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media in brandon , manitoba
|
https://en.wikipedia.org/wiki/Media_in_Brandon%2C_Manitoba
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18410352-1.html.csv
|
count
|
there are two stations that play country music in brandon .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'country', 'result': '2', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'format', 'country'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose format record fuzzily matches to country .', 'tostr': 'filter_eq { all_rows ; format ; country }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; format ; country } }', 'tointer': 'select the rows whose format record fuzzily matches to country . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; format ; country } } ; 2 } = true', 'tointer': 'select the rows whose format record fuzzily matches to country . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; format ; country } } ; 2 } = true
|
select the rows whose format record fuzzily matches to country . 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, 'format_5': 5, 'country_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', 'format_5': 'format', 'country_6': 'country', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'format_5': [0], 'country_6': [0], '2_7': [2]}
|
['frequency', 'call sign', 'branding', 'format', 'owner']
|
[['am 880', 'cklq', 'cklq 880', 'country', 'riding mountain broadcasting'], ['fm 91.5', 'ciwm - fm', 'nci', 'first nations community', 'native communications inc'], ['fm 92.7', 'cbws - fm', 'cbc radio 2', 'public music', 'canadian broadcasting corporation'], ['fm 94.7', 'cklf - fm', 'star fm', 'hot adult contemporary', 'riding mountain broadcasting'], ['fm 96.1', 'ckx - fm', 'bob fm', 'adult hits', 'bell media'], ['fm 97.9', 'cbwv - fm', 'cbc radio one', 'news / information', 'canadian broadcasting corporation'], ['fm 99.5', 'cksb - 8 - fm', 'première chaîne', 'french news / information', 'canadian broadcasting corporation'], ['fm 101.1', 'ckxa - fm', '101 the farm', 'country', 'bell media'], ['fm 106.5', 'cjjj - fm', 'cj106', 'campus radio', 'assiniboine community college']]
|
hisar ( lok sabha constituency )
|
https://en.wikipedia.org/wiki/Hisar_%28Lok_Sabha_constituency%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17922483-1.html.csv
|
comparative
|
uchana kalan has a greater number of electorates than bawani khera .
|
{'row_1': '1', 'row_2': '9', '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', 'name', 'uchana kalan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to uchana kalan .', 'tostr': 'filter_eq { all_rows ; name ; uchana kalan }'}, 'number of electorates ( 2009 )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; uchana kalan } ; number of electorates ( 2009 ) }', 'tointer': 'select the rows whose name record fuzzily matches to uchana kalan . take the number of electorates ( 2009 ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'bawani khera'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to bawani khera .', 'tostr': 'filter_eq { all_rows ; name ; bawani khera }'}, 'number of electorates ( 2009 )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; bawani khera } ; number of electorates ( 2009 ) }', 'tointer': 'select the rows whose name record fuzzily matches to bawani khera . take the number of electorates ( 2009 ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; uchana kalan } ; number of electorates ( 2009 ) } ; hop { filter_eq { all_rows ; name ; bawani khera } ; number of electorates ( 2009 ) } } = true', 'tointer': 'select the rows whose name record fuzzily matches to uchana kalan . take the number of electorates ( 2009 ) record of this row . select the rows whose name record fuzzily matches to bawani khera . take the number of electorates ( 2009 ) record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; name ; uchana kalan } ; number of electorates ( 2009 ) } ; hop { filter_eq { all_rows ; name ; bawani khera } ; number of electorates ( 2009 ) } } = true
|
select the rows whose name record fuzzily matches to uchana kalan . take the number of electorates ( 2009 ) record of this row . select the rows whose name record fuzzily matches to bawani khera . take the number of electorates ( 2009 ) record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name_7': 7, 'uchana kalan_8': 8, 'number of electorates (2009)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'bawani khera_12': 12, 'number of electorates (2009)_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'uchana kalan_8': 'uchana kalan', 'number of electorates (2009)_9': 'number of electorates ( 2009 )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'bawani khera_12': 'bawani khera', 'number of electorates (2009)_13': 'number of electorates ( 2009 )'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'uchana kalan_8': [0], 'number of electorates (2009)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'bawani khera_12': [1], 'number of electorates (2009)_13': [3]}
|
['constituency number', 'name', 'reserved for ( sc / st / none )', 'district', 'number of electorates ( 2009 )']
|
[['37', 'uchana kalan', 'none', 'jind', '154284'], ['47', 'adampur', 'none', 'hisar', '123558'], ['48', 'uklana', 'sc', 'hisar', '147491'], ['49', 'narnaund', 'none', 'hisar', '152958'], ['50', 'hansi', 'none', 'hisar', '133581'], ['51', 'barwala', 'none', 'hisar', '119790'], ['52', 'hisar', 'none', 'hisar', '101595'], ['53', 'nalwa', 'none', 'hisar', '115472'], ['59', 'bawani khera', 'sc', 'bhiwani', '145965'], ['total :', 'total :', 'total :', 'total :', '1194694']]
|
list of cities , towns and villages in vojvodina
|
https://en.wikipedia.org/wiki/List_of_cities%2C_towns_and_villages_in_Vojvodina
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2562572-50.html.csv
|
aggregation
|
the average population of cities , towns and villages in vojvodina is 706.72 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '706.73', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'population ( 2011 )'], 'result': '706.73', 'ind': 0, 'tostr': 'avg { all_rows ; population ( 2011 ) }'}, '706.73'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; population ( 2011 ) } ; 706.73 } = true', 'tointer': 'the average of the population ( 2011 ) record of all rows is 706.73 .'}
|
round_eq { avg { all_rows ; population ( 2011 ) } ; 706.73 } = true
|
the average of the population ( 2011 ) record of all rows is 706.73 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'population (2011)_4': 4, '706.73_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'population (2011)_4': 'population ( 2011 )', '706.73_5': '706.73'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'population (2011)_4': [0], '706.73_5': [1]}
|
['settlement', 'cyrillic name other names', 'type', 'population ( 2011 )', 'largest ethnic group ( 2002 )', 'dominant religion ( 2002 )']
|
[['irig', 'ириг', 'town', '4415', 'serbs', 'orthodox christianity'], ['dobrodol', 'добродол ( hungarian : dobradópuszta )', 'village', '107', 'hungarians', 'catholic christianity'], ['grgetek', 'гргетек', 'village', '76', 'serbs', 'orthodox christianity'], ['jazak', 'јазак', 'village', '960', 'serbs', 'orthodox christianity'], ['krušedol prnjavor', 'крушедол прњавор', 'village', '234', 'serbs', 'orthodox christianity'], ['krušedol selo', 'крушедол село', 'village', '340', 'serbs', 'orthodox christianity'], ['mala remeta', 'мала ремета', 'village', '130', 'serbs', 'orthodox christianity'], ['neradin', 'нерадин', 'village', '475', 'serbs', 'orthodox christianity'], ['rivica', 'ривица', 'village', '620', 'serbs', 'orthodox christianity'], ['šatrinci', 'шатринци ( hungarian : satrinca )', 'village', '373', 'hungarians', 'catholic christianity'], ['velika remeta', 'велика ремета', 'village', '44', 'serbs', 'orthodox christianity']]
|
2006 canary foundation grand prix of san jose
|
https://en.wikipedia.org/wiki/2006_Canary_Foundation_Grand_Prix_of_San_Jose
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10600299-1.html.csv
|
aggregation
|
for the 2006 canary foundation grand prix of san jose , team australia members averaged a best time of 50.12 .
|
{'scope': 'subset', 'col': '5', 'type': 'average', 'result': '50.12', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'team australia'}}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'team australia'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; team ; team australia }', 'tointer': 'select the rows whose team record fuzzily matches to team australia .'}, 'best'], 'result': '50.12', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; team ; team australia } ; best }'}, '50.12'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; team ; team australia } ; best } ; 50.12 } = true', 'tointer': 'select the rows whose team record fuzzily matches to team australia . the average of the best record of these rows is 50.12 .'}
|
round_eq { avg { filter_eq { all_rows ; team ; team australia } ; best } ; 50.12 } = true
|
select the rows whose team record fuzzily matches to team australia . the average of the best record of these rows is 50.12 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'team_5': 5, 'team australia_6': 6, 'best_7': 7, '50.12_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'team_5': 'team', 'team australia_6': 'team australia', 'best_7': 'best', '50.12_8': '50.12'}
|
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'team_5': [0], 'team australia_6': [0], 'best_7': [1], '50.12_8': [2]}
|
['name', 'team', 'qual 1', 'qual 2', 'best']
|
[['sébastien bourdais', 'newman / haas racing', '50.160', '48.989', '48.989'], ['paul tracy', 'forsythe racing', '50.156', '49.810', '49.810'], ['a j allmendinger', 'forsythe racing', '50.246', '49.264', '49.264'], ['cristiano da matta', 'rusport', '50.734', '49.659', '49.659'], ['oriol servià', 'pkv racing', '50.388', '49.813', '49.813'], ['will power', 'team australia', '-', '49.867', '49.867'], ['bruno junqueira', 'newman / haas racing', '50.587', '49.887', '49.887'], ['andrew ranger', 'mi - jack conquest racing', '50.925', '49.962', '49.962'], ['dan clarke', 'cte racing - hvm', '50.278', '50.161', '50.161'], ['mario domínguez', 'dale coyne racing', '50.861', '50.215', '50.215'], ['nelson philippe', 'cte racing - hvm', '50.440', '50.312', '50.312'], ['justin wilson', 'rusport', '50.504', '50.341', '50.341'], ['alex tagliani', 'team australia', '50.373', '-', '50.373'], ['charles zwolsman', 'mi - jack conquest racing', '51.004', '50.435', '50.435'], ['katherine legge', 'pkv racing', '51.329', '50.473', '50.473'], ['jan heylen', 'dale coyne racing', '51.591', '50.838', '50.838'], ['nicky pastorelli', 'rocketsports racing', '52.344', '51.628', '51.628']]
|
2007 - 08 washington capitals season
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Washington_Capitals_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11772462-4.html.csv
|
count
|
washington was the home team 7 times in november .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'washington', 'result': '7', 'col': '4', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home', 'washington'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home record fuzzily matches to washington .', 'tostr': 'filter_eq { all_rows ; home ; washington }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; home ; washington } }', 'tointer': 'select the rows whose home record fuzzily matches to washington . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; home ; washington } } ; 7 } = true', 'tointer': 'select the rows whose home record fuzzily matches to washington . the number of such rows is 7 .'}
|
eq { count { filter_eq { all_rows ; home ; washington } } ; 7 } = true
|
select the rows whose home record fuzzily matches to washington . the number of such rows is 7 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'home_5': 5, 'washington_6': 6, '7_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'home_5': 'home', 'washington_6': 'washington', '7_7': '7'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'home_5': [0], 'washington_6': [0], '7_7': [2]}
|
['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record']
|
[['november 1', 'washington', '0 - 2', 'ny rangers', 'kolzig', '18200', '5 - 7 - 0'], ['november 2', 'philadelphia', '3 - 2', 'washington', 'kolzig', '16055', '5 - 8 - 0'], ['november 5', 'washington', '0 - 5', 'carolina', 'kolzig', '12171', '5 - 9 - 0'], ['november 6', 'washington', '1 - 2', 'atlanta', 'johnson', '15530', '5 - 9 - 1'], ['november 8', 'washington', '4 - 1', 'ottawa', 'kolzig', '19666', '6 - 9 - 1'], ['november 10', 'tampa bay', '5 - 2', 'washington', 'kolzig', '14617', '6 - 10 - 1'], ['november 15', 'washington', '1 - 2', 'florida', 'kolzig', '12101', '6 - 11 - 1'], ['november 16', 'washington', '2 - 5', 'tampa bay', 'kolzig', '19526', '6 - 12 - 1'], ['november 19', 'florida', '4 - 3', 'washington', 'kolzig', '13411', '6 - 13 - 1'], ['november 21', 'atlanta', '5 - 1', 'washington', 'kolzig', '11669', '6 - 14 - 1'], ['november 23', 'washington', '4 - 3', 'philadelphia', 'kolzig', '19727', '7 - 14 - 1'], ['november 24', 'carolina', '2 - 5', 'washington', 'kolzig', '13650', '8 - 14 - 1'], ['november 26', 'buffalo', '3 - 1', 'washington', 'kolzig', '11204', '8 - 15 - 1'], ['november 28', 'florida', '2 - 1', 'washington', 'kolzig', '10526', '8 - 15 - 2'], ['november 30', 'washington', '3 - 4', 'carolina', 'kolzig', '16386', '8 - 16 - 2']]
|
ranked list of norwegian counties
|
https://en.wikipedia.org/wiki/Ranked_list_of_Norwegian_counties
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1064198-3.html.csv
|
unique
|
oslo county was the only county with a percentage higher than 10 % in 1960 .
|
{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'greater_than', 'value': '10.0', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', '% ( 1960 )', '10.0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose % ( 1960 ) record is greater than 10.0 .', 'tostr': 'filter_greater { all_rows ; % ( 1960 ) ; 10.0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; % ( 1960 ) ; 10.0 } }', 'tointer': 'select the rows whose % ( 1960 ) record is greater than 10.0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', '% ( 1960 )', '10.0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose % ( 1960 ) record is greater than 10.0 .', 'tostr': 'filter_greater { all_rows ; % ( 1960 ) ; 10.0 }'}, 'county'], 'result': 'oslo', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; % ( 1960 ) ; 10.0 } ; county }'}, 'oslo'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; % ( 1960 ) ; 10.0 } ; county } ; oslo }', 'tointer': 'the county record of this unqiue row is oslo .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; % ( 1960 ) ; 10.0 } } ; eq { hop { filter_greater { all_rows ; % ( 1960 ) ; 10.0 } ; county } ; oslo } } = true', 'tointer': 'select the rows whose % ( 1960 ) record is greater than 10.0 . there is only one such row in the table . the county record of this unqiue row is oslo .'}
|
and { only { filter_greater { all_rows ; % ( 1960 ) ; 10.0 } } ; eq { hop { filter_greater { all_rows ; % ( 1960 ) ; 10.0 } ; county } ; oslo } } = true
|
select the rows whose % ( 1960 ) record is greater than 10.0 . there is only one such row in the table . the county record of this unqiue row is oslo .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, '% (1960)_7': 7, '10.0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'county_9': 9, 'oslo_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', '% (1960)_7': '% ( 1960 )', '10.0_8': '10.0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'county_9': 'county', 'oslo_10': 'oslo'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], '% (1960)_7': [0], '10.0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'county_9': [2], 'oslo_10': [3]}
|
['rank', 'county', '% ( 1960 )', '% ( 2000 )', '% ( 2040 )']
|
[['1', 'oslo', '13.2', '11.3', '12.8'], ['2', 'akershus', '6.3', '10.4', '11.9'], ['3', 'hordaland', '9.4', '9.7', '10.2'], ['4', 'rogaland', '6.6', '8.3', '9.9'], ['5', 'sør - trøndelag', '5.8', '5.8', '6.0'], ['6', 'østfold', '5.6', '5.5', '5.5'], ['7', 'buskerud', '4.6', '5.2', '5.4'], ['8', 'møre og romsdal', '5.9', '5.4', '4.8'], ['9', 'nordland', '6.6', '5.3', '3.9'], ['10', 'vestfold', '4.8', '4.7', '4.7'], ['11', 'hedmark', '4.9', '4.1', '3.4'], ['12', 'oppland', '4.6', '4.0', '3.3'], ['13', 'vest - agder', '3.0', '3.4', '3.6'], ['14', 'telemark', '4.1', '3.6', '3.0'], ['15', 'troms', '3.5', '3.3', '2.7'], ['16', 'nord - trøndelag', '3.2', '2.8', '2.4'], ['17', 'aust - agder', '2.1', '2.2', '2.3'], ['18', 'sogn og fjordane', '2.8', '2.4', '1.8'], ['19', 'finnmark', '2.0', '1.6', '1.2'], ['sum', 'norway', '100.0', '100.0', '100.0']]
|
b " nemzeti bajnoks \ xc3 \ xa1g i ( men 's handball ) "
|
https://en.wikipedia.org/wiki/Nemzeti_Bajnoks%C3%A1g_I_%28men%27s_handball%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12777591-5.html.csv
|
count
|
there were two handball teams that only had won a single title .
|
{'scope': 'all', 'criterion': 'equal', 'value': '1', 'result': '2', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'titles', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose titles record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; titles ; 1 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; titles ; 1 } }', 'tointer': 'select the rows whose titles record is equal to 1 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; titles ; 1 } } ; 2 } = true', 'tointer': 'select the rows whose titles record is equal to 1 . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; titles ; 1 } } ; 2 } = true
|
select the rows whose titles record is equal to 1 . the number of such rows is 2 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'titles_5': 5, '1_6': 6, '2_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'titles_5': 'titles', '1_6': '1', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'titles_5': [0], '1_6': [0], '2_7': [2]}
|
['rank', 'city', 'titles', 'winning clubs', 'last victory']
|
[['1', 'budapest', '26', 'honvéd spartacus elektromos se vörös meteor újpest', '1991'], ['2', 'veszprém', '20', 'veszprém ( 20 )', '2012'], ['3', 'tatabánya', '4', 'tatabánya ( 4 )', '1984'], ['4', 'győr', '3', 'győr ( 3 )', '1990'], ['5', 'szeged', '2', 'szeged ( 2 )', '2007'], ['6', 'dunaújváros', '1', 'dunaferr se ( 1 )', '2000'], ['6', 'debrecen', '1', 'debrecen ( 1 )', '1975']]
|
new democratic party candidates , 2008 canadian federal election
|
https://en.wikipedia.org/wiki/New_Democratic_Party_candidates%2C_2008_Canadian_federal_election
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10953705-3.html.csv
|
count
|
for the new democratic party candidates in the 2008 canadian federal election , when the gender was female , there were two candidates from halifax .
|
{'scope': 'subset', 'criterion': 'equal', 'value': 'halifax', 'result': '2', 'col': '4', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'f'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'gender', 'f'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; gender ; f }', 'tointer': 'select the rows whose gender record fuzzily matches to f .'}, 'residence', 'halifax'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose gender record fuzzily matches to f . among these rows , select the rows whose residence record fuzzily matches to halifax .', 'tostr': 'filter_eq { filter_eq { all_rows ; gender ; f } ; residence ; halifax }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; gender ; f } ; residence ; halifax } }', 'tointer': 'select the rows whose gender record fuzzily matches to f . among these rows , select the rows whose residence record fuzzily matches to halifax . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; gender ; f } ; residence ; halifax } } ; 2 } = true', 'tointer': 'select the rows whose gender record fuzzily matches to f . among these rows , select the rows whose residence record fuzzily matches to halifax . the number of such rows is 2 .'}
|
eq { count { filter_eq { filter_eq { all_rows ; gender ; f } ; residence ; halifax } } ; 2 } = true
|
select the rows whose gender record fuzzily matches to f . among these rows , select the rows whose residence record fuzzily matches to halifax . the number of such rows is 2 .
|
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, 'gender_6': 6, 'f_7': 7, 'residence_8': 8, 'halifax_9': 9, '2_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', 'gender_6': 'gender', 'f_7': 'f', 'residence_8': 'residence', 'halifax_9': 'halifax', '2_10': '2'}
|
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'gender_6': [0], 'f_7': [0], 'residence_8': [1], 'halifax_9': [1], '2_10': [3]}
|
['riding', 'candidate', 'gender', 'residence', 'occupation', 'votes', 'rank']
|
[['cape breton-canso', 'mark macneill', 'm', 'inverness', 'government and business policy advisor', '7660', '3rd'], ['central nova', 'mary louise lorefice', 'f', 'antigonish', 'retired educator', '7659', '3rd'], ['cumberland-colchester-musquodoboit valley', 'karen olsson', 'f', 'north river', 'stay - at - home mother', '4874', '2nd'], ['dartmouth-cole harbour', 'brad pye', 'm', 'ottawa , on', 'senior political party program officer', '12793', '2nd'], ['halifax', 'megan leslie', 'f', 'halifax', 'community legal worker', '19252', '1st'], ['halifax west', 'tamara lorincz', 'f', 'halifax', 'director of nova scotia environment network', '12201', '2nd'], ['kings-hants', 'carol e harris', 'f', 'wolfville', 'university professor', '8291', '3rd'], ['sackville-eastern shore', 'peter stoffer', 'm', 'windsor junction', 'parliamentarian', '24279', '1st'], ["south shore-st margaret 's", 'gordon s earle', 'm', 'upper tantallon', 'retired public servant', '13456', '2nd'], ['sydney-victoria', 'wayne mckay', 'm', 'sydney', 'teacher', '8559', '2nd'], ['west nova', 'george barron', 'm', 'bear river', 'paramedic', '7097', '3rd']]
|
wjis
|
https://en.wikipedia.org/wiki/WJIS
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12499852-1.html.csv
|
superlative
|
wjis highest frequency among stations with erp above 25 is 106.1 .
|
{'scope': 'subset', 'col_superlative': '2', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '4', 'subset': {'col': '4', 'criterion': 'greater_than', 'value': '25'}}
|
{'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'erp w', '25'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; erp w ; 25 }', 'tointer': 'select the rows whose erp w record is greater than 25 .'}, 'frequency mhz'], 'result': '106.1', 'ind': 1, 'tostr': 'max { filter_greater { all_rows ; erp w ; 25 } ; frequency mhz }', 'tointer': 'select the rows whose erp w record is greater than 25 . the maximum frequency mhz record of these rows is 106.1 .'}, '106.1'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_greater { all_rows ; erp w ; 25 } ; frequency mhz } ; 106.1 } = true', 'tointer': 'select the rows whose erp w record is greater than 25 . the maximum frequency mhz record of these rows is 106.1 .'}
|
eq { max { filter_greater { all_rows ; erp w ; 25 } ; frequency mhz } ; 106.1 } = true
|
select the rows whose erp w record is greater than 25 . the maximum frequency mhz record of these rows is 106.1 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'max_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'erp w_5': 5, '25_6': 6, 'frequency mhz_7': 7, '106.1_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'max_1': 'max', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'erp w_5': 'erp w', '25_6': '25', 'frequency mhz_7': 'frequency mhz', '106.1_8': '106.1'}
|
{'eq_2': [3], 'result_3': [], 'max_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'erp w_5': [0], '25_6': [0], 'frequency mhz_7': [1], '106.1_8': [2]}
|
['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info']
|
[['w254ai', '98.7', 'auburndale , florida', '55', 'd', 'fcc'], ['w291ag', '106.1', 'highland city , florida', '27', 'd', 'fcc'], ['w224bu', '92.7', 'lake city , florida', '24', 'd', 'fcc'], ['w208ar', '89.5', 'lake placid , florida', '10', 'd', 'fcc'], ['w242ak', '96.3', 'lakeland , florida', '55', 'd', 'fcc'], ['w244bc', '96.7', 'ocala , florida', '10', 'd', 'fcc'], ['w237cw', '95.3', 'pinellas park , florida', '14', 'd', 'fcc'], ['w280ea', '103.9', 'ruskin , florida', '8', 'd', 'fcc'], ['w214ba', '90.7', 'sebring , florida', '80', 'd', 'fcc']]
|
ernie irvan
|
https://en.wikipedia.org/wiki/Ernie_Irvan
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1256150-1.html.csv
|
unique
|
ernie irvan was on the donlavey team for only one of his daytona 500 races in 1990 .
|
{'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'donlavey', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'donlavey'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to donlavey .', 'tostr': 'filter_eq { all_rows ; team ; donlavey }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; team ; donlavey } }', 'tointer': 'select the rows whose team record fuzzily matches to donlavey . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'donlavey'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to donlavey .', 'tostr': 'filter_eq { all_rows ; team ; donlavey }'}, 'year'], 'result': '1990', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; donlavey } ; year }'}, '1990'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; team ; donlavey } ; year } ; 1990 }', 'tointer': 'the year record of this unqiue row is 1990 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; team ; donlavey } } ; eq { hop { filter_eq { all_rows ; team ; donlavey } ; year } ; 1990 } } = true', 'tointer': 'select the rows whose team record fuzzily matches to donlavey . there is only one such row in the table . the year record of this unqiue row is 1990 .'}
|
and { only { filter_eq { all_rows ; team ; donlavey } } ; eq { hop { filter_eq { all_rows ; team ; donlavey } ; year } ; 1990 } } = true
|
select the rows whose team record fuzzily matches to donlavey . there is only one such row in the table . the year record of this unqiue row is 1990 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team_7': 7, 'donlavey_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1990_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team_7': 'team', 'donlavey_8': 'donlavey', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1990_10': '1990'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'team_7': [0], 'donlavey_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1990_10': [3]}
|
['year', 'manufacturer', 'start', 'finish', 'team']
|
[['1988', 'pontiac', 'dnq', 'dnq', 'ulrich'], ['1989', 'pontiac', '33', '41', 'ulrich'], ['1990', 'ford', '18', '13', 'donlavey'], ['1991', 'chevrolet', '2', '1', 'morgan - mcclure'], ['1992', 'chevrolet', '7', '28', 'morgan - mcclure'], ['1993', 'chevrolet', '8', '37', 'morgan - mcclure'], ['1994', 'ford', '3', '2', 'yates'], ['1996', 'ford', '2', '35', 'yates'], ['1997', 'ford', '5', '20', 'yates'], ['1998', 'pontiac', '10', '6', 'mb2'], ['1999', 'pontiac', '31', '14', 'mb2']]
|
list of awards and nominations received by sex and the city
|
https://en.wikipedia.org/wiki/List_of_awards_and_nominations_received_by_Sex_and_the_City
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12095272-5.html.csv
|
superlative
|
jenny bicks was the earliest nominee for best writing of an episodic comedy for " sex and the city . " .
