5, '2_6': 6, 'regional county municipality (rcm)_7': 7, 'la haute - yamaska_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', 'density (pop per km2)_5': 'density ( pop per km2 )', '2_6': '2', 'regional county municipality (rcm)_7': 'regional county municipality ( rcm )', 'la haute - yamaska_8': 'la haute - yamaska'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'density (pop per km2)_5': [0], '2_6': [0], 'regional county municipality (rcm)_7': [1], 'la haute - yamaska_8': [2]} | ['regional county municipality ( rcm )', 'population canada 2011 census', 'land area', 'density ( pop per km2 )', 'seat of rcm'] | [['acton', '15381', 'km2 ( sqmi )', '26.5', 'acton vale'], ['brome - missisquoi', '55621', 'km2 ( sqmi )', '33.7', 'cowansville'], ['la haute - yamaska', '85042', 'km2 ( sqmi )', '133.6', 'granby'], ['la vallãe - du - richelieu', '116773', 'km2 ( sqmi )', '198.3', 'mcmasterville'], ['le haut - richelieu', '114344', 'km2 ( sqmi )', '122.1', 'saint - jean - sur - richelieu'], ['les maskoutains', '84248', 'km2 ( sqmi )', '64.7', 'saint - hyacinthe']] |
2009 - 10 sacramento kings season | https://en.wikipedia.org/wiki/2009%E2%80%9310_Sacramento_Kings_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23285805-5.html.csv | aggregation | during the 2009-10 sacramento kings season , tyreke evans averaged about 25 high points per game . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': '25', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'tyreke evans'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high points', 'tyreke evans'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high points ; tyreke evans }', 'tointer': 'select the rows whose high points record fuzzily matches to tyreke evans .'}, 'high points'], 'result': '25', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; high points ; tyreke evans } ; high points }'}, '25'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; high points ; tyreke evans } ; high points } ; 25 } = true', 'tointer': 'select the rows whose high points record fuzzily matches to tyreke evans . the average of the high points record of these rows is 25 .'} | round_eq { avg { filter_eq { all_rows ; high points ; tyreke evans } ; high points } ; 25 } = true | select the rows whose high points record fuzzily matches to tyreke evans . the average of the high points record of these rows is 25 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high points_5': 5, 'tyreke evans_6': 6, 'high points_7': 7, '25_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high points_5': 'high points', 'tyreke evans_6': 'tyreke evans', 'high points_7': 'high points', '25_8': '25'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high points_5': [0], 'tyreke evans_6': [0], 'high points_7': [1], '25_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['17', 'december 2', 'indiana', 'w 110 - 105 ( ot )', 'tyreke evans ( 26 )', 'spencer hawes ( 7 )', 'tyreke evans ( 6 )', 'arco arena 10021', '9 - 8'], ['18', 'december 5', 'phoenix', 'l 107 - 115 ( ot )', 'tyreke evans ( 21 )', 'kenny thomas ( 18 )', 'tyreke evans ( 7 )', 'us airways center 17747', '9 - 9'], ['19', 'december 6', 'miami', 'l 102 - 115 ( ot )', 'tyreke evans ( 30 )', 'jason thompson ( 8 )', 'tyreke evans , jason thompson ( 4 )', 'arco arena 13186', '9 - 10'], ['20', 'december 8', 'new orleans', 'l 94 - 96 ( ot )', 'tyreke evans ( 25 )', 'jason thompson ( 9 )', 'tyreke evans ( 9 )', 'new orleans arena 13140', '9 - 11'], ['21', 'december 9', 'san antonio', 'l 106 - 118 ( ot )', 'omri casspi ( 20 )', 'jason thompson ( 9 )', 'beno udrih ( 6 )', 'at & t center 17353', '9 - 12'], ['22', 'december 12', 'minnesota', 'w 120 - 100 ( ot )', 'jason thompson ( 23 )', 'jason thompson ( 12 )', 'sergio rodríguez ( 9 )', 'arco arena 11333', '10 - 12'], ['23', 'december 15', 'portland', 'l 88 - 95 ( ot )', 'tyreke evans ( 19 )', 'jason thompson ( 9 )', 'beno udrih ( 5 )', 'rose garden 20588', '10 - 13'], ['24', 'december 16', 'washington', 'w 112 - 109 ( ot )', 'tyreke evans ( 26 )', 'jason thompson ( 13 )', 'tyreke evans ( 6 )', 'arco arena 16579', '11 - 13'], ['25', 'december 18', 'minnesota', 'l 96 - 116 ( ot )', 'omri casspi ( 21 )', 'jason