|
{'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '3', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'year'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; year }'}, 'nominee ( s )'], 'result': 'jenny bicks', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; year } ; nominee ( s ) }'}, 'jenny bicks'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; year } ; nominee ( s ) } ; jenny bicks } = true', 'tointer': 'select the row whose year record of all rows is minimum . the nominee ( s ) record of this row is jenny bicks .'}
|
eq { hop { argmin { all_rows ; year } ; nominee ( s ) } ; jenny bicks } = true
|
select the row whose year record of all rows is minimum . the nominee ( s ) record of this row is jenny bicks .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, 'nominee (s)_6': 6, 'jenny bicks_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', 'nominee (s)_6': 'nominee ( s )', 'jenny bicks_7': 'jenny bicks'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], 'nominee (s)_6': [1], 'jenny bicks_7': [2]}
|
['year', 'category', 'nominee ( s )', 'episode', 'result']
|
[['1999', 'best writing - episodic comedy', 'jenny bicks', 'four women and a funeral', 'nominated'], ['1999', 'best writing - episodic comedy', 'cindy chupack', 'evolution', 'nominated'], ['2000', 'best writing - episodic comedy', 'cindy chupack', "attack of the 5 '10 woman", 'nominated'], ['2000', 'best writing - episodic comedy', 'michael patrick king', 'ex and the city', 'nominated'], ['2001', 'best writing - episodic comedy', 'cindy chupack', 'just say yes', 'nominated'], ['2001', 'best writing - episodic comedy', 'julie rottenberg and elisa zuritsky', 'my motherboard , my self', 'nominated'], ['2002', 'best writing - episodic comedy', 'cindy chupack', 'plus one is the loneliest number', 'nominated'], ['2002', 'best writing - episodic comedy', 'michael patrick king', 'i heart ny', 'nominated'], ['2002', 'best writing - episodic comedy', 'julie rottenberg and elisa zuritsky', 'change of a dress', 'nominated'], ['2003', 'best writing - episodic comedy', 'jenny bicks', "a woman 's right to shoes", 'nominated'], ['2004', 'best writing - episodic comedy', 'jenny bicks and cindy chupack', 'splat !', 'nominated'], ['2004', 'best writing - episodic comedy', 'julie rottenberg and elisa zuritsky', 'the ick factor', 'nominated']]
|
list of cities , towns and villages in vojvodina
|
https://en.wikipedia.org/wiki/List_of_cities%2C_towns_and_villages_in_Vojvodina
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2562572-12.html.csv
|
aggregation
|
the average population size of cities , towns , and villages in vojvodina was 4251 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '4251', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'population ( 2011 )'], 'result': '4251', 'ind': 0, 'tostr': 'avg { all_rows ; population ( 2011 ) }'}, '4251'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; population ( 2011 ) } ; 4251 } = true', 'tointer': 'the average of the population ( 2011 ) record of all rows is 4251 .'}
|
round_eq { avg { all_rows ; population ( 2011 ) } ; 4251 } = true
|
the average of the population ( 2011 ) record of all rows is 4251 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'population (2011)_4': 4, '4251_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'population (2011)_4': 'population ( 2011 )', '4251_5': '4251'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'population (2011)_4': [0], '4251_5': [1]}
|
['settlement', 'cyrillic name other names', 'type', 'population ( 2011 )', 'largest ethnic group ( 2002 )', 'dominant religion ( 2002 )']
|
[['bačka palanka', 'бачка паланка', 'town', '28239', 'serbs', 'orthodox christianity'], ['čelarevo', 'челарево', 'village', '4831', 'serbs', 'orthodox christianity'], ['despotovo', 'деспотово', 'village', '1853', 'serbs', 'orthodox christianity'], ['gajdobra', 'гајдобра', 'village', '2578', 'serbs', 'orthodox christianity'], ['karađorđevo', 'карађорђево', 'village', '738', 'serbs', 'orthodox christianity'], ['mladenovo', 'младеново', 'village', '2679', 'serbs', 'orthodox christianity'], ['neštin', 'нештин', 'village', '794', 'serbs', 'orthodox christianity'], ['nova gajdobra', 'нова гајдобра', 'village', '1220', 'serbs', 'orthodox christianity'], ['obrovac', 'обровац', 'village', '2944', 'serbs', 'orthodox christianity'], ['parage', 'параге', 'village', '921', 'serbs', 'orthodox christianity'], ['pivnice', 'пивнице ( slovak : pivnice )', 'village', '3337', 'slovaks', 'protestantism'], ['silbaš', 'силбаш', 'village', '2467', 'serbs', 'orthodox christianity'], ['tovariševo', 'товаришево', 'village', '2657', 'serbs', 'orthodox christianity']]
|
2007 georgia force season
|
https://en.wikipedia.org/wiki/2007_Georgia_Force_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11710574-4.html.csv
|
ordinal
|
john ritcher had the second most carries for the georgia force in 2007 .
|
{'row': '3', 'col': '2', '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', 'car', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; car ; 2 }'}, 'player'], 'result': 'john ritcher', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; car ; 2 } ; player }'}, 'john ritcher'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; car ; 2 } ; player } ; john ritcher } = true', 'tointer': 'select the row whose car record of all rows is 2nd maximum . the player record of this row is john ritcher .'}
|
eq { hop { nth_argmax { all_rows ; car ; 2 } ; player } ; john ritcher } = true
|
select the row whose car record of all rows is 2nd maximum . the player record of this row is john ritcher .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'car_5': 5, '2_6': 6, 'player_7': 7, 'john ritcher_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', 'car_5': 'car', '2_6': '2', 'player_7': 'player', 'john ritcher_8': 'john ritcher'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'car_5': [0], '2_6': [0], 'player_7': [1], 'john ritcher_8': [2]}
|
['player', 'car', 'yards', 'avg', "td 's", 'long']
|
[['matt huebner', '34', '122', '3.6', '5', '24'], ['troy bergeron', '10', '81', '8.1', '0', '19'], ['john ritcher', '20', '58', '2.9', '2', '21'], ['chris greisen', '14', '25', '1.8', '6', '12'], ['chris jackson', '9', '19', '2.1', '4', '8'], ['jarrick hillery', '11', '9', '8', '3', '4'], ['derek lee', '1', '2', '2', '0', '2'], ['bruce mcclure', '1', '1', '1', '1', '1'], ['james macpherson', '1', '1', '1', '1', '1']]
|
utah jazz all - time roster
|
https://en.wikipedia.org/wiki/Utah_Jazz_all-time_roster
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11545282-10.html.csv
|
count
|
a total of two players on the utah jazz all - time roster have the last name of james .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'james', 'result': '2', 'col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'james'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to james .', 'tostr': 'filter_eq { all_rows ; player ; james }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; player ; james } }', 'tointer': 'select the rows whose player record fuzzily matches to james . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; player ; james } } ; 2 } = true', 'tointer': 'select the rows whose player record fuzzily matches to james . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; player ; james } } ; 2 } = true
|
select the rows whose player record fuzzily matches to james . 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, 'player_5': 5, 'james_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', 'player_5': 'player', 'james_6': 'james', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'player_5': [0], 'james_6': [0], '2_7': [2]}
|
['player', 'nationality', 'position', 'years for jazz', 'school / club team']
|
[['mark jackson', 'united states', 'point guard', '2002 - 03', "st john 's"], ['dave jamerson', 'united states', 'guard - forward', '1993', 'ohio'], ['aaron james', 'united states', 'forward', '1974 - 79', 'grambling state'], ['henry james', 'united states', 'forward', '1993', "st mary 's ( tx )"], ['al jefferson', 'united states', 'forward - center', '2010 - present', 'prentiss high school'], ['eric johnson', 'united states', 'guard', '1989 - 90', 'nebraska'], ['ollie johnson', 'united states', 'forward', '1974 - 75', 'temple'], ['nate johnston', 'united states', 'forward', '1989 - 90', 'tampa'], ['jeff judkins', 'united states', 'guard', '1980 - 81', 'utah']]
|
1930 vfl season
|
https://en.wikipedia.org/wiki/1930_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10767641-16.html.csv
|
majority
|
all games of the 1930 vfl season were played on the 30th of august .
|
{'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': '30 august 1930', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'date', '30 august 1930'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them fuzzily match to 30 august 1930 .', 'tostr': 'most_eq { all_rows ; date ; 30 august 1930 } = true'}
|
most_eq { all_rows ; date ; 30 august 1930 } = true
|
for the date records of all rows , most of them fuzzily match to 30 august 1930 .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '30 august 1930_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '30 august 1930_4': '30 august 1930'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '30 august 1930_4': [0]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['essendon', '13.11 ( 89 )', 'melbourne', '12.7 ( 79 )', 'windy hill', '15000', '30 august 1930'], ['collingwood', '16.20 ( 116 )', 'footscray', '10.17 ( 77 )', 'victoria park', '10000', '30 august 1930'], ['carlton', '16.12 ( 108 )', 'st kilda', '15.7 ( 97 )', 'princes park', '20000', '30 august 1930'], ['richmond', '20.15 ( 135 )', 'north melbourne', '5.10 ( 40 )', 'punt road oval', '7000', '30 august 1930'], ['south melbourne', '12.16 ( 88 )', 'geelong', '15.14 ( 104 )', 'lake oval', '28000', '30 august 1930'], ['hawthorn', '3.16 ( 34 )', 'fitzroy', '15.14 ( 104 )', 'glenferrie oval', '7000', '30 august 1930']]
|
1974 buffalo bills season
|
https://en.wikipedia.org/wiki/1974_Buffalo_Bills_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16677874-2.html.csv
|
unique
|
in the 1974 buffalo bills season , for the games in october , the only one where the new england patriots were the opponent was on october 20th .
|
{'scope': 'subset', 'row': '6', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'new england patriots', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'oct'}}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'oct'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; oct }', 'tointer': 'select the rows whose date record fuzzily matches to oct .'}, 'opponent', 'new england patriots'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to oct . among these rows , select the rows whose opponent record fuzzily matches to new england patriots .', 'tostr': 'filter_eq { filter_eq { all_rows ; date ; oct } ; opponent ; new england patriots }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; date ; oct } ; opponent ; new england patriots } } = true', 'tointer': 'select the rows whose date record fuzzily matches to oct . among these rows , select the rows whose opponent record fuzzily matches to new england patriots . there is only one such row in the table .'}
|
only { filter_eq { filter_eq { all_rows ; date ; oct } ; opponent ; new england patriots } } = true
|
select the rows whose date record fuzzily matches to oct . among these rows , select the rows whose opponent record fuzzily matches to new england patriots . there is only one such row in the table .
|
3
|
3
|
{'only_2': 2, 'result_3': 3, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, 'oct_6': 6, 'opponent_7': 7, 'new england patriots_8': 8}
|
{'only_2': 'only', 'result_3': 'true', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', 'oct_6': 'oct', 'opponent_7': 'opponent', 'new england patriots_8': 'new england patriots'}
|
{'only_2': [3], 'result_3': [], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'oct_6': [0], 'opponent_7': [1], 'new england patriots_8': [1]}
|
['game', 'date', 'opponent', 'result', 'bills points', 'opponents', 'bills first downs', 'record', 'attendance']
|
[['1', 'sept 16', 'oakland raiders', 'win', '21', '20', '22', '1 - 0', '80020'], ['2', 'sept 22', 'miami dolphins', 'loss', '16', '24', '16', '1 - 1', '80020'], ['3', 'sept 29', 'new york jets', 'win', '16', '12', '17', '2 - 1', '76978'], ['4', 'oct 6', 'green bay packers', 'win', '27', '7', '22', '3 - 1', '56267'], ['5', 'oct 13', 'baltimore colts', 'win', '27', '14', '15', '4 - 1', '40626'], ['6', 'oct 20', 'new england patriots', 'win', '30', '28', '19', '5 - 1', '78935'], ['7', 'oct 27', 'chicago bears', 'win', '16', '6', '16', '6 - 1', '78084'], ['8', 'nov 3', 'new england patriots', 'win', '29', '28', '22', '7 - 1', '61279'], ['9', 'nov 10', 'houston oilers', 'loss', '9', '21', '16', '7 - 2', '79144'], ['10', 'nov 17', 'miami dolphins', 'loss', '28', '35', '16', '7 - 3', '69313'], ['11', 'nov 24', 'cleveland browns', 'win', '15', '10', '10', '8 - 3', '66504'], ['12', 'dec 1', 'baltimore colts', 'win', '6', '0', '9', '9 - 3', '75325'], ['13', 'dec 8', 'new york jets', 'loss', '10', '20', '12', '9 - 4', '61091']]
|
swimming at the 2007 world aquatics championships - men 's 200 metre freestyle
|
https://en.wikipedia.org/wiki/Swimming_at_the_2007_World_Aquatics_Championships_%E2%80%93_Men%27s_200_metre_freestyle
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10563642-3.html.csv
|
comparative
|
nicola cassio had a faster 100 m swimming time than amaury leveaux .
|
{'row_1': '8', 'row_2': '14', 'col': '6', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'nicola cassio'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to nicola cassio .', 'tostr': 'filter_eq { all_rows ; name ; nicola cassio }'}, '100 m'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; nicola cassio } ; 100 m }', 'tointer': 'select the rows whose name record fuzzily matches to nicola cassio . take the 100 m record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'amaury leveaux'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to amaury leveaux .', 'tostr': 'filter_eq { all_rows ; name ; amaury leveaux }'}, '100 m'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; amaury leveaux } ; 100 m }', 'tointer': 'select the rows whose name record fuzzily matches to amaury leveaux . take the 100 m record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; nicola cassio } ; 100 m } ; hop { filter_eq { all_rows ; name ; amaury leveaux } ; 100 m } } = true', 'tointer': 'select the rows whose name record fuzzily matches to nicola cassio . take the 100 m record of this row . select the rows whose name record fuzzily matches to amaury leveaux . take the 100 m record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; name ; nicola cassio } ; 100 m } ; hop { filter_eq { all_rows ; name ; amaury leveaux } ; 100 m } } = true
|
select the rows whose name record fuzzily matches to nicola cassio . take the 100 m record of this row . select the rows whose name record fuzzily matches to amaury leveaux . take the 100 m 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, 'name_7': 7, 'nicola cassio_8': 8, '100 m_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'amaury leveaux_12': 12, '100 m_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', 'name_7': 'name', 'nicola cassio_8': 'nicola cassio', '100 m_9': '100 m', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'amaury leveaux_12': 'amaury leveaux', '100 m_13': '100 m'}
|
{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'nicola cassio_8': [0], '100 m_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'amaury leveaux_12': [1], '100 m_13': [3]}
|
['rank', 'heat', 'lane', 'name', 'nationality', '100 m', '150 m', 'time']
|
[['1', '2', '4', 'pieter van den hoogenband', 'netherlands', '51.16', '1:18.66', '1:46.33'], ['2', '1', '4', 'michael phelps', 'united states', '52.48', '1:20.10', '1:46.75'], ['3', '2', '2', 'massimiliano rosolino', 'italy', '52.13', '1:19.48', '1:47.44'], ['4', '1', '5', 'kenrick monk', 'australia', '52.96', '1:20.64', '1:47.45'], ['5', '2', '5', 'park tae - hwan', 'south korea', '52.91', '1:20.58', '1:47.83'], ['6', '1', '7', 'zhang lin', 'china', '53.25', '1:21.26', '1:48.29'], ['7', '2', '7', 'paul biedermann', 'germany', '53.28', '1:20.97', '1:48.43'], ['8', '2', '3', 'nicola cassio', 'italy', '53.24', '1:20.83', '1:48.47'], ['9', '2', '8', 'dominik koll', 'austria', '52.66', '1:20.50', '1:48.50'], ['10', '1', '2', 'brian johns', 'canada', '53.28', '1:21.22', '1:48.51'], ['11', '2', '6', 'dominik meichtry', 'switzerland', '52.87', '1:20.48', '1:48.54'], ['12', '1', '1', 'david carry', 'great britain', '52.87', '1:20.94', '1:48.71'], ['13', '2', '1', 'patrick murphy', 'australia', '52.92', '1:20.89', '1:48.75'], ['14', '1', '3', 'amaury leveaux', 'france', '53.28', '1:21.45', '1:48.81'], ['15', '1', '8', 'lã ¡ szlã cubic cseh', 'hungary', '52.92', '1:20.95', '1:48.89'], ['16', '1', '6', 'brent hayden', 'canada', '53.03', '1:20.99', '1:48.92']]
|
united states house of representatives elections , 1864
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1864
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1434834-3.html.csv
|
count
|
in the us house of representatives 1864 election , two of the democratic representatives from ohio were re-elected .
|
{'scope': 'subset', 'criterion': 'equal', 'value': 're-elected', 'result': '2', 'col': '5', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'democratic'}}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; democratic }', 'tointer': 'select the rows whose party record fuzzily matches to democratic .'}, 'result', 're-elected'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose result record fuzzily matches to re-elected .', 'tostr': 'filter_eq { filter_eq { all_rows ; party ; democratic } ; result ; re-elected }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; party ; democratic } ; result ; re-elected } }', 'tointer': 'select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose result record fuzzily matches to re-elected . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; party ; democratic } ; result ; re-elected } } ; 2 } = true', 'tointer': 'select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose result record fuzzily matches to re-elected . the number of such rows is 2 .'}
|
eq { count { filter_eq { filter_eq { all_rows ; party ; democratic } ; result ; re-elected } } ; 2 } = true
|
select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose result record fuzzily matches to re-elected . the number of such rows is 2 .
|
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, 'party_6': 6, 'democratic_7': 7, 'result_8': 8, 're-elected_9': 9, '2_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', 'party_6': 'party', 'democratic_7': 'democratic', 'result_8': 'result', 're-elected_9': 're-elected', '2_10': '2'}
|
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'party_6': [0], 'democratic_7': [0], 'result_8': [1], 're-elected_9': [1], '2_10': [3]}
|
['district', 'incumbent', 'party', 'first elected', 'result']
|
[['ohio 1', 'george h pendleton', 'democratic', '1856', 'retired republican gain'], ['ohio 2', 'alexander long', 'democratic', '1862', 'lost re - nomination republican gain'], ['ohio 3', 'robert c schenck', 'republican', '1862', 're - elected'], ['ohio 4', 'john f mckinney', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 5', 'francis c le blond', 'democratic', '1862', 're - elected'], ['ohio 6', 'chilton a white', 'democratic', '1860', 'lost re - election republican gain'], ['ohio 7', 'samuel s cox', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 8', 'william johnston', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 9', 'warren p noble', 'democratic', '1860', 'lost re - election republican gain'], ['ohio 10', 'james m ashley', 'republican', '1862', 're - elected'], ['ohio 11', 'wells a hutchins', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 12', 'william e finck', 'democratic', '1862', 're - elected'], ['ohio 13', "john o'neill", 'democratic', '1862', 'retired republican gain'], ['ohio 14', 'george bliss', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 15', 'james r morris', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 16', 'joseph w white', 'democratic', '1882', 'lost re - election republican gain'], ['ohio 17', 'ephraim r eckley', 'republican', '1862', 're - elected'], ['ohio 18', 'rufus p spalding', 'republican', '1862', 're - elected'], ['ohio 19', 'james a garfield', 'republican', '1862', 're - elected']]
|
91st united states congress
|
https://en.wikipedia.org/wiki/91st_United_States_Congress
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1204065-2.html.csv
|
unique
|
the illinois 1st seat was the only seat in the 91st united states congress that was left vacant .
|
{'scope': 'all', 'row': '9', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'vacant', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'vacant'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose successor record fuzzily matches to vacant .', 'tostr': 'filter_eq { all_rows ; successor ; vacant }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; successor ; vacant } }', 'tointer': 'select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'vacant'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose successor record fuzzily matches to vacant .', 'tostr': 'filter_eq { all_rows ; successor ; vacant }'}, 'district'], 'result': 'illinois 1st', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; successor ; vacant } ; district }'}, 'illinois 1st'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; successor ; vacant } ; district } ; illinois 1st }', 'tointer': 'the district record of this unqiue row is illinois 1st .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; successor ; vacant } } ; eq { hop { filter_eq { all_rows ; successor ; vacant } ; district } ; illinois 1st } } = true', 'tointer': 'select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table . the district record of this unqiue row is illinois 1st .'}
|
and { only { filter_eq { all_rows ; successor ; vacant } } ; eq { hop { filter_eq { all_rows ; successor ; vacant } ; district } ; illinois 1st } } = true
|
select the rows whose successor record fuzzily matches to vacant . there is only one such row in the table . the district record of this unqiue row is illinois 1st .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'successor_7': 7, 'Vacant_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'district_9': 9, 'illinois 1st_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'successor_7': 'successor', 'Vacant_8': 'vacant', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'district_9': 'district', 'illinois 1st_10': 'illinois 1st'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'successor_7': [0], 'Vacant_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'district_9': [2], 'illinois 1st_10': [3]}
|
['district', 'vacator', 'reason for change', 'successor', 'date successor seated']
|
[['tennessee 8th', 'robert a everett ( d )', 'died january 26 , 1969', 'ed jones ( d )', 'march 25 , 1969'], ['massachusetts 6th', 'william h bates ( r )', 'died june 22 , 1969', 'michael j harrington ( d )', 'september 30 , 1969'], ['illinois 6th', 'daniel j ronan ( d )', 'died august 13 , 1969', 'george w collins ( d )', 'november 3 , 1970'], ['california 24th', 'glenard p lipscomb ( r )', 'died february 1 , 1970', 'john h rousselot ( r )', 'june 30 , 1970'], ['california 35th', 'james b utt ( r )', 'died march 1 , 1970', 'john g schmitz ( r )', 'june 30 , 1970'], ['connecticut 2nd', 'william st onge ( d )', 'died may 1 , 1970', 'robert h steele ( r )', 'november 3 , 1970'], ['ohio 19th', 'michael j kirwan ( d )', 'died july 27 , 1970', 'charles j carney ( d )', 'november 3 , 1970'], ['pennsylvania 9th', 'george watkins ( r )', 'died august 7 , 1970', 'john h ware iii ( r )', 'november 3 , 1970'], ['illinois 1st', 'william l dawson ( d )', 'died november 9 , 1970', 'vacant', 'not filled this term']]
|
new york city mayoral elections
|
https://en.wikipedia.org/wiki/New_York_City_mayoral_elections
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1108394-32.html.csv
|
comparative
|
robert f wagner , jr received more votes than arthur levitt in the 1961 democratic primary election .
|
{'row_1': '1', 'row_2': '3', 'col': '7', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1961 democratic primary', 'robert f wagner , jr'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1961 democratic primary record fuzzily matches to robert f wagner , jr .', 'tostr': 'filter_eq { all_rows ; 1961 democratic primary ; robert f wagner , jr }'}, 'total'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 1961 democratic primary ; robert f wagner , jr } ; total }', 'tointer': 'select the rows whose 1961 democratic primary record fuzzily matches to robert f wagner , jr . take the total record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1961 democratic primary', 'arthur levitt'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose 1961 democratic primary record fuzzily matches to arthur levitt .', 'tostr': 'filter_eq { all_rows ; 1961 democratic primary ; arthur levitt }'}, 'total'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; 1961 democratic primary ; arthur levitt } ; total }', 'tointer': 'select the rows whose 1961 democratic primary record fuzzily matches to arthur levitt . take the total record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; 1961 democratic primary ; robert f wagner , jr } ; total } ; hop { filter_eq { all_rows ; 1961 democratic primary ; arthur levitt } ; total } } = true', 'tointer': 'select the rows whose 1961 democratic primary record fuzzily matches to robert f wagner , jr . take the total record of this row . select the rows whose 1961 democratic primary record fuzzily matches to arthur levitt . take the total record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; 1961 democratic primary ; robert f wagner , jr } ; total } ; hop { filter_eq { all_rows ; 1961 democratic primary ; arthur levitt } ; total } } = true
|
select the rows whose 1961 democratic primary record fuzzily matches to robert f wagner , jr . take the total record of this row . select the rows whose 1961 democratic primary record fuzzily matches to arthur levitt . take the total record of this row . the first record is greater than the second record .
|
5
|
5
|
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, '1961 democratic primary_7': 7, 'robert f wagner , jr_8': 8, 'total_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, '1961 democratic primary_11': 11, 'arthur levitt_12': 12, 'total_13': 13}
|
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', '1961 democratic primary_7': '1961 democratic primary', 'robert f wagner , jr_8': 'robert f wagner , jr', 'total_9': 'total', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', '1961 democratic primary_11': '1961 democratic primary', 'arthur levitt_12': 'arthur levitt', 'total_13': 'total'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], '1961 democratic primary_7': [0], 'robert f wagner , jr_8': [0], 'total_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], '1961 democratic primary_11': [1], 'arthur levitt_12': [1], 'total_13': [3]}
|
['1961 democratic primary', 'manhattan', 'the bronx', 'brooklyn', 'queens', 'richmond', 'total']
|
[['robert f wagner , jr', '122607', '78626', '136440', '102845', '15498', '456016'], ['robert f wagner , jr', '65 %', '62 %', '57 %', '62 %', '60 %', '456016'], ['arthur levitt', '66917', '47885', '103296', '64157', '10471', '292726'], ['arthur levitt', '35 %', '38 %', '43 %', '38 %', '40 %', '292726'], ['subtotal ( for wagner and levitt only )', '189524', '126511', '239736', '167002', '25969', '748742']]
|
corey hill
|
https://en.wikipedia.org/wiki/Corey_Hill
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11034773-2.html.csv
|
aggregation
|
corey hill 's mma fights lasted a combined total of 19 rounds .
|
{'scope': 'all', 'col': '6', 'type': 'sum', 'result': '19', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'round'], 'result': '19', 'ind': 0, 'tostr': 'sum { all_rows ; round }'}, '19'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; round } ; 19 } = true', 'tointer': 'the sum of the round record of all rows is 19 .'}
|
round_eq { sum { all_rows ; round } ; 19 } = true
|
the sum of the round record of all rows is 19 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'round_4': 4, '19_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'round_4': 'round', '19_5': '19'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'round_4': [0], '19_5': [1]}
|
['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location']
|
[['loss', '6 - 5', 'ryan thomas', 'submission ( armbar )', 'xfc 21 : night of champions 2', '1', '2:34', 'nashville , tennessee , united states'], ['win', '6 - 4', 'darryl madison', 'submission ( anaconda choke )', 'complete devastation 5', '1', '1:11', 'altoona , pennsylvania , united states'], ['win', '5 - 4', 'charlie rader', 'submission ( brabo choke )', 'xfc 15 : tribute', '1', '3:58', 'tampa , florida , united states'], ['loss', '4 - 4', 'rob mccullough', 'decision ( unanimous )', 'tachi palace fights 6', '3', '5:00', 'lemoore , california , united states'], ['win', '4 - 3', 'kit cope', 'submission ( triangle choke )', 'raging wolf 8 : cage supremacy', '1', '2:30', 'salamanca , new york , united states'], ['loss', '3 - 3', 'mark holst', 'submission ( kimura )', 'xkl : evolution 1', '2', '4:06', 'ypsilanti , michigan , united states'], ['win', '3 - 2', 'jason trzewieczynski', 'decision ( unanimous )', 'raging wolf 6 : mayhem in the mist', '3', '5:00', 'niagara , new york , united states'], ['loss', '2 - 2', 'dale hartt', 'tko ( broken leg )', 'ufc : fight for the troops', '2', '0:20', 'fayetteville , north carolina , united states'], ['loss', '2 - 1', 'justin buchholz', 'submission ( rear naked choke )', 'ufc 86', '2', '3:57', 'las vegas , nevada , united states'], ['win', '2 - 0', 'joe veres', 'tko ( punches )', 'ufc fight night 12', '2', '0:37', 'las vegas , nevada , united states'], ['win', '1 - 0', 'stryder fann', 'tko ( punches )', 'kickdown classic 31', '1', '0:34', 'casper , wyoming , united states']]
|
indiana high school athletics conferences : mid - eastern - northwestern
|
https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Mid-Eastern_%E2%80%93_Northwestern
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18942405-2.html.csv
|
ordinal
|
the triton central has the 2nd highest enrollment among schools in the indiana high school athletics conference .
|
{'row': '8', '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', 'enrollment', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; enrollment ; 2 }'}, 'school'], 'result': 'triton central', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; enrollment ; 2 } ; school }'}, 'triton central'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; triton central } = true', 'tointer': 'select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is triton central .'}
|
eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; triton central } = true
|
select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is triton central .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, '2_6': 6, 'school_7': 7, 'triton central_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', 'enrollment_5': 'enrollment', '2_6': '2', 'school_7': 'school', 'triton central_8': 'triton central'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], '2_6': [0], 'school_7': [1], 'triton central_8': [2]}
|
['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'county']
|
[['edinburgh community', 'edinburgh', 'lancers', '269', 'a', '41 johnson'], ['hauser', 'hope', 'jets', '297', 'a', '03 bartholomew'], ['indian creek', 'trafalgar', 'braves', '608', 'aaa', '41 johnson'], ['morristown', 'morristown', 'yellow jackets', '231', 'a', '73 shelby'], ['north decatur', 'greensburg', 'chargers', '369', 'aa', '16 decatur'], ['south decatur', 'greensburg', 'cougars', '292', 'a', '16 decatur'], ['southwestern shelbyville', 'shelbyville', 'spartans', '218', 'a', '73 shelby'], ['triton central', 'fairland', 'tigers', '525', 'aa', '73 shelby'], ['waldron', 'waldron', 'mohawks', '237', 'a', '73 shelby']]
|
dorado group
|
https://en.wikipedia.org/wiki/Dorado_Group
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18156552-1.html.csv
|
superlative
|
ngc 1596 is the galaxy in the dorado group that has the highest redshift in km/s .
|
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '18', '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', 'redshift ( km / s )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; redshift ( km / s ) }'}, 'name'], 'result': 'ngc 1596', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; redshift ( km / s ) } ; name }'}, 'ngc 1596'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; redshift ( km / s ) } ; name } ; ngc 1596 } = true', 'tointer': 'select the row whose redshift ( km / s ) record of all rows is maximum . the name record of this row is ngc 1596 .'}
|
eq { hop { argmax { all_rows ; redshift ( km / s ) } ; name } ; ngc 1596 } = true
|
select the row whose redshift ( km / s ) record of all rows is maximum . the name record of this row is ngc 1596 .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'redshift (km / s )_5': 5, 'name_6': 6, 'ngc 1596_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'redshift (km / s )_5': 'redshift ( km / s )', 'name_6': 'name', 'ngc 1596_7': 'ngc 1596'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'redshift (km / s )_5': [0], 'name_6': [1], 'ngc 1596_7': [2]}
|
['name', 'type', 'ra ( j2000 )', 'dec ( j2000 )', 'redshift ( km / s )', 'apparent magnitude']
|
[['ngc 2082', 'sab ( rs + ) c', '05h41 m51 .1 s', 'degree18 ′ 04 ″', '1184 ± 6', '12.6'], ['ngc 1947', 's0 - pec', '05h26 m47 .6 s', 'degree45 ′ 36 ″', '1100 ± 24', '11.7'], ['ngc 1796', '( r ) sb ( r ) dm :', '05h02 m42 .5 s', 'degree08 ′ 24 ″', '1014 ± 9', '12.9'], ['ngc 1688', 'sb ( rs ) dm', '04h48 m23 .8 s', 'degree48 ′ 01 ″', '1228 ± 6', '12.6'], ['ngc 1672', "( r ' _ 1 : ) sb ( r ) bc sy2", '04h45 m42 .5 s', 'degree14 ′ 50 ″', '1331 ± 3', '10.3'], ['ic 2056', 'sab ( r ) b', '04h16 m24 .5 s', 'degree12 ′ 25 ″', '1133 ± 10', '12.5'], ['ngc 1559', 'sb ( s ) cd', '04h17 m35 .8 s', 'degree47 ′ 01 ″', '1304 ± 4', '11.0'], ['ngc 1543', '( r ) sb ( l ) 0 0', '04h12 m43 .2 s', 'degree44 ′ 17 ″', '1176 ± 7', '11.5'], ['ngc 1574', 'sa0 -', '04h21 m58 .8 s', 'degree58 ′ 29 ″', '1050 ± 25', '11.4'], ['ngc 1533', '( l ) sb ( rs ) 0 0', '04h09 m51 .8 s', 'degree07 ′ 06 ″', '790 ± 5', '11.7'], ['ngc 1546', 'sa0 +', '04h14 m36 .5 s', 'degree03 ′ 39 ″', '1284 ± 14', '11.8'], ['ngc 1553', 'sa ( rl ) 0 0', '04h16 m10 .5 s', 'degree46 ′ 49 ″', '1080 ± 11', '10.3'], ['ngc 1549', 'e0 1', '04h15 m45 .1 s', 'degree35 ′ 32 ″', '1220 ± 15', '10.7'], ['ngc 1566', "( r ' _ 1 ) sab ( rs ) bcsy1", '04h20 m00 .4 s', 'degree56 ′ 16 ″', '1504 ± 2', '10.3'], ['ngc 1617', "( r ' ) sab ( rs ) a", '04h31 m39 .5 s', 'degree36 ′ 08 ″', '1063 ± 21', '11.4'], ['ngc 1515', 'sab ( s ) bc', '04h04 m02 .7 s', 'degree06 ′ 00 ″', '1175 ± 7', '12.1'], ['ngc 1705', 'sa0 - pec', '04h54 m13 .5 s', 'degree21 ′ 40 ″', '633 ± 6', '12.8'], ['ngc 1596', 'sa0 : sp', '04h27 m38 .1 s', 'degree01 ′ 40 ″', '1510 ± 8', '12.1']]
|
1963 vfl season
|
https://en.wikipedia.org/wiki/1963_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10783853-9.html.csv
|
majority
|
in the 1963 vfl season , when the home team had over 5 points , most of the crowds were over 20000 people .
|
{'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '20000', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': '5'}}
|
{'func': 'most_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'home team score', '5'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; home team score ; 5 }', 'tointer': 'select the rows whose home team score record is greater than 5 .'}, 'crowd', '20000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose home team score record is greater than 5 . for the crowd records of these rows , most of them are greater than 20000 .', 'tostr': 'most_greater { filter_greater { all_rows ; home team score ; 5 } ; crowd ; 20000 } = true'}
|
most_greater { filter_greater { all_rows ; home team score ; 5 } ; crowd ; 20000 } = true
|
select the rows whose home team score record is greater than 5 . for the crowd records of these rows , most of them are greater than 20000 .
|
2
|
2
|
{'most_greater_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'home team score_4': 4, '5_5': 5, 'crowd_6': 6, '20000_7': 7}
|
{'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'home team score_4': 'home team score', '5_5': '5', 'crowd_6': 'crowd', '20000_7': '20000'}
|
{'most_greater_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'home team score_4': [0], '5_5': [0], 'crowd_6': [1], '20000_7': [1]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['melbourne', '18.6 ( 114 )', 'north melbourne', '9.10 ( 64 )', 'mcg', '23971', '22 june 1963'], ['geelong', '16.13 ( 109 )', 'richmond', '10.11 ( 71 )', 'kardinia park', '20681', '22 june 1963'], ['essendon', '4.16 ( 40 )', 'st kilda', '8.8 ( 56 )', 'windy hill', '24725', '22 june 1963'], ['collingwood', '11.6 ( 72 )', 'footscray', '6.4 ( 40 )', 'victoria park', '26173', '22 june 1963'], ['south melbourne', '8.10 ( 58 )', 'fitzroy', '5.9 ( 39 )', 'lake oval', '12850', '22 june 1963'], ['hawthorn', '9.6 ( 60 )', 'carlton', '7.12 ( 54 )', 'glenferrie oval', '25300', '22 june 1963']]
|
united states house of representatives elections , 2006
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2006
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1805191-4.html.csv
|
superlative
|
jim kolbe is the earliest arizona incumbent in the united states house of representatives election of 2006 to be first elected .
|
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '8', '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', 'first elected'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; first elected }'}, 'incumbent'], 'result': 'jim kolbe', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; first elected } ; incumbent }'}, 'jim kolbe'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; first elected } ; incumbent } ; jim kolbe } = true', 'tointer': 'select the row whose first elected record of all rows is minimum . the incumbent record of this row is jim kolbe .'}
|
eq { hop { argmin { all_rows ; first elected } ; incumbent } ; jim kolbe } = true
|
select the row whose first elected record of all rows is minimum . the incumbent record of this row is jim kolbe .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'first elected_5': 5, 'incumbent_6': 6, 'jim kolbe_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', 'incumbent_6': 'incumbent', 'jim kolbe_7': 'jim kolbe'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], 'incumbent_6': [1], 'jim kolbe_7': [2]}
|
['district', 'incumbent', 'party', 'first elected', 'results']
|
[['arizona 1', 'rick renzi', 'republican', '2002', 're - elected'], ['arizona 2', 'trent franks', 'republican', '2002', 're - elected'], ['arizona 3', 'john shadegg', 'republican', '1994', 're - elected'], ['arizona 4', 'ed pastor', 'democratic', '1990', 're - elected'], ['arizona 5', 'j d hayworth', 'republican', '1994', 'lost re - election democratic gain'], ['arizona 6', 'jeff flake', 'republican', '2000', 're - elected'], ['arizona 7', 'raul grijalva', 'democratic', '2002', 're - elected'], ['arizona 8', 'jim kolbe', 'republican', '1984', 'retired democratic gain']]
|
don branson
|
https://en.wikipedia.org/wiki/Don_Branson
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235983-1.html.csv
|
aggregation
|
for don branson the total number of laps completed was 1098 .
|
{'scope': 'all', 'col': '6', 'type': 'sum', 'result': '1098', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'laps'], 'result': '1098', 'ind': 0, 'tostr': 'sum { all_rows ; laps }'}, '1098'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; laps } ; 1098 } = true', 'tointer': 'the sum of the laps record of all rows is 1098 .'}
|
round_eq { sum { all_rows ; laps } ; 1098 } = true
|
the sum of the laps record of all rows is 1098 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'laps_4': 4, '1098_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'laps_4': 'laps', '1098_5': '1098'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'laps_4': [0], '1098_5': [1]}
|
['year', 'start', 'qual', 'rank', 'finish', 'laps']
|
[['1959', '10', '143.312', '12', '24', '112'], ['1960', '8', '144.753', '11', '4', '200'], ['1961', '2', '146.843', '3', '33', '2'], ['1962', '11', '147.312', '11', '12', '200'], ['1963', '3', '150.188', '4', '5', '200'], ['1964', '9', '152.672', '12', '12', '187'], ['1965', '18', '155.501', '16', '8', '197'], ['1966', '9', '160.385', '12', '23', '0']]
|
adelaide united fc
|
https://en.wikipedia.org/wiki/Adelaide_United_FC
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1257184-2.html.csv
|
unique
|
dario vidošić was the only player to score a goal for adelaide united fc from 2007 to 2013 .
|
{'scope': 'all', 'row': '3', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '( 1 )', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'goals', '( 1 )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals record fuzzily matches to ( 1 ) .', 'tostr': 'filter_eq { all_rows ; goals ; ( 1 ) }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; goals ; ( 1 ) } }', 'tointer': 'select the rows whose goals record fuzzily matches to ( 1 ) . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'goals', '( 1 )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals record fuzzily matches to ( 1 ) .', 'tostr': 'filter_eq { all_rows ; goals ; ( 1 ) }'}, 'player'], 'result': 'dario vidošić', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; goals ; ( 1 ) } ; player }'}, 'dario vidošić'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; goals ; ( 1 ) } ; player } ; dario vidošić }', 'tointer': 'the player record of this unqiue row is dario vidošić .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; goals ; ( 1 ) } } ; eq { hop { filter_eq { all_rows ; goals ; ( 1 ) } ; player } ; dario vidošić } } = true', 'tointer': 'select the rows whose goals record fuzzily matches to ( 1 ) . there is only one such row in the table . the player record of this unqiue row is dario vidošić .'}
|
and { only { filter_eq { all_rows ; goals ; ( 1 ) } } ; eq { hop { filter_eq { all_rows ; goals ; ( 1 ) } ; player } ; dario vidošić } } = true
|
select the rows whose goals record fuzzily matches to ( 1 ) . there is only one such row in the table . the player record of this unqiue row is dario vidošić .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'goals_7': 7, '(1)_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'dario vidošić_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'goals_7': 'goals', '(1)_8': '( 1 )', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'dario vidošić_10': 'dario vidošić'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'goals_7': [0], '(1)_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'dario vidošić_10': [3]}
|
['player', 'country', 'caps', 'goals', 'years active', 'years at club']
|
[['eugene galeković', 'australia', '8', '( 0 )', '2009 -', '2007 -'], ['jonathan mckain', 'australia', '16', '( 0 )', '2004 -', '2011 -'], ['dario vidošić', 'australia', '18', '( 1 )', '2009 -', '2011 - 2013'], ['bruce djite', 'australia', '9', '( 0 )', '2008 -', '2006 - 2008 , 2011 -'], ['fabian barbiero', 'australia', '1', '( 0 )', '2009', '2007 - 2013']]
|
kristy mcpherson
|
https://en.wikipedia.org/wiki/Kristy_McPherson
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14853156-2.html.csv
|
unique
|
2005 is the only year in which kristy mcpherson does n't have a recorded scoring rank .
|
{'scope': 'all', 'row': '1', 'col': '12', 'col_other': '1', 'criterion': 'equal', 'value': 'n/a', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'scoring rank', 'n/a'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose scoring rank record fuzzily matches to n/a .', 'tostr': 'filter_eq { all_rows ; scoring rank ; n/a }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; scoring rank ; n/a } }', 'tointer': 'select the rows whose scoring rank record fuzzily matches to n/a . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'scoring rank', 'n/a'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose scoring rank record fuzzily matches to n/a .', 'tostr': 'filter_eq { all_rows ; scoring rank ; n/a }'}, 'year'], 'result': '2005', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; scoring rank ; n/a } ; year }'}, '2005'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; scoring rank ; n/a } ; year } ; 2005 }', 'tointer': 'the year record of this unqiue row is 2005 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; scoring rank ; n/a } } ; eq { hop { filter_eq { all_rows ; scoring rank ; n/a } ; year } ; 2005 } } = true', 'tointer': 'select the rows whose scoring rank record fuzzily matches to n/a . there is only one such row in the table . the year record of this unqiue row is 2005 .'}
|
and { only { filter_eq { all_rows ; scoring rank ; n/a } } ; eq { hop { filter_eq { all_rows ; scoring rank ; n/a } ; year } ; 2005 } } = true
|
select the rows whose scoring rank record fuzzily matches to n/a . there is only one such row in the table . the year record of this unqiue row is 2005 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'scoring rank_7': 7, 'n/a_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '2005_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'scoring rank_7': 'scoring rank', 'n/a_8': 'n/a', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '2005_10': '2005'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'scoring rank_7': [0], 'n/a_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '2005_10': [3]}
|
['year', 'tournaments played', 'cuts made', 'wins', '2nd', '3rd', 'top 10s', 'best finish', 'earnings', 'money list rank', 'scoring average', 'scoring rank']
|
[['2005', '1', '0', '0', '0', '0', '0', 'mc', '0', 'n / a', '77.00', 'n / a'], ['2007', '18', '11', '0', '0', '0', '0', 't18', '79724', '97', '73.73', 't99'], ['2008', '26', '19', '0', '0', '0', '6', 't4', '407237', '47', '71.86', '34'], ['2009', '24', '21', '0', '2', '1', '6', 't2', '816182', '16', '71.25', '17'], ['2010', '22', '17', '0', '1', '0', '4', 't2', '418217', '27', '72.26', '40'], ['2011', '21', '17', '0', '0', '0', '0', 't18', '157025', '56', '72.65', '50']]
|
2008 national league 1
|
https://en.wikipedia.org/wiki/2008_National_League_1
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19179465-1.html.csv
|
superlative
|
the salford city reds club had the most points in the 2008 national league 1 season .
|
{'scope': 'all', 'col_superlative': '11', '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': 'salford city reds', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; club }'}, 'salford city reds'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; club } ; salford city reds } = true', 'tointer': 'select the row whose points record of all rows is maximum . the club record of this row is salford city reds .'}
|
eq { hop { argmax { all_rows ; points } ; club } ; salford city reds } = true
|
select the row whose points record of all rows is maximum . the club record of this row is salford city reds .
|
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, 'salford city reds_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', 'salford city reds_7': 'salford city reds'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'club_6': [1], 'salford city reds_7': [2]}
|
['position', 'club', 'played', 'won', 'drawn', 'lost', 'pts for', 'pts agst', 'pts diff', 'bp', 'points']
|
[['1', 'salford city reds', '18', '12', '3', '3', '614', '302', '312', '3', '45'], ['2', 'celtic crusaders', '18', '12', '0', '6', '511', '391', '120', '4', '40'], ['3', 'halifax', '18', '11', '1', '6', '634', '514', '120', '3', '38'], ['4', 'leigh centurions', '18', '10', '0', '8', '448', '448', '0', '4', '34'], ['5', 'whitehaven', '18', '10', '0', '8', '420', '399', '21', '2', '32'], ['6', 'widnes vikings', '18', '10', '2', '6', '453', '410', '43', '5', '30'], ['7', 'sheffield eagles', '18', '8', '1', '9', '425', '530', '- 105', '3', '29'], ['8', 'featherstone rovers', '18', '6', '1', '11', '452', '515', '- 63', '6', '26'], ['9', 'batley bulldogs', '18', '5', '0', '13', '387', '538', '- 151', '8', '23']]
|
utah jazz all - time roster
|
https://en.wikipedia.org/wiki/Utah_Jazz_all-time_roster
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11545282-4.html.csv
|
majority
|
all players in the utah jazz all - time roster are from the united states .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'united states', 'subset': None}
|
{'func': 'all_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , all of them fuzzily match to united states .', 'tostr': 'all_eq { all_rows ; nationality ; united states } = true'}
|
all_eq { all_rows ; nationality ; united states } = true
|
for the nationality 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, 'nationality_3': 3, 'united states_4': 4}
|
{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'united states_4': 'united states'}
|
{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'united states_4': [0]}
|
['player', 'nationality', 'position', 'years for jazz', 'school / club team']
|
[['adrian dantley', 'united states', 'guard - forward', '1979 - 86', 'notre dame'], ['brad davis', 'united states', 'guard', '1979 - 80', 'maryland'], ['darryl dawkins', 'united states', 'center', '1987 - 88', 'maynard evans hs'], ['paul dawkins', 'united states', 'guard', '1979 - 80', 'northern illinois'], ['greg deane', 'united states', 'guard', '1979 - 80', 'utah'], ['james donaldson', 'united states', 'center', '1993 , 1994 - 95', 'washington state'], ['john drew', 'united states', 'guard - forward', '1982 - 85', 'gardner - webb'], ['john duren', 'united states', 'guard', '1980 - 82', 'georgetown']]
|
1965 american football league draft
|
https://en.wikipedia.org/wiki/1965_American_Football_League_Draft
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18652198-7.html.csv
|
comparative
|
in the 1965 american football league draft , jack snow was picked one spot after lou bobich .
|
{'row_1': '6', 'row_2': '5', 'col': '1', 'col_other': '3', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '1', 'bigger': 'row1'}}
|
{'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'jack snow'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to jack snow .', 'tostr': 'filter_eq { all_rows ; player ; jack snow }'}, 'pick'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; jack snow } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to jack snow . take the pick record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'lou bobich'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to lou bobich .', 'tostr': 'filter_eq { all_rows ; player ; lou bobich }'}, 'pick'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; lou bobich } ; pick }', 'tointer': 'select the rows whose player record fuzzily matches to lou bobich . take the pick record of this row .'}], 'result': '1', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; player ; jack snow } ; pick } ; hop { filter_eq { all_rows ; player ; lou bobich } ; pick } }'}, '1'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; player ; jack snow } ; pick } ; hop { filter_eq { all_rows ; player ; lou bobich } ; pick } } ; 1 } = true', 'tointer': 'select the rows whose player record fuzzily matches to jack snow . take the pick record of this row . select the rows whose player record fuzzily matches to lou bobich . take the pick record of this row . the first record is 1 larger than the second record .'}
|
eq { diff { hop { filter_eq { all_rows ; player ; jack snow } ; pick } ; hop { filter_eq { all_rows ; player ; lou bobich } ; pick } } ; 1 } = true
|
select the rows whose player record fuzzily matches to jack snow . take the pick record of this row . select the rows whose player record fuzzily matches to lou bobich . take the pick 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, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'player_8': 8, 'jack snow_9': 9, 'pick_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'player_12': 12, 'lou bobich_13': 13, 'pick_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', 'player_8': 'player', 'jack snow_9': 'jack snow', 'pick_10': 'pick', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'player_12': 'player', 'lou bobich_13': 'lou bobich', 'pick_14': 'pick', '1_15': '1'}
|
{'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'player_8': [0], 'jack snow_9': [0], 'pick_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'player_12': [1], 'lou bobich_13': [1], 'pick_14': [3], '1_15': [5]}
|
['pick', 'team', 'player', 'position', 'college']
|
[['49', 'denver broncos', 'jim garcia', 'defensive end', 'purdue'], ['50', 'kansas city chiefs ( from houston oilers )', 'gloster richardson', 'wide receiver', 'jackson state'], ['51', 'new york jets ( from oakland raiders )', 'archie roberts', 'quarterback', 'columbia'], ['52', 'new york jets', 'jim harris , jr', 'defensive tackle', 'utah state'], ['53', 'kansas city chiefs', 'lou bobich', 'defensive back', 'michigan state'], ['54', 'san diego chargers', 'jack snow', 'wide receiver', 'notre dame'], ['55', 'boston patriots', 'tom neville', 'defensive tackle', 'mississippi state'], ['56', 'buffalo bills', 'marty schottenheimer', 'linebacker', 'pittsburgh']]
|
indianapolis colts draft history
|
https://en.wikipedia.org/wiki/Indianapolis_Colts_draft_history
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13312898-47.html.csv
|
unique
|
paul miranda is the only player the indianapolis colts drafted from central florida college .
|
{'scope': 'all', 'row': '4', 'col': '6', 'col_other': '4', 'criterion': 'equal', 'value': 'central florida', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'central florida'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to central florida .', 'tostr': 'filter_eq { all_rows ; college ; central florida }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; central florida } }', 'tointer': 'select the rows whose college record fuzzily matches to central florida . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'central florida'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to central florida .', 'tostr': 'filter_eq { all_rows ; college ; central florida }'}, 'name'], 'result': 'paul miranda', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; central florida } ; name }'}, 'paul miranda'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; central florida } ; name } ; paul miranda }', 'tointer': 'the name record of this unqiue row is paul miranda .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; central florida } } ; eq { hop { filter_eq { all_rows ; college ; central florida } ; name } ; paul miranda } } = true', 'tointer': 'select the rows whose college record fuzzily matches to central florida . there is only one such row in the table . the name record of this unqiue row is paul miranda .'}
|
and { only { filter_eq { all_rows ; college ; central florida } } ; eq { hop { filter_eq { all_rows ; college ; central florida } ; name } ; paul miranda } } = true
|
select the rows whose college record fuzzily matches to central florida . there is only one such row in the table . the name record of this unqiue row is paul miranda .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'central florida_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'paul miranda_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'central florida_8': 'central florida', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'paul miranda_10': 'paul miranda'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'central florida_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'paul miranda_10': [3]}
|
['round', 'pick', 'overall', 'name', 'position', 'college']
|
[['1', '4', '4', 'edgerrin james', 'running back', 'miami ( fl )'], ['2', '5', '36', 'mike peterson', 'linebacker', 'florida'], ['3', '2', '63', 'brandon burlsworth', 'guard', 'arkansas'], ['4', '1', '96', 'paul miranda', 'cornerback', 'central florida'], ['5', '5', '138', 'brad scioli', 'defensive end', 'penn state'], ['7', '4', '210', 'hunter smith', 'punter', 'notre dame'], ['7', '44', '250', 'corey terry', 'linebacker', 'tennessee']]
|
1993 minnesota vikings season
|
https://en.wikipedia.org/wiki/1993_Minnesota_Vikings_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10362162-2.html.csv
|
ordinal
|
the minnesota vikings ' game against the los angeles raiders was the earliest in the 1993 season .
|
{'row': '1', 'col': '1', 'order': '1', 'col_other': '3', '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', 'week', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; week ; 1 }'}, 'opponent'], 'result': 'los angeles raiders', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; week ; 1 } ; opponent }'}, 'los angeles raiders'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; week ; 1 } ; opponent } ; los angeles raiders } = true', 'tointer': 'select the row whose week record of all rows is 1st minimum . the opponent record of this row is los angeles raiders .'}
|
eq { hop { nth_argmin { all_rows ; week ; 1 } ; opponent } ; los angeles raiders } = true
|
select the row whose week record of all rows is 1st minimum . the opponent record of this row is los angeles raiders .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'week_5': 5, '1_6': 6, 'opponent_7': 7, 'los angeles raiders_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', 'week_5': 'week', '1_6': '1', 'opponent_7': 'opponent', 'los angeles raiders_8': 'los angeles raiders'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'week_5': [0], '1_6': [0], 'opponent_7': [1], 'los angeles raiders_8': [2]}
|
['week', 'date', 'opponent', 'result', 'attendance']
|
[['1', 'september 5 , 1993', 'los angeles raiders', 'l 24 - 7', '44120'], ['2', 'september 12 , 1993', 'chicago bears', 'w 10 - 7', '57921'], ['4', 'september 26 , 1993', 'green bay packers', 'w 15 - 13', '61746'], ['5', 'october 3 , 1993', 'san francisco 49ers', 'l 38 - 19', '63071'], ['6', 'october 10 , 1993', 'tampa bay buccaneers', 'w 15 - 0', '54215'], ['8', 'october 25 , 1993', 'chicago bears', 'w 19 - 12', '64677'], ['9', 'october 31 , 1993', 'detroit lions', 'l 30 - 27', '53428'], ['10', 'november 7 , 1993', 'san diego chargers', 'l 30 - 17', '55527'], ['11', 'november 14 , 1993', 'denver broncos', 'w 26 - 23', '67329'], ['12', 'november 21 , 1993', 'tampa bay buccaneers', 'l 23 - 10', '40848'], ['13', 'november 28 , 1993', 'new orleans saints', 'l 17 - 14', '53030'], ['14', 'december 5 , 1993', 'detroit lions', 'w 13 - 0', '63216'], ['15', 'december 12 , 1993', 'dallas cowboys', 'l 37 - 20', '63321'], ['16', 'december 19 , 1993', 'green bay packers ( milw )', 'w 21 - 17', '54773'], ['17', 'december 26 , 1993', 'kansas city chiefs', 'w 30 - 10', '59236'], ['18', 'december 31 , 1993', 'washington redskins', 'w 14 - 9', '42836']]
|
jet engine
|
https://en.wikipedia.org/wiki/Jet_engine
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15944-5.html.csv
|
unique
|
the nk - 33 rocket engine was the only one with over 10 sfc in lb / ( lbf h ) .