thompson', 'tyreke evans ( 8 )', 'target center 13144', '11 - 14'], ['26', 'december 19', 'milwaukee', 'w 96 - 95 ( ot )', 'tyreke evans ( 24 )', 'jason thompson ( 10 )', 'jason thompson ( 4 )', 'bradley center 13745', '12 - 14'], ['27', 'december 21', 'chicago', 'w 102 - 98 ( ot )', 'tyreke evans ( 23 )', 'tyreke evans ( 8 )', 'sergio rodríguez ( 7 )', 'united center 19631', '13 - 14'], ['28', 'december 23', 'cleveland', 'l 104 - 117 ( ot )', 'tyreke evans ( 28 )', 'spencer hawes ( 12 )', 'tyreke evans ( 5 )', 'arco arena 16407', '13 - 15'], ['29', 'december 26', 'la lakers', 'l 103 - 112 ( ot )', 'beno udrih ( 23 )', 'omri casspi ( 10 )', 'spencer hawes ( 7 )', 'arco arena 17345', '13 - 16'], ['30', 'december 28', 'denver', 'w 106 - 101 ( ot )', 'andrés nocioni ( 21 )', 'jason thompson ( 11 )', 'beno udrih ( 7 )', 'arco arena 14548', '14 - 16']] |
1990 pga championship | https://en.wikipedia.org/wiki/1990_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18132874-2.html.csv | count | in the 1990 pga championship , among the players from united states , 2 of them had a total score of 300 each . | {'scope': 'subset', 'criterion': 'equal', 'value': '300', 'result': '2', 'col': '4', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record fuzzily matches to united states .'}, 'total', '300'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is equal to 300 .', 'tostr': 'filter_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is equal to 300 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } } ; 2 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is equal to 300 . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } } ; 2 } = true | select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is equal to 300 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'country_6': 6, 'united states_7': 7, 'total_8': 8, '300_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'country_6': 'country', 'united states_7': 'united states', 'total_8': 'total', '300_9': '300', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'country_6': [0], 'united states_7': [0], 'total_8': [1], '300_9': [1], '2_10': [3]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['payne stewart', 'united states', '1989', '284', '+ 4', 't8'], ['jeff sluman', 'united states', '1988', '297', '+ 9', 't31'], ['john mahaffey', 'united states', '1978', '298', '+ 10', 't40'], ['bob tway', 'united states', '1986', '299', '+ 11', 't45'], ['raymond floyd', 'united states', '1969 , 1982', '300', '+ 12', 't49'], ['hal sutton', 'united states', '1983', '300', '+ 12', 't49'], ['david graham', 'australia', '1979', '304', '+ 16', 't66']] |
mondo film | https://en.wikipedia.org/wiki/Mondo_film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1176486-1.html.csv | majority | all of the mondo film movies were filmed in the country of italy . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'italy', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'country', 'italy'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , all of them fuzzily match to italy .', 'tostr': 'all_eq { all_rows ; country ; italy } = true'} | all_eq { all_rows ; country ; italy } = true | for the country records of all rows , all of them fuzzily match to italy . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'italy_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'italy_4': 'italy'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'italy_4': [0]} | ['title', 'year', 'country', 'music', 'uncut run time'] | [['mondo cane', '1962', 'italy', 'riz ortolani', '108 minutes'], ['la donna nel mondo', '1963', 'italy', 'riz ortolani nino oliviero', '107 minutes'], ['mondo cane 2', '1963', 'italy', 'nino oliviero', '95 minutes'], ['africa addio', '1966', 'italy', 'riz ortolani', '139 minutes'], ['addio zio tom', '1971', 'italy', 'riz ortolani', '136 minutes']] |