|
{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'greater_than', 'value': '10', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'sfc in lb / ( lbf h )', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sfc in lb / ( lbf h ) record is greater than 10 .', 'tostr': 'filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } }', 'tointer': 'select the rows whose sfc in lb / ( lbf h ) record is greater than 10 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'sfc in lb / ( lbf h )', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sfc in lb / ( lbf h ) record is greater than 10 .', 'tostr': 'filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 }'}, 'engine type'], 'result': 'nk - 33 rocket engine', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } ; engine type }'}, 'nk - 33 rocket engine'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } ; engine type } ; nk - 33 rocket engine }', 'tointer': 'the engine type record of this unqiue row is nk - 33 rocket engine .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } } ; eq { hop { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } ; engine type } ; nk - 33 rocket engine } } = true', 'tointer': 'select the rows whose sfc in lb / ( lbf h ) record is greater than 10 . there is only one such row in the table . the engine type record of this unqiue row is nk - 33 rocket engine .'}
|
and { only { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } } ; eq { hop { filter_greater { all_rows ; sfc in lb / ( lbf h ) ; 10 } ; engine type } ; nk - 33 rocket engine } } = true
|
select the rows whose sfc in lb / ( lbf h ) record is greater than 10 . there is only one such row in the table . the engine type record of this unqiue row is nk - 33 rocket engine .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'sfc in lb / (lbf h)_7': 7, '10_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'engine type_9': 9, 'nk - 33 rocket engine_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'sfc in lb / (lbf h)_7': 'sfc in lb / ( lbf h )', '10_8': '10', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'engine type_9': 'engine type', 'nk - 33 rocket engine_10': 'nk - 33 rocket engine'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'sfc in lb / (lbf h)_7': [0], '10_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'engine type_9': [2], 'nk - 33 rocket engine_10': [3]}
|
['engine type', 'scenario', 'sfc in lb / ( lbf h )', 'sfc in g / ( kn s )', 'specific impulse ( s )', 'effective exhaust velocity ( m / s )']
|
[['nk - 33 rocket engine', 'vacuum', '10.9', '309', '331', '3240'], ['ssme rocket engine', 'space shuttle vacuum', '7.95', '225', '453', '4423'], ['ramjet', 'mach 1', '4.5', '127', '800', '7877'], ['j - 58 turbojet', 'sr - 71 at mach 3.2 ( wet )', '1.9', '53.8', '1900', '18587'], ['rolls - royce / snecma olympus 593', 'concorde mach 2 cruise ( dry )', '1.195', '33.8', '3012', '29553'], ['cf6 - 80c2b1f turbofan', 'boeing 747 - 400 cruise', '0.605', '17.1', '5950', '58400'], ['general electric cf6 turbofan', 'sea level', '0.307', '8.696', '11700', '115000']]
|
1974 vfl season
|
https://en.wikipedia.org/wiki/1974_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10869646-9.html.csv
|
ordinal
|
north melbourne had the second lowest home team score of all these teams .
|
{'row': '4', 'col': '2', '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', 'home team score', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; home team score ; 2 }'}, 'home team'], 'result': 'north melbourne', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; home team score ; 2 } ; home team }'}, 'north melbourne'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; home team score ; 2 } ; home team } ; north melbourne } = true', 'tointer': 'select the row whose home team score record of all rows is 2nd minimum . the home team record of this row is north melbourne .'}
|
eq { hop { nth_argmin { all_rows ; home team score ; 2 } ; home team } ; north melbourne } = true
|
select the row whose home team score record of all rows is 2nd minimum . the home team record of this row is north melbourne .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'home team score_5': 5, '2_6': 6, 'home team_7': 7, 'north melbourne_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', 'home team score_5': 'home team score', '2_6': '2', 'home team_7': 'home team', 'north melbourne_8': 'north melbourne'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'home team score_5': [0], '2_6': [0], 'home team_7': [1], 'north melbourne_8': [2]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['hawthorn', '18.15 ( 123 )', 'st kilda', '10.16 ( 76 )', 'princes park', '12630', '1 june 1974'], ['geelong', '16.12 ( 108 )', 'south melbourne', '17.7 ( 109 )', 'kardinia park', '15664', '1 june 1974'], ['footscray', '13.16 ( 94 )', 'melbourne', '8.8 ( 56 )', 'western oval', '15415', '1 june 1974'], ['north melbourne', '11.15 ( 81 )', 'essendon', '16.15 ( 111 )', 'arden street oval', '20027', '1 june 1974'], ['richmond', '9.20 ( 74 )', 'collingwood', '21.17 ( 143 )', 'mcg', '66829', '1 june 1974'], ['carlton', '16.15 ( 111 )', 'fitzroy', '7.10 ( 52 )', 'vfl park', '19906', '1 june 1974']]
|
million dollar password
|
https://en.wikipedia.org/wiki/Million_Dollar_Password
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13673176-3.html.csv
|
count
|
of the episodes of the million dollar password game show listed , three had more than 9 million viewers .
|
{'scope': 'all', 'criterion': 'greater_than', 'value': '9 million', 'result': '3', 'col': '6', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'viewers ( millions )', '9 million'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose viewers ( millions ) record is greater than 9 million .', 'tostr': 'filter_greater { all_rows ; viewers ( millions ) ; 9 million }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; viewers ( millions ) ; 9 million } }', 'tointer': 'select the rows whose viewers ( millions ) record is greater than 9 million . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; viewers ( millions ) ; 9 million } } ; 3 } = true', 'tointer': 'select the rows whose viewers ( millions ) record is greater than 9 million . the number of such rows is 3 .'}
|
eq { count { filter_greater { all_rows ; viewers ( millions ) ; 9 million } } ; 3 } = true
|
select the rows whose viewers ( millions ) record is greater than 9 million . the number of such rows is 3 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'viewers (millions)_5': 5, '9 million_6': 6, '3_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'viewers (millions)_5': 'viewers ( millions )', '9 million_6': '9 million', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'viewers (millions)_5': [0], '9 million_6': [0], '3_7': [2]}
|
['airdate', 'celebrities', 'rating', 'share', '1849', 'viewers ( millions )', 'weekly rank', 'prod code']
|
[['sunday , june 1 , 2008', 'neil patrick harris , rachael ray', '6.8', '12', '2.2 / 7', '10.69', '3', '106'], ['sunday , june 8 , 2008', "tony hawk , rosie o'donnell", '6.3', '11', '2.1 / 6', '9.64', '5', '104'], ['thursday , june 12 , 2008', 'susie essman , betty white', '6.4', '12', '2.0 / 7', '9.52', '7', '102'], ['sunday , june 22 , 2008', 'shanna moakler , steven weber', '5.5', '10', '1.5 / 5', '8.29', '12', '105'], ['sunday , june 29 , 2008', 'sara evans , steve schirripa', '5.6', '10', '1.7 / 5', '8.55', '7', '101'], ['sunday , july 6 , 2008', 'monique coleman , damien fahey', '5.0', '9', '1.3 / 5', '7.53', '3', '103']]
|
1967 detroit lions season
|
https://en.wikipedia.org/wiki/1967_Detroit_Lions_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18908350-1.html.csv
|
unique
|
mel farr was the only player in the position of running back .
|
{'scope': 'all', 'row': '1', 'col': '4', 'col_other': '3', 'criterion': 'equal', 'value': 'running back', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'running back'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to running back .', 'tostr': 'filter_eq { all_rows ; position ; running back }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; position ; running back } }', 'tointer': 'select the rows whose position record fuzzily matches to running back . 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', 'running back'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to running back .', 'tostr': 'filter_eq { all_rows ; position ; running back }'}, 'player'], 'result': 'mel farr', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; running back } ; player }'}, 'mel farr'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; mel farr }', 'tointer': 'the player record of this unqiue row is mel farr .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; position ; running back } } ; eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; mel farr } } = true', 'tointer': 'select the rows whose position record fuzzily matches to running back . there is only one such row in the table . the player record of this unqiue row is mel farr .'}
|
and { only { filter_eq { all_rows ; position ; running back } } ; eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; mel farr } } = true
|
select the rows whose position record fuzzily matches to running back . there is only one such row in the table . the player record of this unqiue row is mel farr .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'position_7': 7, 'running back_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'mel farr_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', 'running back_8': 'running back', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'mel farr_10': 'mel farr'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'position_7': [0], 'running back_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'mel farr_10': [3]}
|
['round', 'pick', 'player', 'position', 'school']
|
[['1', '7', 'mel farr', 'running back', 'ucla'], ['2', '34', 'lem barney', 'defensive back', 'jackson state'], ['3', '60', 'paul naumoff', 'linebacker', 'tennessee'], ['4', '88', 'lew kamanu', 'defensive end', 'weber state'], ['6', '141', 'tim jones', 'quarterback', 'weber state']]
|
list of tallest buildings in indianapolis
|
https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Indianapolis
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14565330-3.html.csv
|
superlative
|
the building in indianapolis with the highest number of floors is bank one tower .
|
{'scope': 'all', 'col_superlative': '5', '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', 'floors'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; floors }'}, 'name'], 'result': 'bank one tower', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; floors } ; name }'}, 'bank one tower'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; floors } ; name } ; bank one tower } = true', 'tointer': 'select the row whose floors record of all rows is maximum . the name record of this row is bank one tower .'}
|
eq { hop { argmax { all_rows ; floors } ; name } ; bank one tower } = true
|
select the row whose floors record of all rows is maximum . the name record of this row is bank one tower .
|
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, 'bank one tower_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', 'bank one tower_7': 'bank one tower'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'floors_5': [0], 'name_6': [1], 'bank one tower_7': [2]}
|
['name', 'street address', 'years as tallest', 'height ft ( m )', 'floors']
|
[['indiana statehouse', '04.0 200 west washington street', '1888 - 1962', '255 ( 78 )', '4'], ['city - county building', '07.0 200 east washington street', '1962 - 1970', '372 ( 113 )', '28'], ['one indiana square', '01.0 1 indiana square', '1970 - 1982', '504 ( 154 )', '36'], ['aul tower', '07.0 200 north illinois street', '1982 - 1990', '533 ( 162 )', '38'], ['bank one tower', '05.0 111 monument circle', '1990 - present', '830 ( 253 )', '48']]
|
being human ( tv series )
|
https://en.wikipedia.org/wiki/Being_Human_%28TV_series%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15823956-1.html.csv
|
ordinal
|
series 1 of the being human tv show had the fourth highest amount of episodes .
|
{'row': '2', 'col': '2', 'order': '4', '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', 'episodes', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; episodes ; 4 }'}, 'series'], 'result': '1', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; episodes ; 4 } ; series }'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; episodes ; 4 } ; series } ; 1 } = true', 'tointer': 'select the row whose episodes record of all rows is 4th maximum . the series record of this row is 1 .'}
|
eq { hop { nth_argmax { all_rows ; episodes ; 4 } ; series } ; 1 } = true
|
select the row whose episodes record of all rows is 4th maximum . the series record of this row is 1 .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'episodes_5': 5, '4_6': 6, 'series_7': 7, '1_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', 'episodes_5': 'episodes', '4_6': '4', 'series_7': 'series', '1_8': '1'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'episodes_5': [0], '4_6': [0], 'series_7': [1], '1_8': [2]}
|
['series', 'episodes', 'series premiere', 'series finale', 'region 1', 'region 2', 'region 4']
|
[['pilot', '1', '18 february 2008', '18 february 2008', 'n / a', 'n / a', 'n / a'], ['1', '6', '25 january 2009', '1 march 2009', '20 july 2010', '20 april 2009', '6 august 2009'], ['2', '8', '10 january 2010', '28 february 2010', '21 september 2010', '12 april 2010', '5 august 2010'], ['3', '8', '23 january 2011', '13 march 2011', '3 may 2011', '28 march 2011', '5 may 2011'], ['4', '8', '5 february 2012', '25 march 2012', '15 january 2013', '23 april 2012', '7 june 2012']]
|
list of list a cricket records
|
https://en.wikipedia.org/wiki/List_of_List_A_cricket_records
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11303072-5.html.csv
|
aggregation
|
all of the batting duos in the list of cricket records scored a combined total of 2296 runs .
|
{'scope': 'all', 'col': '2', 'type': 'sum', 'result': '2296', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'runs'], 'result': '2296', 'ind': 0, 'tostr': 'sum { all_rows ; runs }'}, '2296'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; runs } ; 2296 } = true', 'tointer': 'the sum of the runs record of all rows is 2296 .'}
|
round_eq { sum { all_rows ; runs } ; 2296 } = true
|
the sum of the runs record of all rows is 2296 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'runs_4': 4, '2296_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'runs_4': 'runs', '2296_5': '2296'}
|
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'runs_4': [0], '2296_5': [1]}
|
['wicket', 'runs', 'batting partners', 'batting team', 'fielding team', 'venue', 'season']
|
[['1st', '326', 'ghulam ali and sohail jaffar', 'pakistan international airlines', 'agriculture development bank', 'jinnah stadium , sialkot', '2000 - 01'], ['2nd', '331', 'sachin tendulkar and rahul dravid', 'india', 'new zealand', 'lal bahadur shastri stadium , hyderabad', '1999 - 00'], ['3rd', '309', 'tim curtis and tom moody', 'worcestershire', 'surrey', 'the oval , london', '1994'], ['4th', '276', 'mominul haque and roshen silva', 'prime doleshwar', 'abahani limited', 'shaheed chandu stadium , bogra', '2013 - 14'], ['5th', '267', 'minhajul abedin and khaled mahmud', 'bangladesh', 'bahawalpur', 'united bank limited sports complex , karachi', '1997 - 98'], ['6th', '226', 'nigel llong and matthew fleming', 'kent', 'cheshire', 'south downs road , bowdon', '1999'], ['7th', '203', 'thilina kandamby and rangana herath', 'sri lanka a', 'south africa a', 'willowmoore park , benoni', '2008'], ['8th', '203', 'shahid iqbal and haaris ayaz', 'karachi whites', 'hyderabad', 'united bank limited sports complex , karachi', '1998 - 99'], ['9th', '155', 'chris read and andrew harris', 'nottinghamshire', 'durham', 'trent bridge , nottingham', '2006']]
|
united states house of representatives elections , 1810
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1810
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668374-10.html.csv
|
count
|
2 incumbents were re - elected in the 1810 united states house of representatives elections .
|
{'scope': 'all', 'criterion': 'equal', 'value': 're - elected', 'result': '2', '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': '2', '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 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; re - elected } } ; 2 } = true', 'tointer': 'select the rows whose result record fuzzily matches to re - elected . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; result ; re - elected } } ; 2 } = true
|
select the rows whose result record fuzzily matches to re - elected . 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, 'result_5': 5, 're - elected_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', 'result_5': 'result', 're - elected_6': 're - elected', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 're - elected_6': [0], '2_7': [2]}
|
['district', 'incumbent', 'party', 'first elected', 'result', 'candidates']
|
[['new york 1', 'samuel riker', 'democratic - republican', '1806', 'retired democratic - republican hold', 'ebenezer sage ( dr ) 93.5 % david gardiner ( f ) 6.5 %'], ['new york 5', 'barent gardenier', 'federalist', '1806', 'retired democratic - republican gain', 'thomas b cooke ( dr ) 52.1 % gerrit abeel ( f ) 47.9 %'], ['new york 8', 'john thompson', 'democratic - republican', '1806', 'retired democratic - republican hold', 'benjamin pond ( dr ) 57.6 % james mccrea ( f ) 42.4 %'], ['new york 10', 'john nicholson', 'democratic - republican', '1808', 'retired democratic - republican hold', 'silas stow ( dr ) 51.3 % simeon ford ( f ) 48.7 %'], ['new york 11', 'thomas r gold', 'federalist', '1808', 're - elected', 'thomas r gold ( f ) 52.6 % thomas skinner ( dr ) 47.4 %'], ['new york 12', 'erastus root', 'democratic - republican', '1808', 'retired democratic - republican hold', 'arunah metcalf ( dr ) 56.2 % john m bowers ( f ) 43.8 %'], ['new york 13', 'uri tracy', 'democratic - republican', '1808', 're - elected', 'uri tracy ( dr ) 60.2 % nathaniel waldron ( f ) 39.8 %']]
|
list of bohemian consorts
|
https://en.wikipedia.org/wiki/List_of_Bohemian_consorts
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10870631-3.html.csv
|
unique
|
leopold vi , duke of austria 's daughter was the only bohemian consort to cease being queen due to divorce .
|
{'scope': 'all', 'row': '4', 'col': '5', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'divorced', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ceased to be queen', 'divorced'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ceased to be queen record fuzzily matches to divorced .', 'tostr': 'filter_eq { all_rows ; ceased to be queen ; divorced }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; ceased to be queen ; divorced } }', 'tointer': 'select the rows whose ceased to be queen record fuzzily matches to divorced . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ceased to be queen', 'divorced'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ceased to be queen record fuzzily matches to divorced .', 'tostr': 'filter_eq { all_rows ; ceased to be queen ; divorced }'}, 'father'], 'result': 'leopold vi , duke of austria', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; ceased to be queen ; divorced } ; father }'}, 'leopold vi , duke of austria'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; ceased to be queen ; divorced } ; father } ; leopold vi , duke of austria }', 'tointer': 'the father record of this unqiue row is leopold vi , duke of austria .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; ceased to be queen ; divorced } } ; eq { hop { filter_eq { all_rows ; ceased to be queen ; divorced } ; father } ; leopold vi , duke of austria } } = true', 'tointer': 'select the rows whose ceased to be queen record fuzzily matches to divorced . there is only one such row in the table . the father record of this unqiue row is leopold vi , duke of austria .'}
|
and { only { filter_eq { all_rows ; ceased to be queen ; divorced } } ; eq { hop { filter_eq { all_rows ; ceased to be queen ; divorced } ; father } ; leopold vi , duke of austria } } = true
|
select the rows whose ceased to be queen record fuzzily matches to divorced . there is only one such row in the table . the father record of this unqiue row is leopold vi , duke of austria .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'ceased to be queen_7': 7, 'divorced_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'father_9': 9, 'leopold vi , duke of austria_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'ceased to be queen_7': 'ceased to be queen', 'divorced_8': 'divorced', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'father_9': 'father', 'leopold vi , duke of austria_10': 'leopold vi , duke of austria'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'ceased to be queen_7': [0], 'divorced_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'father_9': [2], 'leopold vi , duke of austria_10': [3]}
|
['father', 'birth', 'marriage', 'became queen', 'ceased to be queen', 'death', 'spouse']
|
[['otto ii , margrave of meissen', '1160 +', '1178', '1198', '1199', '2 feb 1211', 'ottokar i'], ['bãla iii of hungary', '1181', '1199', '1199', '1230', '6 dec 1240', 'ottokar i'], ['philip of swabia', '1200', '1224', '1230', '13 sep 1248', '13 sep 1248', 'wenceslaus i'], ['leopold vi , duke of austria', '1204', 'feb 1252', '1253', '1260 divorced', '29 oct 1266', 'ottokar ii'], ['rostislav of slavonia', '1245', '25 oct 1261', '25 oct 1261', '1278', '9 sep 1285', 'ottokar ii'], ['rudolf i of habsburg', '13 mar 1271', "24 jan 1285 '", "24 jan 1285 '", '18 jun 1297', '18 jun 1297', 'wenceslaus ii'], ['przemysl ii of poland', '1 sep 1286', '1300', '1303', '1305', '18 october 1335', 'wenceslaus ii'], ['mieszko i , duke of teschen', 'c 1290', '1305', '1305', '1306', '21 september 1317', 'wenceslaus iii']]
|
2008 issf world cup final ( shotgun )
|
https://en.wikipedia.org/wiki/2008_ISSF_World_Cup_Final_%28shotgun%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18351792-10.html.csv
|
count
|
6 of the events in the 2008 issf world cup final took place in beijing .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'beijing', 'result': '6', 'col': '2', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'beijing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose event record fuzzily matches to beijing .', 'tostr': 'filter_eq { all_rows ; event ; beijing }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; event ; beijing } }', 'tointer': 'select the rows whose event record fuzzily matches to beijing . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; event ; beijing } } ; 6 } = true', 'tointer': 'select the rows whose event record fuzzily matches to beijing . the number of such rows is 6 .'}
|
eq { count { filter_eq { all_rows ; event ; beijing } } ; 6 } = true
|
select the rows whose event record fuzzily matches to beijing . 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, 'event_5': 5, 'beijing_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', 'event_5': 'event', 'beijing_6': 'beijing', '6_7': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'event_5': [0], 'beijing_6': [0], '6_7': [2]}
|
['shooter', 'event', 'rank points', 'score points', 'total']
|
[['erdzhanik avetisyan ( rus )', 'wcf 2007', 'defending champion', 'defending champion', 'defending champion'], ['chiara cainero ( ita )', 'og beijing', 'olympic gold medalist', 'olympic gold medalist', 'olympic gold medalist'], ['kim rhode ( usa )', 'og beijing', 'olympic silver medalist', 'olympic silver medalist', 'olympic silver medalist'], ['christine brinker ( ger )', 'og beijing', 'olympic bronze medalist', 'olympic bronze medalist', 'olympic bronze medalist'], ['haley dunn ( usa )', 'wc kerrville', '15', '14', '29'], ['andri eleftheriou ( cyp )', 'wc belgrade', '15', '14', '29'], ['wei ning ( chn )', 'wc beijing', '15', '13', '28'], ['diana bacosi ( ita )', 'wc suhl', '15', '13', '28'], ['danka bartekovã ¡ ( svk )', 'wc suhl', '10', '13', '23'], ['connie smotek ( usa )', 'wc kerrville', '8', '10', '18'], ['zhang shan ( chn )', 'wc beijing', '4', '12', '16'], ['nathalie larsson ( swe )', 'wc beijing', '5', '11', '16']]
|
united states house of representatives elections , 2000
|
https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2000
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341423-35.html.csv
|
ordinal
|
for the united states house of representatives election in 2000 , of the incumbents that were re-elected , the one with the 2nd most recent first election date was from ohio district 10 .
|
{'scope': 'subset', 'row': '9', 'col': '4', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '5', 'criterion': 'equal', 'value': 're - elected'}}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'results', 're - elected'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; results ; re - elected }', 'tointer': 'select the rows whose results record fuzzily matches to re - elected .'}, 'first elected', '2'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_eq { all_rows ; results ; re - elected } ; first elected ; 2 }'}, 'district'], 'result': 'ohio 10', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_eq { all_rows ; results ; re - elected } ; first elected ; 2 } ; district }'}, 'ohio 10'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_eq { all_rows ; results ; re - elected } ; first elected ; 2 } ; district } ; ohio 10 } = true', 'tointer': 'select the rows whose results record fuzzily matches to re - elected . select the row whose first elected record of these rows is 2nd maximum . the district record of this row is ohio 10 .'}
|
eq { hop { nth_argmax { filter_eq { all_rows ; results ; re - elected } ; first elected ; 2 } ; district } ; ohio 10 } = true
|
select the rows whose results record fuzzily matches to re - elected . select the row whose first elected record of these rows is 2nd maximum . the district record of this row is ohio 10 .
|
4
|
4
|
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'results_6': 6, 're - elected_7': 7, 'first elected_8': 8, '2_9': 9, 'district_10': 10, 'ohio 10_11': 11}
|
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'results_6': 'results', 're - elected_7': 're - elected', 'first elected_8': 'first elected', '2_9': '2', 'district_10': 'district', 'ohio 10_11': 'ohio 10'}
|
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'results_6': [0], 're - elected_7': [0], 'first elected_8': [1], '2_9': [1], 'district_10': [2], 'ohio 10_11': [3]}
|
['district', 'incumbent', 'party', 'first elected', 'results', 'candidates']
|
[['ohio 1', 'steve chabot', 'republican', '1994', 're - elected', 'steve chabot ( r ) 54 % john cranley ( d ) 45 %'], ['ohio 3', 'tony p hall', 'democratic', '1978', 're - elected', 'tony p hall ( d ) 83 %'], ['ohio 4', 'michael g oxley', 'republican', '1981', 're - elected', 'michael g oxley ( r ) 68 % daniel dickman ( d ) 30 %'], ['ohio 5', 'paul e gillmor', 'republican', '1988', 're - elected', 'paul e gillmor ( r ) 70 % dannie edmon ( d ) 26 %'], ['ohio 6', 'ted strickland', 'democratic', '1992', 're - elected', 'ted strickland ( d ) 58 % mike azinger ( r ) 41 %'], ['ohio 7', 'david l hobson', 'republican', '1990', 're - elected', 'david l hobson ( r ) 68 % donald minor ( d ) 25 %'], ['ohio 8', 'john a boehner', 'republican', '1990', 're - elected', 'john a boehner ( r ) 71 % john parks ( d ) 27 %'], ['ohio 9', 'marcia c kaptur', 'democratic', '1982', 're - elected', 'marcia c kaptur ( d ) 75 % dwight bryan ( r ) 23 %'], ['ohio 10', 'dennis j kucinich', 'democratic', '1996', 're - elected', 'dennis j kucinich ( d ) 76 % bill smith ( r ) 23 %'], ['ohio 11', 'stephanie tubbs jones', 'democratic', '1998', 're - elected', 'stephanie tubbs jones ( d ) 86 % james sykora ( r ) 12 %'], ['ohio 12', 'john kasich', 'republican', '1982', 'retired republican hold', "pat tiberi ( r ) 53 % maryellen o ' shaughnessy ( d ) 44 %"], ['ohio 13', 'sherrod brown', 'democratic', '1992', 're - elected', 'sherrod brown ( d ) 65 % rick jeric ( r ) 33 %'], ['ohio 14', 'tom sawyer', 'democratic', '1986', 're - elected', 'tom sawyer ( d ) 65 % rick wood ( r ) 32 %'], ['ohio 15', 'deborah d pryce', 'republican', '1992', 're - elected', 'deborah d pryce ( r ) 68 % bill buckel ( d ) 28 %'], ['ohio 16', 'ralph s regula', 'republican', '1972', 're - elected', 'ralph s regula ( r ) 70 % william smith ( d ) 27 %'], ['ohio 17', 'james traficant', 'democratic', '1984', 're - elected', 'james traficant ( d ) 50 % paul alberty ( r ) 23 %']]
|
chalid arrab
|
https://en.wikipedia.org/wiki/Chalid_Arrab
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10363239-3.html.csv
|
count
|
three of the events were held in various cities of the country japan .
|
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'japan', 'result': '3', 'col': '6', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'japan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to japan .', 'tostr': 'filter_eq { all_rows ; location ; japan }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; japan } }', 'tointer': 'select the rows whose location record fuzzily matches to japan . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; japan } } ; 3 } = true', 'tointer': 'select the rows whose location record fuzzily matches to japan . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; location ; japan } } ; 3 } = true
|
select the rows whose location record fuzzily matches to japan . 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, 'location_5': 5, 'japan_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', 'location_5': 'location', 'japan_6': 'japan', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'japan_6': [0], '3_7': [2]}
|
['res', 'record', 'opponent', 'method', 'event', 'location']
|
[['win', '7 - 3', 'hiromitsu kanehara', 'decision ( majority )', "hero 's 2005 in seoul", 'seoul , south korea'], ['win', '6 - 3', 'yukiya naito', 'decision ( unanimous )', "hero 's 1", 'saitama , saitama , japan'], ['loss', '5 - 3', 'kazuhiro nakamura', 'submission ( armbar )', 'pride bushido 3', 'yokohama , japan'], ['win', '5 - 2', 'rodney glunder', 'decision ( unanimous )', 'pride bushido 1', 'saitama , saitama , japan'], ['loss', '4 - 2', 'jeremy horn', 'decision ( unanimous )', '2h2h 6 - simply the best 6', 'rotterdam , netherlands'], ['win', '4 - 1', 'stanislav nuschik', 'ko ( punches )', 'm - 1 mfc - european championship 2002', 'saint petersburg , russia'], ['win', '3 - 1', 'roman zentsov', 'ko', 'm - 1 mfc - russia vs the world 2', 'saint petersburg , russia'], ['win', '2 - 1', 'peter varga', 'submission ( arm lock )', 'millenniumsports - veni vidi vici', 'veenendaal , netherlands'], ['loss', '1 - 1', 'ramazan mezhidov', 'submission ( rear naked choke )', 'iafc - pankration world championship 2000', 'moscow , russia'], ['win', '1 - 0', 'spartak kochnev', 'tko ( strikes )', 'iafc - pankration world championship 2000', 'moscow , russia']]
|
list of ngc objects ( 6001 - 7000 )
|
https://en.wikipedia.org/wiki/List_of_NGC_objects_%286001%E2%80%937000%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11051842-3.html.csv
|
unique
|
ngc number 6240 is the only object type labeled as an irregular galaxy and ophiuchus .
|
{'scope': 'all', 'row': '5', 'col': '2', 'col_other': '1,3', 'criterion': 'equal', 'value': 'irregular galaxy', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'object type', 'irregular galaxy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose object type record fuzzily matches to irregular galaxy .', 'tostr': 'filter_eq { all_rows ; object type ; irregular galaxy }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; object type ; irregular galaxy } }', 'tointer': 'select the rows whose object type record fuzzily matches to irregular galaxy . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'object type', 'irregular galaxy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose object type record fuzzily matches to irregular galaxy .', 'tostr': 'filter_eq { all_rows ; object type ; irregular galaxy }'}, 'ngc number'], 'result': '6240', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number }'}, '6240'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number } ; 6240 }', 'tointer': 'the ngc number record of this unqiue row is 6240 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'object type', 'irregular galaxy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose object type record fuzzily matches to irregular galaxy .', 'tostr': 'filter_eq { all_rows ; object type ; irregular galaxy }'}, 'constellation'], 'result': 'ophiuchus', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation }'}, 'ophiuchus'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation } ; ophiuchus }', 'tointer': 'the constellation record of this unqiue row is ophiuchus .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number } ; 6240 } ; eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation } ; ophiuchus } }', 'tointer': 'the ngc number record of this unqiue row is 6240 . the constellation record of this unqiue row is ophiuchus .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; object type ; irregular galaxy } } ; and { eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number } ; 6240 } ; eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation } ; ophiuchus } } } = true', 'tointer': 'select the rows whose object type record fuzzily matches to irregular galaxy . there is only one such row in the table . the ngc number record of this unqiue row is 6240 . the constellation record of this unqiue row is ophiuchus .'}
|
and { only { filter_eq { all_rows ; object type ; irregular galaxy } } ; and { eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; ngc number } ; 6240 } ; eq { hop { filter_eq { all_rows ; object type ; irregular galaxy } ; constellation } ; ophiuchus } } } = true
|
select the rows whose object type record fuzzily matches to irregular galaxy . there is only one such row in the table . the ngc number record of this unqiue row is 6240 . the constellation record of this unqiue row is ophiuchus .
|
10
|
8
|
{'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'object type_10': 10, 'irregular galaxy_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'ngc number_12': 12, '6240_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'constellation_14': 14, 'ophiuchus_15': 15}
|
{'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'object type_10': 'object type', 'irregular galaxy_11': 'irregular galaxy', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'ngc number_12': 'ngc number', '6240_13': '6240', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'constellation_14': 'constellation', 'ophiuchus_15': 'ophiuchus'}
|
{'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'object type_10': [0], 'irregular galaxy_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'ngc number_12': [2], '6240_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'constellation_14': [4], 'ophiuchus_15': [5]}
|
['ngc number', 'object type', 'constellation', 'right ascension ( j2000 )', 'declination ( j2000 )', 'apparent magnitude']
|
[['6205', 'globular cluster', 'hercules', '16h41 m41s', 'degree27 ′ 37 ″', '5.8'], ['6210', 'planetary nebula', 'hercules', '16h44 m29 .5 s', 'degree48 ′ 00 ″', '12.3'], ['6218', 'globular cluster', 'ophiuchus', '16h47 m14 .5 s', 'degree56 ′ 52 ″', '8.5'], ['6231', 'open cluster', 'scorpius', '16h54 m08 .5 s', 'degree49 ′ 36 ″', '2.8'], ['6240', 'irregular galaxy', 'ophiuchus', '16h52 m59 .0 s', 'degree24 ′ 02 ″', '14.7'], ['6242', 'open cluster', 'scorpius', '16h55 m', 'degree28 ′', '7.1'], ['6254', 'globular cluster', 'ophiuchus', '16h57 m09 .0 s', 'degree05 ′ 58 ″', '6.4'], ['6266', 'globular cluster', 'ophiuchus', '17h01 m12 .6 s', 'degree06 ′ 45 ″', '8.6'], ['6273', 'globular cluster', 'ophiuchus', '17h02 m37 .7 s', 'degree16 ′ 05 ″', '8.5']]
|
1971 african cup of champions clubs
|
https://en.wikipedia.org/wiki/1971_African_Cup_of_Champions_Clubs
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12423174-1.html.csv
|
superlative
|
canon yaoundé scored the highest number of total goals in the 1971 african cup of champions .
|
{'scope': 'all', 'col_superlative': '2', 'row_superlative': '6', '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', 'agg'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; agg }'}, 'team 1'], 'result': 'canon yaoundé', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; agg } ; team 1 }'}, 'canon yaoundé'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; agg } ; team 1 } ; canon yaoundé } = true', 'tointer': 'select the row whose agg record of all rows is maximum . the team 1 record of this row is canon yaoundé .'}
|
eq { hop { argmax { all_rows ; agg } ; team 1 } ; canon yaoundé } = true
|
select the row whose agg record of all rows is maximum . the team 1 record of this row is canon yaoundé .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'agg_5': 5, 'team 1_6': 6, 'canon yaoundé_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'agg_5': 'agg', 'team 1_6': 'team 1', 'canon yaoundé_7': 'canon yaoundé'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'agg_5': [0], 'team 1_6': [1], 'canon yaoundé_7': [2]}
|
['team 1', 'agg', 'team 2', '1st leg', '2nd leg']
|
[['al - merrikh', '2 - 2 ( 5 - 4 pen )', 'tele sc asmara', '2 - 1', '0 - 1'], ['abaluhya united', '1 - 3', 'great olympics', '0 - 0', '1 - 3'], ['asc diaraf', '3 - 4', 'stade malien', '3 - 0', '0 - 4'], ['maseru united', '3 - 5', 'mmm tamatave', '1 - 2', '2 - 3'], ['as porto novo', '0 - 3', 'victoria club mokanda', '0 - 1', '0 - 2'], ['canon yaoundé', '9 - 4', 'as solidarité', '7 - 3', '2 - 1'], ['espérance', '1 - 0', 'al - ahly ( benghazi )', '0 - 0', '1 - 0'], ['secteur 6', '1 - 2', 'enugu rangers', '1 - 1', '0 - 1'], ['young africans', '2 - 0', 'lavori publici', '2 - 0', '0 - 0']]
|
1940 vfl season
|
https://en.wikipedia.org/wiki/1940_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10807253-11.html.csv
|
count
|
two games had a crowd of 10,000 people .
|
{'scope': 'all', 'criterion': 'equal', 'value': '10000', 'result': '2', 'col': '6', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'crowd', '10000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose crowd record is equal to 10000 .', 'tostr': 'filter_eq { all_rows ; crowd ; 10000 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; crowd ; 10000 } }', 'tointer': 'select the rows whose crowd record is equal to 10000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; crowd ; 10000 } } ; 2 } = true', 'tointer': 'select the rows whose crowd record is equal to 10000 . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; crowd ; 10000 } } ; 2 } = true
|
select the rows whose crowd record is equal to 10000 . 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, 'crowd_5': 5, '10000_6': 6, '2_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', '10000_6': '10000', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '10000_6': [0], '2_7': [2]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['geelong', '12.22 ( 94 )', 'st kilda', '11.10 ( 76 )', 'corio oval', '6500', '6 july 1940'], ['fitzroy', '17.8 ( 110 )', 'footscray', '14.13 ( 97 )', 'brunswick street oval', '18000', '6 july 1940'], ['essendon', '19.14 ( 128 )', 'north melbourne', '16.9 ( 105 )', 'windy hill', '11000', '6 july 1940'], ['south melbourne', '10.18 ( 78 )', 'melbourne', '19.16 ( 130 )', 'lake oval', '10000', '6 july 1940'], ['hawthorn', '10.17 ( 77 )', 'collingwood', '14.17 ( 101 )', 'glenferrie oval', '10000', '6 july 1940'], ['richmond', '9.11 ( 65 )', 'carlton', '9.22 ( 76 )', 'punt road oval', '18000', '6 july 1940']]
|
list of australia one day international cricket records
|
https://en.wikipedia.org/wiki/List_of_Australia_One_Day_International_cricket_records
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21100348-10.html.csv
|
superlative
|
ricky ponting holds the highest number of innings in the australia one day international cricket records .
|
{'scope': 'all', 'col_superlative': '5', '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', 'innings'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; innings }'}, 'player'], 'result': 'ricky ponting', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; innings } ; player }'}, 'ricky ponting'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; innings } ; player } ; ricky ponting } = true', 'tointer': 'select the row whose innings record of all rows is maximum . the player record of this row is ricky ponting .'}
|
eq { hop { argmax { all_rows ; innings } ; player } ; ricky ponting } = true
|
select the row whose innings record of all rows is maximum . the player record of this row is ricky ponting .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'innings_5': 5, 'player_6': 6, 'ricky ponting_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'innings_5': 'innings', 'player_6': 'player', 'ricky ponting_7': 'ricky ponting'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'innings_5': [0], 'player_6': [1], 'ricky ponting_7': [2]}
|
['rank', 'average', 'player', 'runs', 'innings', 'not out', 'period']
|
[['1', '56.85', 'george bailey', '1535', '33', '4', '2012 -'], ['2', '53.58', 'michael bevan', '6912', '196', '67', '1994 - 2004'], ['3', '52.53', 'adam voges', '683', '20', '7', '2007 -'], ['4', '48.15', 'mike hussey', '5442', '157', '44', '2004 - 2012'], ['5', '45.08', 'michael clarke', '7484', '209', '43', '2003 -'], ['6', '44.61', 'dean jones', '6068', '161', '25', '1984 - 1994'], ['7', '44.10', 'matthew hayden', '6131', '154', '15', '1993 - 2008'], ['8', '41.81', 'ricky ponting', '13589', '364', '39', '1995 - 2012'], ['9', '41.43', 'callum ferguson', '663', '25', '9', '2009 - 2011']]
|
list of kyle xy episodes
|
https://en.wikipedia.org/wiki/List_of_Kyle_XY_episodes
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11075747-4.html.csv
|
unique
|
peter deluise was the only director who directed only a single episode of kyle xy .
|
{'scope': 'all', 'row': '8', 'col': '4', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': 'peter deluise', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'peter deluise'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to peter deluise .', 'tostr': 'filter_eq { all_rows ; directed by ; peter deluise }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; directed by ; peter deluise } } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to peter deluise . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; directed by ; peter deluise } } = true
|
select the rows whose directed by record fuzzily matches to peter deluise . 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, 'directed by_4': 4, 'peter deluise_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'directed by_4': 'directed by', 'peter deluise_5': 'peter deluise'}
|
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'directed by_4': [0], 'peter deluise_5': [0]}
|
['series', 'episode', 'title', 'directed by', 'written by', 'original air date']
|
[['34', '1', 'it happened one night', 'chris grismer', 'eric tuchman', 'january 12 , 2009'], ['35', '2', 'psychic friend', 'michael robison', 'julie plec', 'january 19 , 2009'], ['36', '3', 'electric kiss', 'chris grismer', 'gayle abrams', 'january 26 , 2009'], ['37', '4', 'in the company of men', 'guy norman bee', 'daniel arkin', 'february 2 , 2009'], ['38', '5', 'life support', 'michael robison', 'bryan m holdman', 'february 9 , 2009'], ['39', '6', 'welcome to latnok', 'guy norman bee', 'rp gaborno & chris hollier', 'february 16 , 2009'], ['40', '7', 'chemistry 101', 'james head', 'steven lilien & bryan wynbrandt', 'february 23 , 2009'], ['41', '8', 'tell - tale heart', 'peter deluise', 'gayle abrams & brian ridings', 'march 2 , 2009'], ['42', '9', "guess who 's coming to dinner", 'james head', 'daniel arkin & andrea conway', 'march 9 , 2009']]
|
2008 - 09 manchester city f.c. season
|
https://en.wikipedia.org/wiki/2008%E2%80%9309_Manchester_City_F.C._season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17370522-17.html.csv
|
unique
|
matthew mills was the only player that was transferred for a fee in the 2008 - 09 manchester city f.c. season .
|
{'scope': 'all', 'row': '6', 'col': '5', 'col_other': '3', 'criterion': 'greater_than', 'value': '0', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'transfer fee', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose transfer fee record is greater than 0 .', 'tostr': 'filter_greater { all_rows ; transfer fee ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; transfer fee ; 0 } }', 'tointer': 'select the rows whose transfer fee record is greater than 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'transfer fee', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose transfer fee record is greater than 0 .', 'tostr': 'filter_greater { all_rows ; transfer fee ; 0 }'}, 'player'], 'result': 'matthew mills', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; transfer fee ; 0 } ; player }'}, 'matthew mills'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; transfer fee ; 0 } ; player } ; matthew mills }', 'tointer': 'the player record of this unqiue row is matthew mills .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; transfer fee ; 0 } } ; eq { hop { filter_greater { all_rows ; transfer fee ; 0 } ; player } ; matthew mills } } = true', 'tointer': 'select the rows whose transfer fee record is greater than 0 . there is only one such row in the table . the player record of this unqiue row is matthew mills .'}
|
and { only { filter_greater { all_rows ; transfer fee ; 0 } } ; eq { hop { filter_greater { all_rows ; transfer fee ; 0 } ; player } ; matthew mills } } = true
|
select the rows whose transfer fee record is greater than 0 . there is only one such row in the table . the player record of this unqiue row is matthew mills .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'transfer fee_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'matthew mills_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'transfer fee_7': 'transfer fee', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'matthew mills_10': 'matthew mills'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'transfer fee_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'matthew mills_10': [3]}
|
['exit date', 'pos', 'player', 'to club', 'transfer fee']
|
[['1 july 2008', 'df', 'garry breen', 'hereford united', 'free'], ['1 july 2008', 'df', 'michael daly', 'released', 'released'], ['1 july 2008', 'gk', 'andrea giombetti', 'released', 'released'], ['1 july 2008', 'mf', 'ashley grimes', 'millwall', 'free'], ['1 july 2008', 'fw', 'christian mouritsen', 'released', 'released'], ['30 july 2008', 'df', 'matthew mills', 'doncaster rovers', '300000'], ['16 oct 2008', 'fw', 'teerasil dangda', 'released', 'released'], ['16 oct 2008', 'df', 'kiatprawut saiwaeo', 'released', 'released'], ['16 oct 2008', 'df', 'suree sukha', 'released', 'released'], ['02 jan 2009', 'df', 'sam williamson', 'wrexham', 'free']]
|
doves discography
|
https://en.wikipedia.org/wiki/Doves_discography
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10537807-4.html.csv
|
count
|
there are 3 songs on the album lost souls .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'lost souls', 'result': '3', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'album', 'lost souls'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose album record fuzzily matches to lost souls .', 'tostr': 'filter_eq { all_rows ; album ; lost souls }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; album ; lost souls } }', 'tointer': 'select the rows whose album record fuzzily matches to lost souls . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; album ; lost souls } } ; 3 } = true', 'tointer': 'select the rows whose album record fuzzily matches to lost souls . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; album ; lost souls } } ; 3 } = true
|
select the rows whose album record fuzzily matches to lost souls . 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, 'album_5': 5, 'lost souls_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', 'album_5': 'album', 'lost souls_6': 'lost souls', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'album_5': [0], 'lost souls_6': [0], '3_7': [2]}
|
['song', 'release date', 'release info', 'formats', 'album']
|
[['here it comes', '2 august 1999', 'casino ( chip003 )', 'cd , 10 vinyl', 'here it comes ep'], ['the cedar room', '20 march 2000', 'heavenly ( hvn95 )', 'cd , 10 vinyl', 'lost souls'], ['catch the sun', '29 may 2000', 'heavenly ( hvn96 )', 'cd1 , cd2 , 10 vinyl', 'lost souls'], ['the man who told everything', '30 october 2000', 'heavenly ( hvn98 )', 'cd1 , cd2 , 7 vinyl', 'lost souls'], ['there goes the fear', '15 april 2002', 'heavenly ( hvn111 )', 'cd , 10 vinyl', 'the last broadcast'], ['pounding', '22 july 2002', 'heavenly ( hvn116 )', 'cd , dvd , 10 vinyl', 'the last broadcast'], ['caught by the river', '14 october 2002', 'heavenly ( hvn126 )', 'ecd , cd , 10 vinyl', 'the last broadcast'], ['black and white town', '7 february 2005', 'heavenly ( hvn145 )', 'cd1 , cd2 , 7 vinyl', 'some cities'], ['snowden', '9 may 2005', 'heavenly ( hvn150 )', 'cd1 , cd2 , 7 vinyl', 'some cities'], ['sky starts falling', '12 september 2005', 'heavenly ( hvn152 )', 'cd , dvd , 7 vinyl', 'some cities'], ['kingdom of rust', '30 march 2009', 'heavenly ( hvn189 )', 'cd , 7 vinyl , 3 x 12 vinyl', 'kingdom of rust'], ['winter hill', '20 july 2009', 'heavenly ( hvn192 )', '7 vinyl , 3 x 12 vinyl', 'kingdom of rust'], ['andalucia', '5 april 2010', 'heavenly ( hvn201 )', 'dl', 'the places between : the best of doves'], ['- denotes a release that did not chart', '- denotes a release that did not chart', '- denotes a release that did not chart', '- denotes a release that did not chart', '- denotes a release that did not chart']]
|
water polo at the 2004 summer olympics - men 's team rosters
|
https://en.wikipedia.org/wiki/Water_polo_at_the_2004_Summer_Olympics_%E2%80%93_Men%27s_team_rosters
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17759945-7.html.csv
|
majority
|
most of the men 's water polo players at the 2004 summer olympics , were born in the 1970s .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '1980', 'subset': None}
|
{'func': 'most_less', 'args': ['all_rows', 'date of birth', '1980'], 'result': True, 'ind': 0, 'tointer': 'for the date of birth records of all rows , most of them are less than 1980 .', 'tostr': 'most_less { all_rows ; date of birth ; 1980 } = true'}
|
most_less { all_rows ; date of birth ; 1980 } = true
|
for the date of birth records of all rows , most of them are less than 1980 .
|
1
|
1
|
{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date of birth_3': 3, '1980_4': 4}
|
{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date of birth_3': 'date of birth', '1980_4': '1980'}
|
{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'date of birth_3': [0], '1980_4': [0]}
|
['name', 'pos', 'height', 'weight', 'date of birth', 'club']
|
[['zoltán szécsi', 'gk', 'm ( ft 6in )', '-', '1977 - 12 - 22', 'bvsc vízilabda'], ['tamás varga', 'cb', 'm ( ft 4in )', '-', '1975 - 07 - 14', 'vasas sc'], ['norbert madaras', 'cf', 'm ( ft 3in )', '-', '1979 - 12 - 01', 'vasas sc'], ['ádám steinmetz', 'cf', 'm ( ft 6in )', '-', '1980 - 08 - 11', 'vasas sc'], ['tamás kásás', 'd', 'm ( ft 7in )', '-', '1976 - 07 - 20', 'vasas sc'], ['attila vári', 'cb', 'm ( ft 7in )', '-', '1976 - 02 - 26', 'domino bhse'], ['gergely kiss', 'cf', 'm ( ft 6in )', '-', '1977 - 09 - 21', 'domino bhse'], ['tibor benedek', 'cf', 'm ( ft 3in )', '-', '1972 - 07 - 12', 'pro recco'], ['rajmund fodor', 'd', 'm ( ft 3in )', '-', '1976 - 02 - 21', 'domino bhse'], ['istván gergely', 'gk', 'm ( ft 8in )', '-', '1976 - 08 - 20', 'domino bhse'], ['barnabás steinmetz', 'cb', 'm ( ft 5in )', '-', '1975 - 10 - 06', 'vasas sc'], ['tamás molnár', 'cf', 'm ( ft 5in )', '-', '1975 - 08 - 02', 'domino bhse'], ['péter biros', 'cf', 'm ( ft 4in )', '-', '1976 - 04 - 05', 'domino bhse']]
|
chutes too narrow
|
https://en.wikipedia.org/wiki/Chutes_Too_Narrow
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1236321-2.html.csv
|
count
|
in 2009 , chutes too narrow had a total of six accolades from the us and the uk .
|
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '3', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'accolade'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose accolade record is arbitrary .', 'tostr': 'filter_all { all_rows ; accolade }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; accolade } }', 'tointer': 'select the rows whose accolade record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; accolade } } ; 6 } = true', 'tointer': 'select the rows whose accolade record is arbitrary . the number of such rows is 6 .'}
|
eq { count { filter_all { all_rows ; accolade } } ; 6 } = true
|
select the rows whose accolade record is arbitrary . the number of such rows is 6 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'accolade_5': 5, '6_6': 6}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'accolade_5': 'accolade', '6_6': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'accolade_5': [0], '6_6': [2]}
|
['publication', 'country', 'accolade', 'year', 'rank']
|
[['the av club', 'us', 'the best music of the decade', '2009', '17'], ['nme', 'uk', 'the top 100 greatest albums of the decade', '2009', '75'], ['paste', 'us', 'the 50 best albums of the decade ( 2000 - 2009 )', '2009', '24'], ['pitchfork media', 'us', 'the top 200 albums of the 2000s', '2009', '46'], ['slant magazine', 'us', 'best of the aughts : albums', '2009', '91'], ['uncut', 'uk', '150 greatest albums of the decade', '2009', '113']]
|
michael shenton
|
https://en.wikipedia.org/wiki/Michael_Shenton
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14979029-1.html.csv
|
unique
|
the only game that michael shenton played where 0 attempts were made was in 2004 .
|
{'scope': 'all', 'row': '1', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'tries', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tries record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; tries ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; tries ; 0 } }', 'tointer': 'select the rows whose tries record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'tries', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tries record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; tries ; 0 }'}, 'year'], 'result': '2004', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tries ; 0 } ; year }'}, '2004'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; tries ; 0 } ; year } ; 2004 }', 'tointer': 'the year record of this unqiue row is 2004 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; tries ; 0 } } ; eq { hop { filter_eq { all_rows ; tries ; 0 } ; year } ; 2004 } } = true', 'tointer': 'select the rows whose tries record is equal to 0 . there is only one such row in the table . the year record of this unqiue row is 2004 .'}
|
and { only { filter_eq { all_rows ; tries ; 0 } } ; eq { hop { filter_eq { all_rows ; tries ; 0 } ; year } ; 2004 } } = true
|
select the rows whose tries record is equal to 0 . there is only one such row in the table . the year record of this unqiue row is 2004 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'tries_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'year_9': 9, '2004_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'tries_7': 'tries', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'year_9': 'year', '2004_10': '2004'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'tries_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'year_9': [2], '2004_10': [3]}
|
['year', 'team', 'apps', 'tries', 'goals', 'points']
|
[['2004', 'castleford tigers', '3', '0', '0', '0'], ['2005', 'castleford tigers', '29', '24', '0', '96'], ['2006', 'castleford tigers', '27', '8', '0', '32'], ['2007', 'castleford tigers', '20', '19', '0', '76'], ['2008', 'castleford tigers', '22', '13', '0', '52'], ['2009', 'castleford tigers', '30', '19', '0', '76'], ['2010', 'castleford tigers', '22', '10', '0', '40'], ['total', 'castleford tigers', '153', '93', '0', '372']]
|
1948 ashes series
|
https://en.wikipedia.org/wiki/1948_Ashes_series
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16570286-2.html.csv
|
aggregation
|
an average of 4.1 matches were played in the 1948 ashes series .