1908 michigan wolverines football team | https://en.wikipedia.org/wiki/1908_Michigan_Wolverines_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25730460-2.html.csv | superlative | dave allerdice had the most points in the 1908 michigan wolverines football team . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', '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', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'player'], 'result': 'dave allerdice', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; player }'}, 'dave allerdice'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; player } ; dave allerdice } = true', 'tointer': 'select the row whose points record of all rows is maximum . the player record of this row is dave allerdice .'} | eq { hop { argmax { all_rows ; points } ; player } ; dave allerdice } = true | select the row whose points record of all rows is maximum . the player record of this row is dave allerdice . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'player_6': 6, 'dave allerdice_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', 'player_6': 'player', 'dave allerdice_7': 'dave allerdice'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'player_6': [1], 'dave allerdice_7': [2]} | ['player', 'touchdowns', 'extra points', 'field goals', 'points'] | [['dave allerdice', '2', '14', '10', '64'], ['sam davison', '7', '0', '1', '39'], ['donald w greene', '2', '0', '0', '10'], ['william p edmunds', '1', '0', '0', '5'], ['maurice e crumpacker', '1', '0', '0', '5'], ['william j embs', '1', '0', '0', '5']] |
annerose fiedler | https://en.wikipedia.org/wiki/Annerose_Fiedler | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11503769-1.html.csv | majority | all of the tournaments that annerose fiedler was in were in the decade of the 1970s . | {'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'greater_than', 'value': '1970', 'subset': None} | {'func': 'all_greater', 'args': ['all_rows', 'year', '1970'], 'result': True, 'ind': 0, 'tointer': 'for the year records of all rows , all of them are greater than 1970 .', 'tostr': 'all_greater { all_rows ; year ; 1970 } = true'} | all_greater { all_rows ; year ; 1970 } = true | for the year records of all rows , all of them are greater than 1970 . | 1 | 1 | {'all_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'year_3': 3, '1970_4': 4} | {'all_greater_0': 'all_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'year_3': 'year', '1970_4': '1970'} | {'all_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'year_3': [0], '1970_4': [0]} | ['year', 'tournament', 'venue', 'result', 'extra'] | [['1972', 'olympic games', 'munich , west germany', '7th', '100 m hurdles'], ['1974', 'european indoor championships', 'gothenburg , sweden', '2nd', '60 m hurdles'], ['1974', 'european championships', 'rome , italy', '2nd', '100 m hurdles'], ['1975', 'european indoor championships', 'katowice , poland', '4th', '60 m hurdles'], ['1978', 'european championships', 'prague , czechoslovakia', '6th', '100 m hurdles']] |
list of a1 grand prix seasons | https://en.wikipedia.org/wiki/List_of_A1_Grand_Prix_seasons | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12868148-2.html.csv | comparative | switzerland scored more points than germany in their respective grand prix season . | {'row_1': '3', 'row_2': '2', 'col': '13', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'switzerland'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to switzerland .', 'tostr': 'filter_eq { all_rows ; team ; switzerland }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; switzerland } ; points }', 'tointer': 'select the rows whose team record fuzzily matches to switzerland . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'germany'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to germany .', 'tostr': 'filter_eq { all_rows ; team ; germany }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; germany } ; points }', 'tointer': 'select the rows whose team record fuzzily matches to germany . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; team ; switzerland } ; points } ; hop { filter_eq { all_rows ; team ; germany } ; points } } = true', 'tointer': 'select the rows whose team record fuzzily matches to switzerland . take the points record of this row . select the rows whose team record fuzzily matches to germany . take the points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; team ; switzerland } ; points } ; hop { filter_eq { all_rows ; team ; germany } ; points } } = true | select the rows whose team record fuzzily matches to switzerland . take the points record of this row . select the rows whose team record fuzzily matches to germany . take the points 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, 'team_7': 7, 'switzerland_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'germany_12': 12, 'points_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', 'team_7': 'team', 'switzerland_8': 'switzerland', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'germany_12': 'germany', 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'switzerland_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'germany_12': [1], 'points_13': [3]} | ['season', 'team', 'racing team', 'chassis', 'engine', 'tyres', 'drivers', 'wins', 'sprints wins', 'main wins', 'poles', 'fastest laps', 'points'] | [['2005 - 06', 'france', 'dams', 'lola', 'zytek', 'cooper avon', 'alexandre prémat nicolas lapierre', '13', '7', '6', '4', '5', '172'], ['2006 - 07', 'germany', 'super nova racing', 'lola', 'zytek', 'cooper avon', 'nico hülkenberg christian vietoris', '9', '3', '6', '3', '3', '128'], ['2007 - 08', 'switzerland', 'max motorsport consulting', 'lola', 'zytek', 'cooper avon', 'neel jani', '4', '2', '2', '5', '5', '168'], ['2008 - 09', 'ireland', 'status grand prix', 'a1 gp', 'ferrari', 'michelin', 'adam carroll', '5', '3', '2', '6', '5', '112'], ['2009 - 10', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled', 'season cancelled']] |
wake forest demon deacons football , 1980 - 89 | https://en.wikipedia.org/wiki/Wake_Forest_Demon_Deacons_football%2C_1980%E2%80%9389 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15531181-15.html.csv | aggregation | the average attendance for the wake forest demon deacons games was 33151 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '33151', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '33151', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '33151'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 33151 } = true', 'tointer': 'the average of the attendance record of all rows is 33151 .'} | round_eq { avg { all_rows ; attendance } ; 33151 } = true | the average of the attendance record of all rows is 33151 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '33151_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '33151_5': '33151'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '33151_5': [1]} | ['date', 'opponent', 'location', 'result', 'attendance'] | [['09 / 12 / 1987', 'richmond', 'groves stadium winston - salem , nc', 'w 24 - 0', '14250'], ['09 / 19 / 1987', 'north carolina state', 'groves stadium winston - salem , nc', 'w 21 - 3', '23600'], ['09 / 26 / 1987', 'appalachian state', 'groves stadium winston - salem , nc', 'w 16 - 12', '33400'], ['10 / 01 / 1987', 'army', 'michie stadium west point , ny', 'w 17 - 13', '36690'], ['10 / 10 / 1987', 'north carolina', 'kenan memorial stadium chapel hill , nc', 'w 22 - 14', '50000'], ['10 / 17 / 1987', 'maryland', 'groves stadium winston - salem , nc', 'l 0 - 14', '25175'], ['10 / 24 / 1987', 'virginia', 'scott stadium charlottesville , va', 'l 21 - 35', '32500'], ['10 / 31 / 1987', '14 clemson', 'memorial stadium clemson , sc', 'l 17 - 31', '69711'], ['11 / 07 / 1987', 'duke', 'groves stadium winston - salem , nc', 'w 30 - 27', '23500'], ['11 / 14 / 1987', '14 south carolina', 'groves stadium winston - salem , nc', 'l 0 - 30', '34720'], ['11 / 21 / 1987', 'georgia tech', 'grant field atlanta , ga', 'w 33 - 6', '21114']] |
eiza gonzález | https://en.wikipedia.org/wiki/Eiza_Gonz%C3%A1lez | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16264878-4.html.csv | majority | most of elza gonzalez movies resulted in at least an award nomination . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'nominated', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'nominated'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to nominated .', 'tostr': 'most_eq { all_rows ; result ; nominated } = true'} | most_eq { all_rows ; result ; nominated } = true | for the result records of all rows , most of them fuzzily match to nominated . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'nominated_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'nominated_4': 'nominated'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'nominated_4': [0]} | ['year', 'award', 'nominated work', 'category', 'result'] | [['2007', 'premios oye !', 'lola érase una vez ( album )', 'artista revelacion', 'nominated'], ['2008', 'premios tv y novelas', 'lola , érase una vez', 'actriz revelacion del año', 'won'], ['2009', 'premio lo nuestro', 'lola érase una vez ( álbum )', 'revelación pop del año', 'won'], ['2011', 'kids choice awards méxico', 'sueña conmigo', 'personaje femenino favorito de una serie', 'nominated'], ['2011', 'kids choice awards argentina', 'sueña conmigo', 'mejor actriz', 'nominated'], ['2011', 'kids choice awards argentina', 'sueña conmigo', 'revelacion en tv', 'won'], ['2011', 'meus premios nick', 'sueña conmigo', 'mejor actriz', 'nominated'], ['2011', 'meus premios nick', 'sueña conmigo', 'cabello maluco', 'nominated'], ['2012', 'premios juventud', 'moda ( style )', 'quiero vestir como ella', 'nominated'], ['2012', 'premios celebrity e!', 'amores verdaderos', 'celebridad del año', 'nominated'], ['2013', 'premios juventud', 'me puedes pedir lo que sea ( feat marconi )', 'mejor tema novelero', 'won'], ['2013', 'premios juventud', 'amores verdaderos', 'chica que me quita el sueño', 'nominated'], ['2013', 'mtv millennial awards', 'eiza gonzález', 'bizcocho del año', 'nominated'], ['2013', 'kids choice awards méxico', 'eiza gonzález', 'solista favorito', 'won'], ['2013', 'kids choice awards méxico', 'eiza gonzález por epp en los croods', 'doblaje favorito en película', 'won']] |
1973 - 74 football league cup | https://en.wikipedia.org/wiki/1973%E2%80%9374_Football_League_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24887326-8.html.csv | unique | in england 's football league cup , the only game played on 21 - 11 - 1973 with an attendance below 10000 was tie number 6 . | {'scope': 'subset', 'row': '6', 'col': '5', 'col_other': '1,6', 'criterion': 'less_than', 'value': '10000', 'subset': {'col': '6', 'criterion': 'equal', 'value': '21 - 11 - 1973'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '21 - 11 - 1973'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 21 - 11 - 1973 }', 'tointer': 'select the rows whose date record fuzzily matches to 21 - 11 - 1973 .'}, 'attendance', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 21 - 11 - 1973 . among these rows , select the rows whose attendance record is less than 10000 .', 'tostr': 'filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } }', 'tointer': 'select the rows whose date record fuzzily matches to 21 - 11 - 1973 . among these rows , select the rows whose attendance record is less than 10000 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '21 - 11 - 1973'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 21 - 11 - 1973 }', 'tointer': 'select the rows whose date record fuzzily matches to 21 - 11 - 1973 .'}, 'attendance', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 21 - 11 - 1973 . among these rows , select the rows whose attendance record is less than 10000 .', 'tostr': 'filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 }'}, 'tie no'], 'result': '6', 'ind': 3, 'tostr': 'hop { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } ; tie no }'}, '6'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } ; tie no } ; 6 }', 'tointer': 'the tie no record of this unqiue row is 6 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } } ; eq { hop { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } ; tie no } ; 6 } } = true', 'tointer': 'select the rows whose date record fuzzily matches to 21 - 11 - 1973 . among these rows , select the rows whose attendance record is less than 10000 . there is only one such row in the table . the tie no record of this unqiue row is 6 .'} | and { only { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } } ; eq { hop { filter_less { filter_eq { all_rows ; date ; 21 - 11 - 1973 } ; attendance ; 10000 } ; tie no } ; 6 } } = true | select the rows whose date record fuzzily matches to 21 - 11 - 1973 . among these rows , select the rows whose attendance record is less than 10000 . there is only one such row in the table . the tie no record of this unqiue row is 6 . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_less_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'date_8': 8, '21 - 11 - 1973_9': 9, 'attendance_10': 10, '10000_11': 11, 'eq_4': 4, 'num_hop_3': 3, 'tie no_12': 12, '6_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_less_1': 'filter_less', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'date_8': 'date', '21 - 11 - 1973_9': '21 - 11 - 1973', 'attendance_10': 'attendance', '10000_11': '10000', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'tie no_12': 'tie no', '6_13': '6'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_less_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'date_8': [0], '21 - 11 - 1973_9': [0], 'attendance_10': [1], '10000_11': [1], 'eq_4': [5], 'num_hop_3': [4], 'tie no_12': [3], '6_13': [4]} | ['tie no', 'home team', 'score 1', 'away team', 'attendance', 'date'] | [['1', 'york city', '0 - 0', 'manchester city', '15360', '21 - 11 - 1973'], ['2', 'queens park rangers', '0 - 3', 'plymouth argyle', '19072', '20 - 11 - 1973'], ['3', 'southampton', '0 - 2', 'norwich city', '14415', '21 - 11 - 1973'], ['4', 'ipswich town', '1 - 3', 'birmingham city', '12241', '21 - 11 - 1973'], ['5', 'wolverhampton wanderers', '5 - 1', 'exeter city', '7623', '20 - 11 - 1973'], ['6', 'millwall', '3 - 1', 'luton town', '8777', '21 - 11 - 1973'], ['7', 'coventry city', '2 - 1', 'stoke city', '17485', '20 - 11 - 1973']] |
busiest airports in the united kingdom by total passenger traffic | https://en.wikipedia.org/wiki/Busiest_airports_in_the_United_Kingdom_by_total_passenger_traffic | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13836704-8.html.csv | aggregation | the busiest airports in the uk have an average of 44574 transit passengers . | {'scope': 'all', 'col': '7', 'type': 'average', 'result': '44574', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'transit passengers'], 'result': '44574', 'ind': 0, 'tostr': 'avg { all_rows ; transit passengers }'}, '44574'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; transit passengers } ; 44574 } = true', 'tointer': 'the average of the transit passengers record of all rows is 44574 .'} | round_eq { avg { all_rows ; transit passengers } ; 44574 } = true | the average of the transit passengers record of all rows is 44574 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'transit passengers_4': 4, '44574_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'transit passengers_4': 'transit passengers', '44574_5': '44574'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'transit passengers_4': [0], '44574_5': [1]} | ['rank', 'airport', 'total passengers', '% change 2006 / 2007', 'international passengers', 'domestic passengers', 'transit passengers', 'aircraft movements', 'freight ( metric tonnes )'] | [['1', 'london heathrow', '68066028', '0.8 %', '62098911', '5753476', '213641', '481476', '1310987'], ['2', 'london gatwick', '35216113', '3.1 %', '31142002', '4023402', '50709', '266550', '171078'], ['3', 'london stansted', '23779697', '0.4 %', '21204946', '2554304', '20447', '208462', '203747'], ['4', 'manchester', '22112625', '1.5 %', '18662468', '3229255', '220902', '222703', '165366'], ['5', 'london luton', '9927321', '5.3 %', '8427894', '1491467', '7960', '120238', '38095'], ['6', 'birmingham airport', '9226340', '0.9 %', '7592240', '1541815', '92285', '114679', '13585'], ['7', 'edinburgh', '9047558', '5.1 %', '3417891', '5619309', '10358', '128172', '19292'], ['8', 'glasgow international', '8795727', '0.6 %', '4131512', '4594575', '69640', '108305', '4276'], ['9', 'bristol', '5926774', '2.9 %', '4608290', '1275566', '42918', '76428', '20'], ['10', 'newcastle', '5650716', '4.0 %', '3948594', '1675013', '27109', '79200', '785'], ['11', 'liverpool', '5468510', '10.2 %', '4636149', '827085', '5276', '86668', '3709'], ['12', 'east midlands', '5413360', '14.5 %', '4709855', '696649', '6856', '93989', '274753'], ['13', 'belfast international', '5272664', '4.6 %', '1788807', '3447248', '36609', '77395', '38429'], ['14', 'aberdeen', '3412257', '7.8 %', '1475988', '1935152', '1117', '121927', '3434'], ['15', 'london city', '2912123', '23.5 %', '2214884', '697239', '0', '91177', '0'], ['16', 'leeds bradford', '2881539', '3.2 %', '2229283', '630575', '21681', '65249', '109'], ['17', 'glasgow prestwick', '2422332', '1.0 %', '1827592', '593117', '1623', '47910', '31517'], ['18', 'belfast city', '2186993', '3.9 %', '93547', '2093320', '126', '43022', '1057'], ['19', 'cardiff', '2111148', '4.3 %', '1665247', '428260', '17641', '43963', '2391']] |