|
{'scope': 'all', 'col': '3', 'type': 'average', 'result': '4.1', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'matches'], 'result': '4.1', 'ind': 0, 'tostr': 'avg { all_rows ; matches }'}, '4.1'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; matches } ; 4.1 } = true', 'tointer': 'the average of the matches record of all rows is 4.1 .'}
|
round_eq { avg { all_rows ; matches } ; 4.1 } = true
|
the average of the matches record of all rows is 4.1 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'matches_4': 4, '4.1_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'matches_4': 'matches', '4.1_5': '4.1'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'matches_4': [0], '4.1_5': [1]}
|
['player', 'team', 'matches', 'innings', 'runs', 'average', 'highest score', '100s']
|
[['arthur morris', 'australia', '5', '9', '696', '87.00', '196', '3'], ['sid barnes', 'australia', '4', '6', '329', '82.25', '141', '1'], ['donald bradman', 'australia', '5', '9', '508', '72.57', '173', '2'], ['neil harvey', 'australia', '2', '3', '133', '66.50', '112', '1'], ['denis compton', 'england', '5', '10', '562', '62.44', '184', '2'], ['cyril washbrook', 'england', '4', '8', '356', '50.85', '143', '1'], ['sam loxton', 'australia', '3', '3', '144', '48.00', '93', '0'], ['lindsay hassett', 'australia', '5', '8', '310', '44.28', '137', '1'], ['len hutton', 'england', '4', '8', '342', '42.75', '81', '0']]
|
2008 continental cup of curling
|
https://en.wikipedia.org/wiki/2008_Continental_Cup_of_Curling
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18446443-1.html.csv
|
unique
|
oslo was the home of only 1 team .
|
{'scope': 'all', 'row': '12', 'col': '3', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'oslo', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home', 'oslo'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose home record fuzzily matches to oslo .', 'tostr': 'filter_eq { all_rows ; home ; oslo }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; home ; oslo } } = true', 'tointer': 'select the rows whose home record fuzzily matches to oslo . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; home ; oslo } } = true
|
select the rows whose home record fuzzily matches to oslo . 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, 'home_4': 4, 'oslo_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'home_4': 'home', 'oslo_5': 'oslo'}
|
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'home_4': [0], 'oslo_5': [0]}
|
['team', 'country', 'home', 'skip', 'third', 'second', 'lead']
|
[['north america', 'united states', 'madison , wisconsin', 'craig brown', 'rich ruohonen', 'john dunlop', 'peter annis'], ['north america', 'canada', 'winnipeg , manitoba', 'jennifer jones', 'cathy overton - clapham', 'jill officer', 'dawn askin'], ['north america', 'canada', 'edmonton , alberta', 'kevin koe', 'blake macdonald', 'carter rycroft', 'nolan thiessen'], ['north america', 'canada', 'saskatoon , saskatchewan', 'stefanie lawton', 'marliese kasner', 'teejay surik', 'lana vey'], ['north america', 'canada', 'edmonton , alberta', 'kevin martin', 'john morris', 'marc kennedy', 'ben hebert'], ['north america', 'united states', 'madison , wisconsin', 'debbie mccormick', 'allison pottinger', 'nicole joraanstad', 'tracy sachtjen'], ['world', 'scotland / sweden', 'lockerbie', 'david murdoch', 'ewan macdonald', 'niklas edin', 'euan byers'], ['world', 'sweden', 'härnösand', 'anette norberg', 'kajsa bergström', 'cathrine lindahl', 'anna svärd'], ['world', 'switzerland', 'davos', 'mirjam ott', 'carmen schäfer', 'valeria spälty', 'janine greiner'], ['world', 'china', 'harbin', 'wang bingyu', 'liu yin', 'yue qingshuang', 'zhou yan'], ['world', 'china', 'harbin', 'wang fengchun', 'liu rui', 'xu xiaoming', 'zang jialiang'], ['world', 'norway', 'oslo', 'thomas ulsrud', 'torger nergård', 'christoffer svae', 'håvard vad petersson']]
|
indiana high school athletics conferences : mid - eastern - northwestern
|
https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Mid-Eastern_%E2%80%93_Northwestern
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18942405-2.html.csv
|
unique
|
indian creek is the only school with ' aaa ' ihsaa class in the indiana high school athletics conference .
|
{'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'aaa', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ihsaa class', 'aaa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ihsaa class record fuzzily matches to aaa .', 'tostr': 'filter_eq { all_rows ; ihsaa class ; aaa }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; ihsaa class ; aaa } }', 'tointer': 'select the rows whose ihsaa class record fuzzily matches to aaa . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ihsaa class', 'aaa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ihsaa class record fuzzily matches to aaa .', 'tostr': 'filter_eq { all_rows ; ihsaa class ; aaa }'}, 'school'], 'result': 'indian creek', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; ihsaa class ; aaa } ; school }'}, 'indian creek'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; ihsaa class ; aaa } ; school } ; indian creek }', 'tointer': 'the school record of this unqiue row is indian creek .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; ihsaa class ; aaa } } ; eq { hop { filter_eq { all_rows ; ihsaa class ; aaa } ; school } ; indian creek } } = true', 'tointer': 'select the rows whose ihsaa class record fuzzily matches to aaa . there is only one such row in the table . the school record of this unqiue row is indian creek .'}
|
and { only { filter_eq { all_rows ; ihsaa class ; aaa } } ; eq { hop { filter_eq { all_rows ; ihsaa class ; aaa } ; school } ; indian creek } } = true
|
select the rows whose ihsaa class record fuzzily matches to aaa . there is only one such row in the table . the school record of this unqiue row is indian creek .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'ihsaa class_7': 7, 'aaa_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'school_9': 9, 'indian creek_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'ihsaa class_7': 'ihsaa class', 'aaa_8': 'aaa', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'school_9': 'school', 'indian creek_10': 'indian creek'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'ihsaa class_7': [0], 'aaa_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'school_9': [2], 'indian creek_10': [3]}
|
['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'county']
|
[['edinburgh community', 'edinburgh', 'lancers', '269', 'a', '41 johnson'], ['hauser', 'hope', 'jets', '297', 'a', '03 bartholomew'], ['indian creek', 'trafalgar', 'braves', '608', 'aaa', '41 johnson'], ['morristown', 'morristown', 'yellow jackets', '231', 'a', '73 shelby'], ['north decatur', 'greensburg', 'chargers', '369', 'aa', '16 decatur'], ['south decatur', 'greensburg', 'cougars', '292', 'a', '16 decatur'], ['southwestern shelbyville', 'shelbyville', 'spartans', '218', 'a', '73 shelby'], ['triton central', 'fairland', 'tigers', '525', 'aa', '73 shelby'], ['waldron', 'waldron', 'mohawks', '237', 'a', '73 shelby']]
|
stuart appleby
|
https://en.wikipedia.org/wiki/Stuart_Appleby
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1662630-1.html.csv
|
majority
|
in most tournaments , stuart appleby had a 1 stroke margin of victory .
|
{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '1 stroke', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'margin of victory', '1 stroke'], 'result': True, 'ind': 0, 'tointer': 'for the margin of victory records of all rows , most of them fuzzily match to 1 stroke .', 'tostr': 'most_eq { all_rows ; margin of victory ; 1 stroke } = true'}
|
most_eq { all_rows ; margin of victory ; 1 stroke } = true
|
for the margin of victory records of all rows , most of them fuzzily match to 1 stroke .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'margin of victory_3': 3, '1 stroke_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'margin of victory_3': 'margin of victory', '1 stroke_4': '1 stroke'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'margin of victory_3': [0], '1 stroke_4': [0]}
|
['date', 'tournament', 'winning score', 'margin of victory', 'runner ( s ) - up']
|
[['16 mar 1997', 'honda classic', '14 ( 68 + 68 + 67 + 71 = 274 )', '1 stroke', 'michael bradley , payne stewart'], ['7 jun 1998', 'kemper open', '10 ( 70 + 63 + 69 + 72 = 274 )', '1 stroke', 'scott hoch'], ['2 may 1999', 'shell houston open', '9 ( 70 + 68 + 70 + 71 = 279 )', '1 stroke', 'john cook , hal sutton'], ['12 oct 2003', 'las vegas invitational', '31 ( 62 - 68 + 63 + 66 + 69 = 328 )', 'playoff', 'scott mccarron'], ['11 jan 2004', 'mercedes championships', '22 ( 66 + 67 + 66 + 71 = 270 )', '1 stroke', 'vijay singh'], ['9 jan 2005', 'mercedes championships ( 2 )', '21 ( 74 + 64 + 66 + 67 = 271 )', '1 stroke', 'jonathan kaye'], ['8 jan 2006', 'mercedes championships ( 3 )', '8 ( 71 + 72 + 70 + 71 = 284 )', 'playoff', 'vijay singh'], ['23 apr 2006', 'shell houston open ( 2 )', '19 ( 66 + 67 + 69 + 67 = 269 )', '6 strokes', 'bob estes'], ['1 aug 2010', 'greenbrier classic', '22 ( 66 + 68 + 65 + 59 = 258 )', '1 stroke', 'jeff overton']]
|
lgbt in islam
|
https://en.wikipedia.org/wiki/LGBT_in_Islam
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15471-1.html.csv
|
comparative
|
nigeria has a longer punishment for homosexuality than uzbekistan does .
|
{'row_1': '6', 'row_2': '9', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'nigeria'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to nigeria .', 'tostr': 'filter_eq { all_rows ; country ; nigeria }'}, 'penalty'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; nigeria } ; penalty }', 'tointer': 'select the rows whose country record fuzzily matches to nigeria . take the penalty record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'uzbekistan'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to uzbekistan .', 'tostr': 'filter_eq { all_rows ; country ; uzbekistan }'}, 'penalty'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; country ; uzbekistan } ; penalty }', 'tointer': 'select the rows whose country record fuzzily matches to uzbekistan . take the penalty record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; country ; nigeria } ; penalty } ; hop { filter_eq { all_rows ; country ; uzbekistan } ; penalty } } = true', 'tointer': 'select the rows whose country record fuzzily matches to nigeria . take the penalty record of this row . select the rows whose country record fuzzily matches to uzbekistan . take the penalty record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; country ; nigeria } ; penalty } ; hop { filter_eq { all_rows ; country ; uzbekistan } ; penalty } } = true
|
select the rows whose country record fuzzily matches to nigeria . take the penalty record of this row . select the rows whose country record fuzzily matches to uzbekistan . take the penalty 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, 'country_7': 7, 'nigeria_8': 8, 'penalty_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'country_11': 11, 'uzbekistan_12': 12, 'penalty_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', 'country_7': 'country', 'nigeria_8': 'nigeria', 'penalty_9': 'penalty', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'country_11': 'country', 'uzbekistan_12': 'uzbekistan', 'penalty_13': 'penalty'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'country_7': [0], 'nigeria_8': [0], 'penalty_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'country_11': [1], 'uzbekistan_12': [1], 'penalty_13': [3]}
|
['country', 'laws against homosexuality', 'penalty', 'same - sex unions', 'laws against discrimination']
|
[['afghanistan', 'yes', 'death', 'no', 'no'], ['egypt', 'no', 'prison', 'no', 'no'], ['indonesia', 'no', '-', '-', 'no'], ['iraq', 'no', '-', '-', 'no'], ['malaysia', 'yes', 'fine to 20 years', '-', 'no'], ['nigeria', 'yes', '5 - 14 years / death', '-', 'no'], ['pakistan', 'yes', '2 years to life', '-', 'no'], ['turkey', 'no', '-', '-', 'no'], ['uzbekistan', 'male only', 'fine to 3 years', '-', 'no']]
|
list of big brother ( uk ) shows
|
https://en.wikipedia.org/wiki/List_of_Big_Brother_%28UK%29_shows
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11748792-2.html.csv
|
unique
|
rylan clark is the only solo presenter for sunday big brother ( uk ) shows .
|
{'scope': 'all', 'row': '7', 'col': '8', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'rylan clark', 'subset': None}
|
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sunday', 'rylan clark'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sunday record fuzzily matches to rylan clark .', 'tostr': 'filter_eq { all_rows ; sunday ; rylan clark }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; sunday ; rylan clark } } = true', 'tointer': 'select the rows whose sunday record fuzzily matches to rylan clark . there is only one such row in the table .'}
|
only { filter_eq { all_rows ; sunday ; rylan clark } } = true
|
select the rows whose sunday record fuzzily matches to rylan clark . 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, 'sunday_4': 4, 'rylan clark_5': 5}
|
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'sunday_4': 'sunday', 'rylan clark_5': 'rylan clark'}
|
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'sunday_4': [0], 'rylan clark_5': [0]}
|
['series', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']
|
[['celebrity big brother 8', 'emma willis jamie east', 'emma willis', 'emma willis', 'emma willis', 'emma willis jamie east', 'alice levine jamie east', 'alice levine jamie east'], ['big brother 12', 'emma willis jamie east', 'emma willis', 'emma willis', 'emma willis', 'emma willis jamie east', 'alice levine jamie east', 'alice levine jamie east'], ['celebrity big brother 9', 'emma willis jamie east', 'emma willis', 'emma willis', 'emma willis', 'emma willis jamie east', 'alice levine jamie east', 'alice levine jamie east'], ['big brother 13', 'emma willis jamie east', 'emma willis', 'emma willis', 'emma willis', 'emma willis jamie east', 'alice levine jamie east', 'alice levine jamie east'], ['celebrity big brother 10', 'emma willis jamie east', 'emma willis', 'emma willis', 'emma willis', 'emma willis jamie east', 'alice levine jamie east', 'alice levine jamie east'], ['celebrity big brother 11', 'emma willis jamie east', 'emma willis', 'emma willis', 'emma willis', 'emma willis jamie east', 'alice levine jamie east', 'alice levine jamie east'], ['big brother 14', 'aj odudu rylan clark', 'emma willis', 'emma willis', 'emma willis', 'aj odudu rylan clark', 'aj odudu iain lee', 'rylan clark']]
|
the suite life on deck
|
https://en.wikipedia.org/wiki/The_Suite_Life_on_Deck
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15627191-3.html.csv
|
unique
|
the only time debby ryan was nominated was for the 2010 hollywood teen tv awards .
|
{'scope': 'all', 'row': '6', 'col': '4', 'col_other': '1,2,5', 'criterion': 'equal', 'value': 'debby ryan', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'recipient', 'debby ryan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose recipient record fuzzily matches to debby ryan .', 'tostr': 'filter_eq { all_rows ; recipient ; debby ryan }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; recipient ; debby ryan } }', 'tointer': 'select the rows whose recipient record fuzzily matches to debby ryan . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'recipient', 'debby ryan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose recipient record fuzzily matches to debby ryan .', 'tostr': 'filter_eq { all_rows ; recipient ; debby ryan }'}, 'year'], 'result': '2010', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; recipient ; debby ryan } ; year }'}, '2010'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; year } ; 2010 }', 'tointer': 'the year record of this unqiue row is 2010 .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'recipient', 'debby ryan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose recipient record fuzzily matches to debby ryan .', 'tostr': 'filter_eq { all_rows ; recipient ; debby ryan }'}, 'award'], 'result': 'hollywood teen tv awards', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; recipient ; debby ryan } ; award }'}, 'hollywood teen tv awards'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; award } ; hollywood teen tv awards }', 'tointer': 'the award record of this unqiue row is hollywood teen tv awards .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'recipient', 'debby ryan'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose recipient record fuzzily matches to debby ryan .', 'tostr': 'filter_eq { all_rows ; recipient ; debby ryan }'}, 'result'], 'result': 'nominated', 'ind': 6, 'tostr': 'hop { filter_eq { all_rows ; recipient ; debby ryan } ; result }'}, 'nominated'], 'result': True, 'ind': 7, 'tostr': 'eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; result } ; nominated }', 'tointer': 'the result record of this unqiue row is nominated .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; award } ; hollywood teen tv awards } ; eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; result } ; nominated } }', 'tointer': 'the award record of this unqiue row is hollywood teen tv awards . the result record of this unqiue row is nominated .'}], 'result': True, 'ind': 9, 'tostr': 'and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; year } ; 2010 } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; award } ; hollywood teen tv awards } ; eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; result } ; nominated } } }', 'tointer': 'the year record of this unqiue row is 2010 . the award record of this unqiue row is hollywood teen tv awards . the result record of this unqiue row is nominated .'}], 'result': True, 'ind': 10, 'tostr': 'and { only { filter_eq { all_rows ; recipient ; debby ryan } } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; year } ; 2010 } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; award } ; hollywood teen tv awards } ; eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; result } ; nominated } } } } = true', 'tointer': 'select the rows whose recipient record fuzzily matches to debby ryan . there is only one such row in the table . the year record of this unqiue row is 2010 . the award record of this unqiue row is hollywood teen tv awards . the result record of this unqiue row is nominated .'}
|
and { only { filter_eq { all_rows ; recipient ; debby ryan } } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; year } ; 2010 } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; award } ; hollywood teen tv awards } ; eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; result } ; nominated } } } } = true
|
select the rows whose recipient record fuzzily matches to debby ryan . there is only one such row in the table . the year record of this unqiue row is 2010 . the award record of this unqiue row is hollywood teen tv awards . the result record of this unqiue row is nominated .
|
14
|
11
|
{'and_10': 10, 'result_11': 11, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_12': 12, 'recipient_13': 13, 'debby ryan_14': 14, 'and_9': 9, 'eq_3': 3, 'num_hop_2': 2, 'year_15': 15, '2010_16': 16, 'and_8': 8, 'str_eq_5': 5, 'str_hop_4': 4, 'award_17': 17, 'hollywood teen tv awards_18': 18, 'str_eq_7': 7, 'str_hop_6': 6, 'result_19': 19, 'nominated_20': 20}
|
{'and_10': 'and', 'result_11': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_12': 'all_rows', 'recipient_13': 'recipient', 'debby ryan_14': 'debby ryan', 'and_9': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_15': 'year', '2010_16': '2010', 'and_8': 'and', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'award_17': 'award', 'hollywood teen tv awards_18': 'hollywood teen tv awards', 'str_eq_7': 'str_eq', 'str_hop_6': 'str_hop', 'result_19': 'result', 'nominated_20': 'nominated'}
|
{'and_10': [11], 'result_11': [], 'only_1': [10], 'filter_str_eq_0': [1, 2, 4, 6], 'all_rows_12': [0], 'recipient_13': [0], 'debby ryan_14': [0], 'and_9': [10], 'eq_3': [9], 'num_hop_2': [3], 'year_15': [2], '2010_16': [3], 'and_8': [9], 'str_eq_5': [8], 'str_hop_4': [5], 'award_17': [4], 'hollywood teen tv awards_18': [5], 'str_eq_7': [8], 'str_hop_6': [7], 'result_19': [6], 'nominated_20': [7]}
|
['year', 'award', 'category', 'recipient', 'result']
|
[['2010', 'green globe film awards', 'outstanding actors asians in hollywood', 'brenda song', 'nominated'], ['2010', "2010 kids ' choice awards", 'favorite tv show', 'the suite life on deck', 'nominated'], ['2010', "2010 kids ' choice awards", 'favorite tv actor', 'cole sprouse', 'nominated'], ['2010', "2010 kids ' choice awards", 'favorite tv actor', 'dylan sprouse', 'won'], ['2010', 'hollywood teen tv awards', 'teen pick show : comedy', 'the suite life on deck', 'nominated'], ['2010', 'hollywood teen tv awards', 'teen pick actress : comedy', 'debby ryan', 'nominated'], ['2010', 'hollywood teen tv awards', 'teen pick actor : comedy', 'dylan sprouse', 'nominated'], ['2011', "2011 kids ' choice awards", 'favorite tv show', 'the suite life on deck', 'nominated'], ['2011', "2011 kids ' choice awards", 'favorite tv actor', 'cole sprouse', 'nominated'], ['2011', "2011 kids ' choice awards", 'favorite tv actor', 'dylan sprouse', 'won'], ['2011', "2011 kids ' choice awards", 'favorite tv sidekick', 'brenda song', 'nominated'], ['2011', 'casting society of america', "best casting children 's series", 'dana gergely brandi brice', 'won']]
|
breaking bad ( season 3 )
|
https://en.wikipedia.org/wiki/Breaking_Bad_%28season_3%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26736342-1.html.csv
|
comparative
|
the breaking bad season 3 episode " one minute " drew more viewers than the episode " green light " .
|
{'row_1': '7', 'row_2': '4', 'col': '7', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'one minute'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to one minute .', 'tostr': 'filter_eq { all_rows ; title ; one minute }'}, 'us viewers ( millions )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; title ; one minute } ; us viewers ( millions ) }', 'tointer': 'select the rows whose title record fuzzily matches to one minute . take the us viewers ( millions ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'green light'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose title record fuzzily matches to green light .', 'tostr': 'filter_eq { all_rows ; title ; green light }'}, 'us viewers ( millions )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; title ; green light } ; us viewers ( millions ) }', 'tointer': 'select the rows whose title record fuzzily matches to green light . take the us viewers ( millions ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; title ; one minute } ; us viewers ( millions ) } ; hop { filter_eq { all_rows ; title ; green light } ; us viewers ( millions ) } } = true', 'tointer': 'select the rows whose title record fuzzily matches to one minute . take the us viewers ( millions ) record of this row . select the rows whose title record fuzzily matches to green light . take the us viewers ( millions ) record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; title ; one minute } ; us viewers ( millions ) } ; hop { filter_eq { all_rows ; title ; green light } ; us viewers ( millions ) } } = true
|
select the rows whose title record fuzzily matches to one minute . take the us viewers ( millions ) record of this row . select the rows whose title record fuzzily matches to green light . take the us viewers ( millions ) 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, 'title_7': 7, 'one minute_8': 8, 'us viewers (millions)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'title_11': 11, 'green light_12': 12, 'us viewers (millions)_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', 'title_7': 'title', 'one minute_8': 'one minute', 'us viewers (millions)_9': 'us viewers ( millions )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'title_11': 'title', 'green light_12': 'green light', 'us viewers (millions)_13': 'us viewers ( millions )'}
|
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'title_7': [0], 'one minute_8': [0], 'us viewers (millions)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'title_11': [1], 'green light_12': [1], 'us viewers (millions)_13': [3]}
|
['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'us viewers ( millions )']
|
[['21', '1', 'no más', 'bryan cranston', 'vince gilligan', 'march 21 , 2010', '1.95'], ['22', '2', 'caballo sin nombre', 'adam bernstein', 'peter gould', 'march 28 , 2010', '1.55'], ['23', '3', 'ift', 'michelle maclaren', 'george mastras', 'april 4 , 2010', '1.33'], ['24', '4', 'green light', 'scott winant', 'sam catlin', 'april 11 , 2010', '1.46'], ['25', '5', 'más', 'johan renck', 'moira walley - beckett', 'april 18 , 2010', '1.61'], ['26', '6', 'sunset', 'john shiban', 'john shiban', 'april 25 , 2010', '1.64'], ['27', '7', 'one minute', 'michelle maclaren', 'thomas schnauz', 'may 2 , 2010', '1.52'], ['28', '8', 'i see you', 'colin bucksey', 'gennifer hutchison', 'may 9 , 2010', '1.78'], ['29', '9', 'kafkaesque', 'michael slovis', 'peter gould & george mastras', 'may 16 , 2010', '1.61'], ['30', '10', 'fly', 'rian johnson', 'sam catlin & moira walley - beckett', 'may 23 , 2010', '1.20'], ['31', '11', 'abiquiu', 'michelle maclaren', 'john shiban & thomas schnauz', 'may 30 , 2010', '1.32'], ['32', '12', 'half measures', 'adam bernstein', 'sam catlin & peter gould', 'june 6 , 2010', '1.19']]
|
lithuania in the eurovision song contest 2009
|
https://en.wikipedia.org/wiki/Lithuania_in_the_Eurovision_Song_Contest_2009
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18932779-1.html.csv
|
ordinal
|
artists darius pranckevičius and violeta valskytė , who represented lithuania in the eurovision song contest of 2009 , scored the second-highest amount of points .
|
{'row': '7', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
|
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'points', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; points ; 2 }'}, 'artist'], 'result': 'darius pranckevičius and violeta valskytė', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; points ; 2 } ; artist }'}, 'darius pranckevičius and violeta valskytė'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; points ; 2 } ; artist } ; darius pranckevičius and violeta valskytė } = true', 'tointer': 'select the row whose points record of all rows is 2nd maximum . the artist record of this row is darius pranckevičius and violeta valskytė .'}
|
eq { hop { nth_argmax { all_rows ; points ; 2 } ; artist } ; darius pranckevičius and violeta valskytė } = true
|
select the row whose points record of all rows is 2nd maximum . the artist record of this row is darius pranckevičius and violeta valskytė .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, '2_6': 6, 'artist_7': 7, 'darius pranckevičius and violeta valskytė_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', 'points_5': 'points', '2_6': '2', 'artist_7': 'artist', 'darius pranckevičius and violeta valskytė_8': 'darius pranckevičius and violeta valskytė'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], '2_6': [0], 'artist_7': [1], 'darius pranckevičius and violeta valskytė_8': [2]}
|
['draw', 'artist', 'song', 'points', 'place']
|
[['1', 'jonas čepulis and skirmantė', 'uosilėli žaliasai', '49', '7'], ['2', 'alanas', 'geras jausmas', '35', '9'], ['3', 'violeta tarasovienė', 'aš būsiu šalia', '74', '3'], ['4', 'milana', 'ar tu mane matei', '30', '12'], ['5', 'vilius tarasovas', 'aš tik tavim tikiu', '64', '4'], ['6', 'augustė', 'not the best time', '41', '8'], ['7', 'darius pranckevičius and violeta valskytė', 'nelytėta viltis', '78', '2'], ['8', 'kamilė', 'no way to run', '33', '10'], ['9', 'sasha son', 'pasiklydęs žmogus', '92', '1'], ['10', 'vita rusaitytė', 'dar pabūkim drauge', '33', '10'], ['11', '69 danguje', 'meilės simfonija', '62', '5'], ['12', 'egidijus sipavičius', 'per mažai', '56', '6']]
|
spain in the eurovision song contest 2009
|
https://en.wikipedia.org/wiki/Spain_in_the_Eurovision_Song_Contest_2009
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19763199-3.html.csv
|
aggregation
|
the final songs in the spain eurovision song contest of 2009 totaled 29 jury votes .
|
{'scope': 'subset', 'col': '4', 'type': 'sum', 'result': '29', 'subset': {'col': '7', 'criterion': 'equal', 'value': 'final'}}
|
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'final'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; final }', 'tointer': 'select the rows whose result record fuzzily matches to final .'}, 'jury votes'], 'result': '29', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; result ; final } ; jury votes }'}, '29'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; result ; final } ; jury votes } ; 29 } = true', 'tointer': 'select the rows whose result record fuzzily matches to final . the sum of the jury votes record of these rows is 29 .'}
|
round_eq { sum { filter_eq { all_rows ; result ; final } ; jury votes } ; 29 } = true
|
select the rows whose result record fuzzily matches to final . the sum of the jury votes record of these rows is 29 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'final_6': 6, 'jury votes_7': 7, '29_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'final_6': 'final', 'jury votes_7': 'jury votes', '29_8': '29'}
|
{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'final_6': [0], 'jury votes_7': [1], '29_8': [2]}
|
['draw', 'artist', 'song', 'jury votes', 'televotes', 'total votes', 'result']
|
[['1', 'yulia valentayn', 'uh la la', '6', '3', '9', 'out'], ['2', 'la red de san luis', 'gracias , madre tierra', '8', '2', '10', 'out'], ['3', 'vicente casal', 'tú me complementas', '1', '7', '8', 'out'], ['4', 'noelia cano', 'cruza los dedos', '7', '4', '11', 'final'], ['5', 'carlos ferrer eai', 'el patito', '4', '5', '9', 'out'], ['6', 'la la love you', 'dame un beso', '10', '10', '20', 'final'], ['7', 'normativa vigente', 'alejandría - the new generation', '3', '6', '9', 'out'], ['8', 'atalis', 'retrato frontal', '5', '1', '6', 'out'], ['9', 'melody y los vivancos', 'amante de la luna', '12', '12', '24', 'final']]
|
alexia dechaume - balleret
|
https://en.wikipedia.org/wiki/Alexia_Dechaume-Balleret
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16570128-5.html.csv
|
count
|
alexia dechaume was the runner-up in five of the listed matches .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'runner-up', 'result': '5', 'col': '1', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'outcome', 'runner-up'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose outcome record fuzzily matches to runner-up .', 'tostr': 'filter_eq { all_rows ; outcome ; runner-up }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; outcome ; runner-up } }', 'tointer': 'select the rows whose outcome record fuzzily matches to runner-up . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; outcome ; runner-up } } ; 5 } = true', 'tointer': 'select the rows whose outcome record fuzzily matches to runner-up . the number of such rows is 5 .'}
|
eq { count { filter_eq { all_rows ; outcome ; runner-up } } ; 5 } = true
|
select the rows whose outcome record fuzzily matches to runner-up . the number of such rows is 5 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'outcome_5': 5, 'runner-up_6': 6, '5_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'outcome_5': 'outcome', 'runner-up_6': 'runner-up', '5_7': '5'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'outcome_5': [0], 'runner-up_6': [0], '5_7': [2]}
|
['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents', 'score']
|
[['winner', '25 september 1988', 'paris , france', 'clay', 'emmanuelle derly', 'louise field nathalie herreman', '6 - 0 , 6 - 2'], ['runner - up', '23 september 1990', 'paris , france', 'clay', 'nathalie herreman', 'kristin godridge kirrily sharpe', '6 - 4 , 3 - 6 , 1 - 6'], ['winner', '5 may 1991', 'taranto , italy', 'clay', 'florencia labat', 'laura golarsa ann grossman', '6 - 2 , 7 - 5'], ['runner - up', '22 september 1991', 'paris , france', 'clay', 'julie halard', 'petra langrová radomira zrubáková', '4 - 6 , 4 - 6'], ['winner', '12 july 1992', 'kitzbühel , austria', 'clay', 'florencia labat', 'amanda coetzer wiltrud probst', '6 - 3 , 6 - 3'], ['winner', '26 july 1992', 'san marino', 'clay', 'florencia labat', 'sandra cecchini laura garrone', '7 - 6 , 7 - 5'], ['winner', '30 august 1992', 'schenectady , new york , usa', 'hard', 'florencia labat', 'ginger helgeson shannan mccarthy', '6 - 3 , 1 - 6 , 6 - 2'], ['runner - up', '6 august 1995', 'san diego , california , usa', 'hard', 'sandrine testud', 'gigi fernández natalia zvereva', '2 - 6 , 1 - 6'], ['runner - up', '5 may 1996', 'bol , croatia', 'clay', 'alexandra fusai', 'laura montalvo paola suárez', '7 - 6 , 3 - 6 , 4 - 6'], ['winner', '20 april 1997', 'tokyo , japan', 'hard', 'rika hiraki', 'kerry - anne guse corina morariu', '6 - 4 , 6 - 2'], ['runner - up', '16 january 1999', 'hobart , australia', 'hard', 'émilie loit', 'mariaan de swardt elena tatarkova', '1 - 6 , 2 - 6']]
|
1905 in brazilian football
|
https://en.wikipedia.org/wiki/1905_in_Brazilian_football
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15421748-1.html.csv
|
unique
|
out of the active brazilian football teams of 1905 , paulistano was the only team to not lose a game .
|
{'scope': 'all', 'row': '1', 'col': '6', 'col_other': '2', 'criterion': 'equal', 'value': '0', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'lost', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose lost record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; lost ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; lost ; 0 } }', 'tointer': 'select the rows whose lost record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'lost', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose lost record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; lost ; 0 }'}, 'team'], 'result': 'paulistano', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; lost ; 0 } ; team }'}, 'paulistano'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; paulistano }', 'tointer': 'the team record of this unqiue row is paulistano .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; paulistano } } = true', 'tointer': 'select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is paulistano .'}
|
and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; paulistano } } = true
|
select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is paulistano .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'lost_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'paulistano_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'lost_7': 'lost', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'paulistano_10': 'paulistano'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'lost_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'paulistano_10': [3]}
|
['position', 'team', 'points', 'played', 'drawn', 'lost', 'against', 'difference']
|
[['1', 'paulistano', '18', '10', '2', '0', '3', '17'], ['2', 'germnia', '13', '10', '3', '2', '16', '14'], ['3', 'sc internacional de são paulo', '11', '10', '3', '3', '19', '- 4'], ['4', 'são paulo athletic', '8', '10', '0', '6', '26', '- 10'], ['5', 'mackenzie', '7', '10', '1', '6', '27', '0'], ['6', 'aa das palmeiras', '3', '10', '1', '8', '27', '- 17']]
|
mister international
|
https://en.wikipedia.org/wiki/Mister_International
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20325360-2.html.csv
|
count
|
three of the countries in the mister international were semifinalists 1 time .
|
{'scope': 'all', 'criterion': 'equal', 'value': '1', 'result': '3', 'col': '8', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'semifinalists', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose semifinalists record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; semifinalists ; 1 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; semifinalists ; 1 } }', 'tointer': 'select the rows whose semifinalists record is equal to 1 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; semifinalists ; 1 } } ; 3 } = true', 'tointer': 'select the rows whose semifinalists record is equal to 1 . the number of such rows is 3 .'}
|
eq { count { filter_eq { all_rows ; semifinalists ; 1 } } ; 3 } = true
|
select the rows whose semifinalists record is equal to 1 . the number of such rows is 3 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'semifinalists_5': 5, '1_6': 6, '3_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'semifinalists_5': 'semifinalists', '1_6': '1', '3_7': '3'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'semifinalists_5': [0], '1_6': [0], '3_7': [2]}
|
['rank', 'country / territory', 'mister international', '1st runner - up', '2nd runner - up', '3rd runner - up', '4th runner - up', 'semifinalists', 'total']
|
[['1', 'lebanon', '2', '1', '1', '0', '1', '0', '5'], ['2', 'brazil', '2', '1', '0', '1', '0', '1', '5'], ['3', 'vietnam', '1', '0', '0', '1', '0', '1', '3'], ['4', 'bolivia', '1', '0', '0', '0', '0', '1', '2'], ['5', 'great britain', '1', '0', '0', '0', '0', '0', '1'], ['6', 'spain', '0', '1', '1', '0', '0', '0', '2'], ['7', 'venezuela', '0', '1', '0', '1', '0', '5', '7'], ['8', 'singapore', '0', '1', '0', '0', '0', '4', '5'], ['9', 'south korea', '0', '1', '0', '0', '0', '2', '3']]
|
british rail classes 253 , 254 and 255
|
https://en.wikipedia.org/wiki/British_Rail_Classes_253%2C_254_and_255
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1131463-1.html.csv
|
aggregation
|
considering the british rail classes 253 , 254 and 255 , the average number of cars per set was around 9.5 .
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '9.5', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'cars per set'], 'result': '9.5', 'ind': 0, 'tostr': 'avg { all_rows ; cars per set }'}, '9.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; cars per set } ; 9.5 } = true', 'tointer': 'the average of the cars per set record of all rows is 9.5 .'}
|
round_eq { avg { all_rows ; cars per set } ; 9.5 } = true
|
the average of the cars per set record of all rows is 9.5 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'cars per set_4': 4, '9.5_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'cars per set_4': 'cars per set', '9.5_5': '9.5'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'cars per set_4': [0], '9.5_5': [1]}
|
['class', 'operator', 'number', 'year built', 'cars per set', 'unit numbers']
|
[['class 253', 'br western region', '27', '1975 - 1977', '9', '253001 - 253027'], ['class 253', 'br western region', '13', '1978 - 1979', '9', '253028 - 253040'], ['class 253', 'br cross country', '18', '1981 - 1982', '9', '253041 - 253058'], ['class 254', 'br eastern region br scottish region', '32', '1977 - 1979', '10', '254001 - 254032'], ['class 254', 'br eastern region br scottish region', '4', '1982', '10', '254033 - 254037']]
|
juan garriga
|
https://en.wikipedia.org/wiki/Juan_Garriga
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14820149-3.html.csv
|
aggregation
|
juan garriga 's average points across all events he participated in was around 68.7 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '68.7', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '68.7', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '68.7'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 68.7 } = true', 'tointer': 'the average of the points record of all rows is 68.7 .'}
|
round_eq { avg { all_rows ; points } ; 68.7 } = true
|
the average of the points record of all rows is 68.7 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '68.7_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '68.7_5': '68.7'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '68.7_5': [1]}
|
['year', 'class', 'team', 'points', 'wins']
|
[['1984', '250cc', 'yamaha', '0', '0'], ['1985', '250cc', 'jj cobas', '8', '0'], ['1986', '500cc', 'cagiva', '4', '0'], ['1987', '250cc', 'ducados - yamaha', '46', '0'], ['1988', '250cc', 'ducados - yamaha', '221', '3'], ['1989', '250cc', 'ducados - yamaha', '98', '0'], ['1990', '500cc', 'ducados - yamaha', '121', '0'], ['1991', '500cc', 'ducados - yamaha', '121', '0'], ['1992', '500cc', 'ducados - yamaha', '61', '0'], ['1993', '500cc', 'cagiva', '7', '0']]
|
1982 denver broncos season
|
https://en.wikipedia.org/wiki/1982_Denver_Broncos_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17928444-1.html.csv
|
aggregation
|
the average crowd attendance of games in the 1982 denver broncos season was 61409 .
|
{'scope': 'all', 'col': '7', 'type': 'average', 'result': '61409', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '61409', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '61409'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 61409 } = true', 'tointer': 'the average of the attendance record of all rows is 61409 .'}
|
round_eq { avg { all_rows ; attendance } ; 61409 } = true
|
the average of the attendance record of all rows is 61409 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '61409_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '61409_5': '61409'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '61409_5': [1]}
|
['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance']
|
[['1', 'september 12', 'san diego chargers', 'l 3 - 23', 'mile high stadium', '0 - 1', '73564'], ['2', 'september 19', 'san francisco 49ers', 'w 24 - 21', 'mile high stadium', '1 - 1', '73899'], ['10', 'november 21', 'seattle seahawks', 'l 10 - 17', 'mile high stadium', '1 - 2', '73996'], ['11', 'november 28', 'san diego chargers', 'l 20 - 30', 'jack murphy stadium', '1 - 3', '47629'], ['12', 'december 5', 'atlanta falcons', 'l 27 - 34', 'mile high stadium', '1 - 4', '73984'], ['13', 'december 12', 'los angeles rams', 'w 27 - 24', 'anaheim stadium', '2 - 4', '48112'], ['14', 'december 19', 'kansas city chiefs', 'l 16 - 37', 'mile high stadium', '2 - 5', '74192'], ['15', 'december 26', 'los angeles raiders', 'l 10 - 27', 'los angeles memorial coliseum', '2 - 6', '44160'], ['16', 'january 2', 'seattle seahawks', 'l 11 - 13', 'kingdome', '2 - 7', '43145']]
|
alpert awards in the arts
|
https://en.wikipedia.org/wiki/Alpert_Awards_in_the_Arts
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10996831-1.html.csv
|
unique
|
the year 2013 was the only year where sharon hayes was in the visual arts category .
|
{'scope': 'all', 'row': '19', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'sharon hayes', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visual arts', 'sharon hayes'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose visual arts record fuzzily matches to sharon hayes .', 'tostr': 'filter_eq { all_rows ; visual arts ; sharon hayes }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; visual arts ; sharon hayes } }', 'tointer': 'select the rows whose visual arts record fuzzily matches to sharon hayes . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'visual arts', 'sharon hayes'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose visual arts record fuzzily matches to sharon hayes .', 'tostr': 'filter_eq { all_rows ; visual arts ; sharon hayes }'}, 'year'], 'result': '2013', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; visual arts ; sharon hayes } ; year }'}, '2013'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; visual arts ; sharon hayes } ; year } ; 2013 }', 'tointer': 'the year record of this unqiue row is 2013 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; visual arts ; sharon hayes } } ; eq { hop { filter_eq { all_rows ; visual arts ; sharon hayes } ; year } ; 2013 } } = true', 'tointer': 'select the rows whose visual arts record fuzzily matches to sharon hayes . there is only one such row in the table . the year record of this unqiue row is 2013 .'}
|
and { only { filter_eq { all_rows ; visual arts ; sharon hayes } } ; eq { hop { filter_eq { all_rows ; visual arts ; sharon hayes } ; year } ; 2013 } } = true
|
select the rows whose visual arts record fuzzily matches to sharon hayes . there is only one such row in the table . the year record of this unqiue row is 2013 .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'visual arts_7': 7, 'sharon hayes_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '2013_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'visual arts_7': 'visual arts', 'sharon hayes_8': 'sharon hayes', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '2013_10': '2013'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'visual arts_7': [0], 'sharon hayes_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '2013_10': [3]}
|
['year', 'film / video', 'visual arts', 'theatre', 'dance', 'music']
|
[['1995', 'leslie thornton', 'mel chin', 'reza abdoh', 'ann carlson', 'james carter'], ['1996', 'su friedrich', 'carrie mae weems', 'suzan - lori parks', 'david rousseve', 'anne lebaron'], ['1997', 'craig baldwin', 'kerry james marshall', 'lisa kron', 'victoria marks', 'chen yi'], ['1998', 'jeanne c finley', 'roni horn', 'danny hoch', 'joanna haigood', 'pamela z'], ['1999', 'lourdes portillo', 'pepon osorio', 'brian freeman', 'ralph lemon', 'george lewis'], ['2000', 'peggy ahwesh', 'shirin neshat', 'w david hancock', 'mark dendy', 'steve coleman'], ['2001', 'ellen bruno', 'cai guo - qiang', 'erik ehn', 'john kelly', 'zhou long'], ['2002', '® tm', 'christian marclay', 'david greenspan', 'lisa nelson', 'laetitia sonami'], ['2003', 'coco fusco', 'catherine opie', 'carl hancock rux', 'rennie harris', 'vijay iyer'], ['2004', 'renee tajima - peña', 'catherine sullivan', 'dan hurlin', 'stephan koplowitz', 'miya masaoka'], ['2005', 'jem cohen', 'harrell fletcher', 'naomi iizuka', 'donna uchizono', 'david dunn'], ['2006', 'bill morrison', 'jim hodges', 'daniel alexander jones', 'sarah michelson', 'lawrence d morris'], ['2007', 'jacqueline goss', 'walid raad', 'cynthia hopkins', 'jeanine durning', 'mark feldman'], ['2008', 'bruce mcclure', 'bryon kim', "lisa d'amour", 'pat graney', 'derek bermel'], ['2009', 'paul chan', 'paul pfeiffer', 'rinde eckert', 'reggie wilson', 'john king'], ['2010', 'jim trainor', 'rachel harrison', 'bill talen', 'susan rethorst', 'lukas ligeti'], ['2011', 'natalia almada', 'emily jacir', 'marc bamuthi joseph', 'jess curtis', 'nicole mitchell'], ['2012', 'kevin everson', 'michael smith', 'eisa davis', 'nora chipaumire', 'myra melford'], ['2013', 'lucien castaing - taylor', 'sharon hayes', 'pavol liska & kelly copper', 'julia rhoads', 'alex mincek']]
|
abc amsterdam
|
https://en.wikipedia.org/wiki/ABC_Amsterdam
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14189125-1.html.csv
|
majority
|
when abc amsterdam was a finalist in the dutch cup , most of the time they were the champion in the postseason .
|
{'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'champion', 'subset': {'col': '6', 'criterion': 'equal', 'value': 'finalist'}}
|
{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'dutch cup', 'finalist'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; dutch cup ; finalist }', 'tointer': 'select the rows whose dutch cup record fuzzily matches to finalist .'}, 'postseason', 'champion'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose dutch cup record fuzzily matches to finalist . for the postseason records of these rows , most of them fuzzily match to champion .', 'tostr': 'most_eq { filter_eq { all_rows ; dutch cup ; finalist } ; postseason ; champion } = true'}
|
most_eq { filter_eq { all_rows ; dutch cup ; finalist } ; postseason ; champion } = true
|
select the rows whose dutch cup record fuzzily matches to finalist . for the postseason records of these rows , most of them fuzzily match to champion .
|
2
|
2
|
{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'dutch cup_4': 4, 'finalist_5': 5, 'postseason_6': 6, 'champion_7': 7}
|
{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'dutch cup_4': 'dutch cup', 'finalist_5': 'finalist', 'postseason_6': 'postseason', 'champion_7': 'champion'}
|
{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'dutch cup_4': [0], 'finalist_5': [0], 'postseason_6': [1], 'champion_7': [1]}
|
['season', 'tier', 'league', 'pos', 'postseason', 'dutch cup', 'european competitions']
|
[['2004 - 05', '1', 'dbl', '3', 'champion', '-', 'europe league 3 : last 16'], ['2005 - 06', '1', 'dbl', '2', 'semifinalist', 'winner', 'uleb cup 2 : group stage'], ['2006 - 07', '1', 'dbl', '6', 'semifinalist', '-', 'eurocup 3 : group stage'], ['2007 - 08', '1', 'dbl', '1', 'champion', 'finalist', '-'], ['2008 - 09', '1', 'dbl', '1', 'champion', 'finalist', 'eurochallenge 3 : quarterfinalist'], ['2009 - 10', '1', 'dbl', '7', 'quarterfinalist', 'finalist', 'eurochallenge 3 : group stage'], ['2010 - 11', '1', 'dbl', '8', 'quarterfinalist', '-', '-']]
|
spain men 's national water polo team
|
https://en.wikipedia.org/wiki/Spain_men%27s_national_water_polo_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18985137-1.html.csv
|
majority
|
most of the players on the team play the cf position .
|
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'cf', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'pos', 'cf'], 'result': True, 'ind': 0, 'tointer': 'for the pos records of all rows , most of them fuzzily match to cf .', 'tostr': 'most_eq { all_rows ; pos ; cf } = true'}
|
most_eq { all_rows ; pos ; cf } = true
|
for the pos records of all rows , most of them fuzzily match to cf .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'pos_3': 3, 'cf_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'pos_3': 'pos', 'cf_4': 'cf'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'pos_3': [0], 'cf_4': [0]}
|
['name', 'pos', 'height', 'weight', '2012 club']
|
[['iñaki aguilar', 'gk', 'm', '-', 'cn sabadell'], ['mario josé garcía', 'd', 'm', '-', 'real canoe'], ['david martín', 'd', 'm', '-', 'cn atlètic - barceloneta'], ['balázs szirányi', 'cf', 'm', '-', 'real canoe'], ['guillermo molina', 'cf', 'm', '-', 'pro recco'], ['marc minguell', 'cf', 'm', '-', 'posillipo'], ['blai mallarach', 'cf', 'm', '-', 'havk mladost'], ['albert español', 'd', 'm', '-', 'cn atlètic - barceloneta'], ['xavier vallès', 'cf', 'm', '-', 'cn atlètic - barceloneta'], ['felipe perrone', 'd', 'm', '-', 'pro recco'], ['iván pérez', 'cf', 'm', '-', 'cn sabadell'], ['xavier garcía', 'cf', 'm', '-', 'vk primorje rijeka'], ['daniel lópez', 'gk', 'm', '-', 'cn atlètic - barceloneta']]
|
english premier ice hockey league
|
https://en.wikipedia.org/wiki/English_Premier_Ice_Hockey_League
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2384331-1.html.csv
|
aggregation
|
on average , most teams joined the english premier ice hockey league in 2005 .
|
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '2005', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'joined'], 'result': '2005', 'ind': 0, 'tostr': 'avg { all_rows ; joined }'}, '2005'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; joined } ; 2005 } = true', 'tointer': 'the average of the joined record of all rows is 2005 .'}
|
round_eq { avg { all_rows ; joined } ; 2005 } = true
|
the average of the joined record of all rows is 2005 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'joined_4': 4, '2005_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'joined_4': 'joined', '2005_5': '2005'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'joined_4': [0], '2005_5': [1]}
|
['team', 'city / area', 'arena', 'founded', 'joined', 'head coach', 'captain']
|
[['basingstoke bison', 'basingstoke , hampshire', 'planet ice silverdome arena', '1988', '2009', 'doug sheppard ( p / c )', 'tony redmond'], ['bracknell bees', 'bracknell , berkshire', 'john nike leisuresport complex', '1987', '2005', 'gareth cox', 'rob lamey'], ['guildford flames', 'guildford , surrey', 'guildford spectrum', '1992', '2005', 'paul dixon ( p / c )', 'david longstaff'], ['manchester phoenix', 'manchester , greater manchester', 'altrincham ice dome', '2003', '2009', 'tony hand', 'luke boothroyd'], ['milton keynes lightning', 'milton keynes , buckinghamshire', 'planet ice milton keynes', '2002', '2002', 'nick poole ( p / c )', 'adam carr'], ['peterborough phantoms', 'peterborough , cambridgeshire', 'planet ice peterborough', '2002', '2002', 'jon kynaston', 'jeff glowa'], ['sheffield steeldogs', 'sheffield , south yorkshire', 'icesheffield', '2010', '2010', 'andre payette ( p / c )', 'greg wood'], ['slough jets', 'slough , berkshire', 'absolute ice', '1986', '2005', 'slava koulikov ( p / c )', 'michael wales'], ['swindon wildcats', 'swindon , wiltshire', 'link centre', '1986', '2005', 'ryan aldridge', 'aaron nell']]
|
2001 new england patriots season
|
https://en.wikipedia.org/wiki/2001_New_England_Patriots_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10716061-1.html.csv
|
count
|
two of the players selected in the draft were from notre dame university .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'notre dame', 'result': '2', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'notre dame'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to notre dame .', 'tostr': 'filter_eq { all_rows ; college ; notre dame }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; college ; notre dame } }', 'tointer': 'select the rows whose college record fuzzily matches to notre dame . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; college ; notre dame } } ; 2 } = true', 'tointer': 'select the rows whose college record fuzzily matches to notre dame . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; college ; notre dame } } ; 2 } = true
|
select the rows whose college record fuzzily matches to notre dame . 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, 'college_5': 5, 'notre dame_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', 'college_5': 'college', 'notre dame_6': 'notre dame', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'college_5': [0], 'notre dame_6': [0], '2_7': [2]}
|
['round', 'overall', 'player', 'position', 'college']
|
[['1', '6', 'richard seymour', 'defensive tackle', 'georgia'], ['2', '48', 'matt light', 'offensive tackle', 'purdue'], ['3', '86', 'brock williams', 'cornerback', 'notre dame'], ['4', '96', 'kenyatta jones', 'offensive tackle', 'south florida'], ['4', '119', 'jabari holloway', 'tight end', 'notre dame'], ['5', '163', 'hakim akbar', 'safety', 'washington'], ['6', '180', 'arther love', 'tight end', 'south carolina state'], ['6', '200', 'leonard myers', 'cornerback', 'miami ( fl )'], ['7', '216', 'owen pochman', 'kicker', 'byu'], ['7', '239', 't j turner', 'linebacker', 'michigan state']]
|
trevor taylor
|
https://en.wikipedia.org/wiki/Trevor_Taylor
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226576-1.html.csv
|
comparative
|
trevor taylor scored the same number of points in both his 1962 races for team lotus .
|
{'row_1': '3', 'row_2': '4', 'col': '5', 'col_other': '2', 'relation': 'equal', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'entrant', 'team lotus'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose entrant record fuzzily matches to team lotus .', 'tostr': 'filter_eq { all_rows ; entrant ; team lotus }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; entrant ; team lotus } ; points }', 'tointer': 'select the rows whose entrant record fuzzily matches to team lotus . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'entrant', 'team lotus'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose entrant record fuzzily matches to team lotus .', 'tostr': 'filter_eq { all_rows ; entrant ; team lotus }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; entrant ; team lotus } ; points }', 'tointer': 'select the rows whose entrant record fuzzily matches to team lotus . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; entrant ; team lotus } ; points } ; hop { filter_eq { all_rows ; entrant ; team lotus } ; points } } = true', 'tointer': 'select the rows whose entrant record fuzzily matches to team lotus . take the points record of this row . select the rows whose entrant record fuzzily matches to team lotus . take the points record of this row . the first record is equal to the second record .'}
|
eq { hop { filter_eq { all_rows ; entrant ; team lotus } ; points } ; hop { filter_eq { all_rows ; entrant ; team lotus } ; points } } = true
|
select the rows whose entrant record fuzzily matches to team lotus . take the points record of this row . select the rows whose entrant record fuzzily matches to team lotus . take the points record of this row . the first record is equal to the second record .
|
5
|
5
|
{'eq_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'entrant_7': 7, 'team lotus_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'entrant_11': 11, 'team lotus_12': 12, 'points_13': 13}
|
{'eq_4': 'eq', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'entrant_7': 'entrant', 'team lotus_8': 'team lotus', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'entrant_11': 'entrant', 'team lotus_12': 'team lotus', 'points_13': 'points'}
|
{'eq_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'entrant_7': [0], 'team lotus_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'entrant_11': [1], 'team lotus_12': [1], 'points_13': [3]}
|
['year', 'entrant', 'chassis', 'engine', 'points']
|
[['1959', 'ace garage ( rotherham )', 'cooper t51', 'climax straight - 4', '0'], ['1961', 'team lotus', 'lotus 18', 'climax straight - 4', '0'], ['1962', 'team lotus', 'lotus 24', 'climax v8', '6'], ['1962', 'team lotus', 'lotus 25', 'climax v8', '6'], ['1963', 'team lotus', 'lotus 25', 'climax v8', '1'], ['1964', 'british racing partnership', 'brp 1', 'brm v8', '1'], ['1964', 'british racing partnership', 'brp 2', 'brm v8', '1'], ['1964', 'british racing partnership', 'lotus 24', 'brm v8', '1'], ['1966', 'aiden jones / paul emery', 'shannon', 'climax v8', '0']]
|
factor x
|
https://en.wikipedia.org/wiki/Factor_X
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1555308-1.html.csv
|
unique
|
a c1inh deficiency is the only condition associated with a shortened partial thromboplastin time .