aberdeen , south dakota | https://en.wikipedia.org/wiki/Aberdeen%2C_South_Dakota | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2709-4.html.csv | count | in aberdeen , south dakota , when the city of license was aberdeen , there were three occasions where armada media was the owner . | {'scope': 'subset', 'criterion': 'equal', 'value': 'armada media', 'result': '3', 'col': '5', 'subset': {'col': '7', 'criterion': 'equal', 'value': 'aberdeen'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city of license', 'aberdeen'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; city of license ; aberdeen }', 'tointer': 'select the rows whose city of license record fuzzily matches to aberdeen .'}, 'owner', 'armada media'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose city of license record fuzzily matches to aberdeen . among these rows , select the rows whose owner record fuzzily matches to armada media .', 'tostr': 'filter_eq { filter_eq { all_rows ; city of license ; aberdeen } ; owner ; armada media }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; city of license ; aberdeen } ; owner ; armada media } }', 'tointer': 'select the rows whose city of license record fuzzily matches to aberdeen . among these rows , select the rows whose owner record fuzzily matches to armada media . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; city of license ; aberdeen } ; owner ; armada media } } ; 3 } = true', 'tointer': 'select the rows whose city of license record fuzzily matches to aberdeen . among these rows , select the rows whose owner record fuzzily matches to armada media . the number of such rows is 3 .'} | eq { count { filter_eq { filter_eq { all_rows ; city of license ; aberdeen } ; owner ; armada media } } ; 3 } = true | select the rows whose city of license record fuzzily matches to aberdeen . among these rows , select the rows whose owner record fuzzily matches to armada media . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'city of license_6': 6, 'aberdeen_7': 7, 'owner_8': 8, 'armada media_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'city of license_6': 'city of license', 'aberdeen_7': 'aberdeen', 'owner_8': 'owner', 'armada media_9': 'armada media', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'city of license_6': [0], 'aberdeen_7': [0], 'owner_8': [1], 'armada media_9': [1], '3_10': [3]} | ['frequency', 'call sign', 'name', 'format', 'owner', 'target city / market', 'city of license'] | [['89.7 fm', 'k209fr', 'effect radio', 'christian rock', 'the river christian fellowship', 'aberdeen', 'aberdeen'], ['90.9 fm', 'kdsd', 'south dakota public broadcasting', 'public radio', 'south dakota public broadcasting', 'aberdeen', 'pierpont'], ['91.7 fm', 'k219 cm', 'south dakota public broadcasting', 'public radio', 'south dakota public broadcasting', 'aberdeen', 'aberdeen'], ['94.1 fm', 'ksdn', '94.1 the rock', 'mainstream rock', 'armada media', 'aberdeen', 'aberdeen'], ['94.5 fm', 'k233bn', 'life 97.9', 'christian', 'northwestern college', 'aberdeen', 'aberdeen'], ['94.9 fm', 'klrj', 'k - love', 'christian', 'educational media foundation', 'aberdeen', 'aberdeen'], ['97.7 fm', 'knbz', 'sunny 97.7', 'adult contemporary', 'armada media', 'aberdeen', 'redfield'], ['98.5 fm', 'k253ab', 'praise fm', 'christian', 'christian heritage broadcasting', 'aberdeen', 'aberdeen'], ['103.7 fm', 'kgim - fm', 'pheasant country 103', 'country', 'armada media', 'aberdeen', 'redfield'], ['105.5 fm', 'kmom', 'dakota 105.5', 'country', 'dakota broadcasting', 'aberdeen', 'roscoe'], ['106.7 fm', 'kbfo', 'point fm', 'top 40', 'armada media', 'aberdeen', 'aberdeen'], ['107.1 fm', 'k296fw', 'espn radio 1420 / 107.1', 'sports', 'armada media', 'aberdeen', 'aberdeen']] |
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