|
{'scope': 'all', 'row': '16', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'shortened', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partial thromboplastin time', 'shortened'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partial thromboplastin time record fuzzily matches to shortened .', 'tostr': 'filter_eq { all_rows ; partial thromboplastin time ; shortened }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; partial thromboplastin time ; shortened } }', 'tointer': 'select the rows whose partial thromboplastin time record fuzzily matches to shortened . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'partial thromboplastin time', 'shortened'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose partial thromboplastin time record fuzzily matches to shortened .', 'tostr': 'filter_eq { all_rows ; partial thromboplastin time ; shortened }'}, 'condition'], 'result': 'c1inh deficiency', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; partial thromboplastin time ; shortened } ; condition }'}, 'c1inh deficiency'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; partial thromboplastin time ; shortened } ; condition } ; c1inh deficiency }', 'tointer': 'the condition record of this unqiue row is c1inh deficiency .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; partial thromboplastin time ; shortened } } ; eq { hop { filter_eq { all_rows ; partial thromboplastin time ; shortened } ; condition } ; c1inh deficiency } } = true', 'tointer': 'select the rows whose partial thromboplastin time record fuzzily matches to shortened . there is only one such row in the table . the condition record of this unqiue row is c1inh deficiency .'}
|
and { only { filter_eq { all_rows ; partial thromboplastin time ; shortened } } ; eq { hop { filter_eq { all_rows ; partial thromboplastin time ; shortened } ; condition } ; c1inh deficiency } } = true
|
select the rows whose partial thromboplastin time record fuzzily matches to shortened . there is only one such row in the table . the condition record of this unqiue row is c1inh deficiency .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'partial thromboplastin time_7': 7, 'shortened_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'condition_9': 9, 'c1inh deficiency_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'partial thromboplastin time_7': 'partial thromboplastin time', 'shortened_8': 'shortened', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'condition_9': 'condition', 'c1inh deficiency_10': 'c1inh deficiency'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'partial thromboplastin time_7': [0], 'shortened_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'condition_9': [2], 'c1inh deficiency_10': [3]}
|
['condition', 'prothrombin time', 'partial thromboplastin time', 'bleeding time', 'platelet count']
|
[['vitamin k deficiency or warfarin', 'prolonged', 'normal or mildly prolonged', 'unaffected', 'unaffected'], ['disseminated intravascular coagulation', 'prolonged', 'prolonged', 'prolonged', 'decreased'], ['von willebrand disease', 'unaffected', 'prolonged or unaffected', 'prolonged', 'unaffected'], ['hemophilia', 'unaffected', 'prolonged', 'unaffected', 'unaffected'], ['aspirin', 'unaffected', 'unaffected', 'prolonged', 'unaffected'], ['thrombocytopenia', 'unaffected', 'unaffected', 'prolonged', 'decreased'], ['liver failure , early', 'prolonged', 'unaffected', 'unaffected', 'unaffected'], ['liver failure , end - stage', 'prolonged', 'prolonged', 'prolonged', 'decreased'], ['uremia', 'unaffected', 'unaffected', 'prolonged', 'unaffected'], ['congenital afibrinogenemia', 'prolonged', 'prolonged', 'prolonged', 'unaffected'], ['factor v deficiency', 'prolonged', 'prolonged', 'unaffected', 'unaffected'], ['factor x deficiency as seen in amyloid purpura', 'prolonged', 'prolonged', 'unaffected', 'unaffected'], ["glanzmann 's thrombasthenia", 'unaffected', 'unaffected', 'prolonged', 'unaffected'], ['bernard - soulier syndrome', 'unaffected', 'unaffected', 'prolonged', 'decreased or unaffected'], ['factor xii deficiency', 'unaffected', 'prolonged', 'unaffected', 'unaffected'], ['c1inh deficiency', 'unaffected', 'shortened', 'unaffected', 'unaffected']]
|
2005 cfl draft
|
https://en.wikipedia.org/wiki/2005_CFL_Draft
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10960039-6.html.csv
|
unique
|
in the 2005 cfl draft , the only player drafted from acadia was karl ortmanns .
|
{'scope': 'all', 'row': '8', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'acadia', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'acadia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to acadia .', 'tostr': 'filter_eq { all_rows ; college ; acadia }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; acadia } }', 'tointer': 'select the rows whose college record fuzzily matches to acadia . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'acadia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to acadia .', 'tostr': 'filter_eq { all_rows ; college ; acadia }'}, 'player'], 'result': 'karl ortmanns', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; acadia } ; player }'}, 'karl ortmanns'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; acadia } ; player } ; karl ortmanns }', 'tointer': 'the player record of this unqiue row is karl ortmanns .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; acadia } } ; eq { hop { filter_eq { all_rows ; college ; acadia } ; player } ; karl ortmanns } } = true', 'tointer': 'select the rows whose college record fuzzily matches to acadia . there is only one such row in the table . the player record of this unqiue row is karl ortmanns .'}
|
and { only { filter_eq { all_rows ; college ; acadia } } ; eq { hop { filter_eq { all_rows ; college ; acadia } ; player } ; karl ortmanns } } = true
|
select the rows whose college record fuzzily matches to acadia . there is only one such row in the table . the player record of this unqiue row is karl ortmanns .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'acadia_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'karl ortmanns_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'college_7': 'college', 'acadia_8': 'acadia', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'karl ortmanns_10': 'karl ortmanns'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'acadia_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'karl ortmanns_10': [3]}
|
['pick', 'cfl team', 'player', 'position', 'college']
|
[['45', 'calgary stampeders', 'brett ralph', 'wr', 'alberta'], ['46', 'ottawa renegades', 'lenard semajuste', 'fb', 'adams state'], ['47', 'winnipeg blue bombers', 'ryan bisson', 'ol', 'northwood'], ['48', 'saskatchewan roughriders', 'ryan gottselig', 'dl', 'saskatchewan'], ['49', 'montreal alouettes ( via edmonton )', 'adam eckert', 'wr', 'dickinson state'], ['50', 'hamilton tiger - cats', 'andrew paopao', 'dl', 'san jose state'], ['51', 'montreal alouettes', 'olivier manigat', 'ol', 'columbia'], ['52', 'bc lions', 'karl ortmanns', 'ol', 'acadia']]
|
fiba eurobasket 2007 squads
|
https://en.wikipedia.org/wiki/FIBA_EuroBasket_2007_squads
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12962773-1.html.csv
|
count
|
there is a total of 5 eurobasket players listed on the panathinaikos club .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'panathinaikos', 'result': '5', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'current club', 'panathinaikos'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose current club record fuzzily matches to panathinaikos .', 'tostr': 'filter_eq { all_rows ; current club ; panathinaikos }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; current club ; panathinaikos } }', 'tointer': 'select the rows whose current club record fuzzily matches to panathinaikos . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; current club ; panathinaikos } } ; 5 } = true', 'tointer': 'select the rows whose current club record fuzzily matches to panathinaikos . the number of such rows is 5 .'}
|
eq { count { filter_eq { all_rows ; current club ; panathinaikos } } ; 5 } = true
|
select the rows whose current club record fuzzily matches to panathinaikos . the number of such rows is 5 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'current club_5': 5, 'panathinaikos_6': 6, '5_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'current club_5': 'current club', 'panathinaikos_6': 'panathinaikos', '5_7': '5'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'current club_5': [0], 'panathinaikos_6': [0], '5_7': [2]}
|
['player', 'height', 'position', 'year born', 'current club']
|
[['theodoros papaloukas', '2.00', 'guard', '1977', 'cska moscow'], ['ioannis bourousis', '2.13', 'center', '1983', 'olympiacos'], ['nikolaos zisis', '1.95', 'guard', '1983', 'cska moscow'], ['vasileios spanoulis', '1.92', 'guard', '1982', 'panathinaikos'], ['panagiotis vasilopoulos', '2.01', 'forward', '1984', 'olympiacos'], ['michalis pelekanos', '1.98', 'forward', '1981', 'real madrid'], ['nikolaos chatzivrettas', '1.95', 'guard', '1977', 'panathinaikos'], ['dimosthenis dikoudis', '2.06', 'forward', '1977', 'panathinaikos'], ['konstantinos tsartsaris', '2.09', 'center', '1979', 'panathinaikos'], ['dimitris diamantidis', '1.96', 'guard', '1980', 'panathinaikos'], ['lazaros papadopoulos', '2.10', 'center', '1980', 'real madrid'], ['michail kakiouzis', '2.07', 'forward', '1976', 'cb sevilla']]
|
eurovision song contest 1965
|
https://en.wikipedia.org/wiki/Eurovision_Song_Contest_1965
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-184806-1.html.csv
|
majority
|
in the 1965 eurovision song contest the majority of songs gained less than 10 points .
|
{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '10', 'subset': None}
|
{'func': 'most_less', 'args': ['all_rows', 'points', '10'], 'result': True, 'ind': 0, 'tointer': 'for the points records of all rows , most of them are less than 10 .', 'tostr': 'most_less { all_rows ; points ; 10 } = true'}
|
most_less { all_rows ; points ; 10 } = true
|
for the points records of all rows , most of them are less than 10 .
|
1
|
1
|
{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'points_3': 3, '10_4': 4}
|
{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'points_3': 'points', '10_4': '10'}
|
{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'points_3': [0], '10_4': [0]}
|
['draw', 'language', 'artist', 'place', 'points']
|
[['01', 'dutch', 'conny van den bos', '11', '5'], ['02', 'english', 'kathy kirby', '2', '26'], ['03', 'spanish', 'conchita bautista', '15', '0'], ['04', 'english', 'butch moore', '6', '11'], ['05', 'german', 'ulla wiesner', '15', '0'], ['06', 'german', 'udo jürgens', '4', '16'], ['07', 'norwegian', 'kirsti sparboe', '13', '1'], ['08', 'dutch', 'lize marke', '15', '0'], ['09', 'french', 'marjorie noël', '9', '7'], ['10', 'english', 'ingvar wixell', '10', '6'], ['11', 'french', 'guy mardel', '3', '22'], ['12', 'portuguese', 'simone de oliveira', '13', '1'], ['13', 'italian', 'bobby solo', '5', '15'], ['14', 'danish', 'birgit brüel', '7', '10'], ['15', 'french', 'france gall', '1', '32'], ['16', 'finnish', 'viktor klimenko', '15', '0'], ['17', 'croatian', 'vice vukov', '12', '2'], ['18', 'french', 'yovanna', '8', '8']]
|
kuwait at the 2008 summer paralympics
|
https://en.wikipedia.org/wiki/Kuwait_at_the_2008_Summer_Paralympics
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19398910-4.html.csv
|
unique
|
tariq alqallaf is the only athlete who reached quarterfinals .
|
{'scope': 'all', 'row': '3', 'col': '12', 'col_other': '1', 'criterion': 'not_equal', 'value': 'did not advance', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'quarterfinals', 'did not advance'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose quarterfinals record does not match to did not advance .', 'tostr': 'filter_not_eq { all_rows ; quarterfinals ; did not advance }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; quarterfinals ; did not advance } }', 'tointer': 'select the rows whose quarterfinals record does not match to did not advance . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_not_eq', 'args': ['all_rows', 'quarterfinals', 'did not advance'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose quarterfinals record does not match to did not advance .', 'tostr': 'filter_not_eq { all_rows ; quarterfinals ; did not advance }'}, 'athlete'], 'result': 'tariq alqallaf', 'ind': 2, 'tostr': 'hop { filter_not_eq { all_rows ; quarterfinals ; did not advance } ; athlete }'}, 'tariq alqallaf'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_not_eq { all_rows ; quarterfinals ; did not advance } ; athlete } ; tariq alqallaf }', 'tointer': 'the athlete record of this unqiue row is tariq alqallaf .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_not_eq { all_rows ; quarterfinals ; did not advance } } ; eq { hop { filter_not_eq { all_rows ; quarterfinals ; did not advance } ; athlete } ; tariq alqallaf } } = true', 'tointer': 'select the rows whose quarterfinals record does not match to did not advance . there is only one such row in the table . the athlete record of this unqiue row is tariq alqallaf .'}
|
and { only { filter_not_eq { all_rows ; quarterfinals ; did not advance } } ; eq { hop { filter_not_eq { all_rows ; quarterfinals ; did not advance } ; athlete } ; tariq alqallaf } } = true
|
select the rows whose quarterfinals record does not match to did not advance . there is only one such row in the table . the athlete record of this unqiue row is tariq alqallaf .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_not_eq_0': 0, 'all_rows_6': 6, 'quarterfinals_7': 7, 'did not advance_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'athlete_9': 9, 'tariq alqallaf_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_not_eq_0': 'filter_str_not_eq', 'all_rows_6': 'all_rows', 'quarterfinals_7': 'quarterfinals', 'did not advance_8': 'did not advance', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'athlete_9': 'athlete', 'tariq alqallaf_10': 'tariq alqallaf'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_not_eq_0': [1, 2], 'all_rows_6': [0], 'quarterfinals_7': [0], 'did not advance_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'athlete_9': [2], 'tariq alqallaf_10': [3]}
|
['athlete', 'class', 'event', 'bout 1', 'bout 2', 'bout 3', 'bout 4', 'bout 5', 'bout 6', 'rank', '1 / 8 finals', 'quarterfinals', 'semifinals']
|
[['abdullah alhaddad', 'cat a', 'foil', 'pender ( pol ) l 3 - 5', 'maillard ( fra ) l 1 - 5', 'mato ( hun ) l 1 - 5', 'pellegrini ( ita ) l 4 - 5', 'andreev ( rus ) w 5 - 2', 'n / a', '5 q', 'pender ( pol ) l 6 - 15', 'did not advance', 'did not advance'], ['abdullah alhaddad', 'cat a', 'épée', 'pylarinos ( gre ) w 5 - 3', 'davydenko ( ukr ) l 1 - 5', 'serafini ( ita ) w 5 - 1', 'maillard ( fra ) l 4 - 5', 'saengsawang ( tha ) w 5 - 4', 'sanchez ( esp ) w 5 - 0', '3 q', 'saengsawang ( tha ) l 9 - 15', 'did not advance', 'did not advance'], ['tariq alqallaf', 'cat a', 'foil', 'saengsawang ( tha ) w 5 - 1', 'zhang ( chn ) l 0 - 5', 'betti ( ita ) l 0 - 5', 'horvath ( hun ) w 5 - 4', 'granell ( esp ) w 5 - 0', 'andree ( ger ) w 5 - 1', '3 q', 'bazhukov ( ukr ) w 15 - 9', 'ye ( chn ) l 6 - 15', 'did not advance'], ['tariq alqallaf', 'cat a', 'épée', 'horvath ( hun ) w 5 - 1', 'stanczuk ( pol ) w 5 - 3', 'wong ( hkg ) l 3 - 5', 'tian ( chn ) l 0 - 5', 'betti ( ita ) l 0 - 5', 'n / a', '5 q', 'maillard ( fra ) l 7 - 15', 'did not advance', 'did not advance'], ['abdulwahab alsaedi', 'cat b', 'foil', 'fawcett ( gbr ) w 5 - 2', 'francois ( fra ) l 3 - 5', 'rodgers ( usa ) l 4 - 5', 'datsko ( ukr ) l 4 - 5', 'czop ( pol ) l 2 - 5', 'n / a', '5 q', 'hui ( hkg ) l 3 - 15', 'did not advance', 'did not advance'], ['abdulwahab alsaedi', 'cat b', 'épée', 'williams ( usa ) l 4 - 5', 'bogdos ( gre ) l 4 - 5', 'poleshchuk ( rus ) l 1 - 5', 'latreche ( fra ) l 2 - 5', 'komar ( ukr ) l 2 - 5', 'n / a', '6', 'did not advance', 'did not advance', 'did not advance']]
|
1974 green bay packers season
|
https://en.wikipedia.org/wiki/1974_Green_Bay_Packers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14655820-2.html.csv
|
aggregation
|
the average attendance in the 1974 green bay packers season was 46966 .
|
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '46966', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '46966', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '46966'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 46966 } = true', 'tointer': 'the average of the attendance record of all rows is 46966 .'}
|
round_eq { avg { all_rows ; attendance } ; 46966 } = true
|
the average of the attendance record of all rows is 46966 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '46966_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '46966_5': '46966'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '46966_5': [1]}
|
['week', 'date', 'opponent', 'result', 'venue', 'attendance']
|
[['1', 'september 15 , 1974', 'minnesota vikings', 'l 32 - 17', 'lambeau field', '56267'], ['2', 'september 22 , 1974', 'baltimore colts', 'w 20 - 13', 'memorial stadium', '41252'], ['3', 'september 29 , 1974', 'detroit lions', 'w 21 - 19', 'milwaukee county stadium', '47292'], ['4', 'october 6 , 1974', 'buffalo bills', 'l 27 - 7', 'lambeau field', '56267'], ['5', 'october 13 , 1974', 'los angeles rams', 'w 17 - 6', 'milwaukee county stadium', '47499'], ['6', 'october 21 , 1974', 'chicago bears', 'l 10 - 9', 'soldier field', '50623'], ['7', 'october 27 , 1974', 'detroit lions', 'l 19 - 17', 'tiger stadium', '51775'], ['8', 'november 3 , 1974', 'washington redskins', 'l 17 - 6', 'lambeau field', '56267'], ['9', 'november 10 , 1974', 'chicago bears', 'w 20 - 3', 'milwaukee county stadium', '46567'], ['10', 'november 17 , 1974', 'minnesota vikings', 'w 19 - 7', 'metropolitan stadium', '47924'], ['11', 'november 24 , 1974', 'san diego chargers', 'w 34 - 0', 'lambeau field', '56267'], ['12', 'december 1 , 1974', 'philadelphia eagles', 'l 36 - 14', 'veterans stadium', '42030'], ['13', 'december 8 , 1974', 'san francisco 49ers', 'l 7 - 6', 'candlestick park', '47475'], ['14', 'december 15 , 1974', 'atlanta falcons', 'l 10 - 3', 'atlanta stadium', '10020']]
|
2010 - 11 albany great danes men 's basketball team
|
https://en.wikipedia.org/wiki/2010%E2%80%9311_Albany_Great_Danes_men%27s_basketball_team
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29846807-5.html.csv
|
majority
|
ambrose was the leading scorer for the great danes in the majority of games in 2010-2011 from games 16-22 .
|
{'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'ambrose', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'high assists', 'ambrose'], 'result': True, 'ind': 0, 'tointer': 'for the high assists records of all rows , most of them fuzzily match to ambrose .', 'tostr': 'most_eq { all_rows ; high assists ; ambrose } = true'}
|
most_eq { all_rows ; high assists ; ambrose } = true
|
for the high assists records of all rows , most of them fuzzily match to ambrose .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high assists_3': 3, 'ambrose_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high assists_3': 'high assists', 'ambrose_4': 'ambrose'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high assists_3': [0], 'ambrose_4': [0]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['16', 'january 2', 'new hampshire', 'w 59 - 44', 'ambrose - 16', 'ambrose - 9', 'ambrose - 5', 'sefcu arena , albany , ny ( 1730 )', '8 - 8'], ['17', 'january 6', 'vermont', 'l 60 - 48', 'aronhalt - 21', 'aronhalt - 4', 'ambrose - 4', 'patrick gym , burlington , vt ( 2270 )', '8 - 9'], ['19', 'january 12', 'maine', 'l 66 - 64', 'black - 18', 'devlin - 6', 'ambrose - 5', 'sefcu arena , albany , ny ( 1041 )', '8 - 11'], ['20', 'january 15', 'boston university', 'l 70 - 67', 'ambrose - 18', 'watts - 6', 'black - 8', 'sefcu arena , albany , ny ( 1348 )', '8 - 12'], ['21', 'january 17', 'stony brook', 'w 52 - 50', 'ambrose - 15', 'ambrose - 14', 'black - 4', 'pritchard gymnasium , stony brook , ny ( 1630 )', '9 - 12'], ['22', 'january 20', 'binghamton', 'w 76 - 37', 'black - 15', 'ambrose - 7', 'ambrose - 5', 'sefcu arena , albany , ny ( 1804 )', '10 - 12']]
|
agriculture in australia
|
https://en.wikipedia.org/wiki/Agriculture_in_Australia
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1057262-1.html.csv
|
aggregation
|
the average quantity of all commodities in 2001-2002 was approximately 2907 .
|
{'scope': 'all', 'col': '2', 'type': 'average', 'result': '2907', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', '2001 - 02'], 'result': '2907', 'ind': 0, 'tostr': 'avg { all_rows ; 2001 - 02 }'}, '2907'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; 2001 - 02 } ; 2907 } = true', 'tointer': 'the average of the 2001 - 02 record of all rows is 2907 .'}
|
round_eq { avg { all_rows ; 2001 - 02 } ; 2907 } = true
|
the average of the 2001 - 02 record of all rows is 2907 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, '2001 - 02_4': 4, '2907_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', '2001 - 02_4': '2001 - 02', '2907_5': '2907'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], '2001 - 02_4': [0], '2907_5': [1]}
|
['commodity', '2001 - 02', '2002 - 03', '2003 - 04', '2004 - 05', '2005 - 06', '2006 - 07']
|
[['cattle and calves', '6617', '5849', '6345', '7331', '7082', '6517'], ['wheat', '6356', '2692', '5636', '4320', '5905', '6026'], ['milk', '3717', '2795', '2808', '3194', '3268', '3245'], ['fruit and nuts', '2333', '2408', '2350', '2640', '2795', '2915'], ['s vegetable', '2269', '2126', '2356', '2490', '2601', '2715'], ['wool', '2713', '3318', '2397', '2196', '2187', '2138'], ['barley', '1725', '984', '1750', '1240', '1744', '1624'], ['poultry', '1175', '1273', '1264', '1358', '1416', '1461'], ['s lamb', '1181', '1161', '1318', '1327', '1425', '1348'], ['sugar cane', '989', '1019', '854', '968', '1037', '1208']]
|
1981 - 82 coupe de france
|
https://en.wikipedia.org/wiki/1981%E2%80%9382_Coupe_de_France
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16893470-1.html.csv
|
aggregation
|
the average score for all games in the 1981-82 coupe de france was about 3.7-2 .7 .
|
{'scope': 'all', 'col': '2', 'type': 'average', 'result': '3.7-2 .7', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '3.7-2 .7', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '3.7-2 .7'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 3.7-2 .7 } = true', 'tointer': 'the average of the score record of all rows is 3.7-2 .7 .'}
|
round_eq { avg { all_rows ; score } ; 3.7-2 .7 } = true
|
the average of the score record of all rows is 3.7-2 .7 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '3.7-2.7_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '3.7-2.7_5': '3.7-2 .7'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '3.7-2.7_5': [1]}
|
['team 1', 'score', 'team 2', '1st round', '2nd round']
|
[['girondins de bordeaux ( d1 )', '4 - 2', 'as monaco ( d1 )', '2 - 1', '2 - 1'], ['as saint - étienne ( d1 )', '5 - 3', 'stade brestois ( d1 )', '2 - 0', '3 - 3'], ['sc bastia ( d1 )', '4 - 3', 'olympique lyonnais ( d1 )', '2 - 0', '2 - 3'], ['tours fc ( d1 )', '6 - 5', 'fc metz ( d1 )', '4 - 1', '2 - 4'], ['olympique de marseille ( d2 )', '1 - 4', 'paris sg ( d1 )', '0 - 1', '1 - 3'], ['sporting toulon var ( d2 )', '4 - 2', 'as nancy ( d1 )', '2 - 1', '2 - 1'], ['valenciennes fc ( d1 )', '4 - 2', 'le havre ac ( d2 )', '2 - 0', '2 - 2'], ['stade lavallois ( d1 )', '2 - 1', 'besançon rc ( d2 )', '2 - 1', '0 - 0']]
|
fiba eurobasket 2007 squads
|
https://en.wikipedia.org/wiki/FIBA_EuroBasket_2007_squads
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12962773-14.html.csv
|
count
|
2 players in the fiba eurobasket 2007 squad were born in 1986 .
|
{'scope': 'all', 'criterion': 'equal', 'value': '1986', 'result': '2', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year born', '1986'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year born record is equal to 1986 .', 'tostr': 'filter_eq { all_rows ; year born ; 1986 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; year born ; 1986 } }', 'tointer': 'select the rows whose year born record is equal to 1986 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; year born ; 1986 } } ; 2 } = true', 'tointer': 'select the rows whose year born record is equal to 1986 . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; year born ; 1986 } } ; 2 } = true
|
select the rows whose year born record is equal to 1986 . 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, 'year born_5': 5, '1986_6': 6, '2_7': 7}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'year born_5': 'year born', '1986_6': '1986', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'year born_5': [0], '1986_6': [0], '2_7': [2]}
|
['no', 'player', 'height', 'position', 'year born', 'current club']
|
[['4', 'sandi čebular', '1.94', 'guard', '1986', 'unattached'], ['5', 'jaka lakovič', '1.86', 'guard', '1978', 'axa fc barcelona'], ['6', 'aleksandar ćapin', '1.86', 'guard', '1982', 'whirlpool varese'], ['7', 'goran dragić', '1.88', 'guard', '1986', 'tau cerámica'], ['8', 'rasho nesterovič', '2.14', 'center', '1976', 'toronto raptors'], ['9', 'matjaž smodiš', '2.05', 'forward', '1979', 'cska moscow'], ['10', 'uroš slokar', '2.09', 'center', '1983', 'triumph lyubertsy'], ['11', 'jaka klobučar', '1.94', 'guard', '1987', 'geoplin slovan'], ['12', 'goran jagodnik', '2.02', 'forward', '1974', 'hemofarm'], ['13', 'domen lorbek', '1.96', 'guard', '1985', 'mmt estudiantes'], ['14', 'gašper vidmar', '2.08', 'center', '1987', 'fenerbahçe ülker']]
|
list of brotherly love episodes
|
https://en.wikipedia.org/wiki/List_of_Brotherly_Love_episodes
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25679312-2.html.csv
|
comparative
|
the episode of brotherly love entitled remember aired earlier than big brotherly love .
|
{'row_1': '13', 'row_2': '14', 'col': '5', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
|
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode title', 'remember'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose episode title record fuzzily matches to remember .', 'tostr': 'filter_eq { all_rows ; episode title ; remember }'}, 'original air date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; episode title ; remember } ; original air date }', 'tointer': 'select the rows whose episode title record fuzzily matches to remember . take the original air date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'episode title', 'big brotherly love'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose episode title record fuzzily matches to big brotherly love .', 'tostr': 'filter_eq { all_rows ; episode title ; big brotherly love }'}, 'original air date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; episode title ; big brotherly love } ; original air date }', 'tointer': 'select the rows whose episode title record fuzzily matches to big brotherly love . take the original air date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; episode title ; remember } ; original air date } ; hop { filter_eq { all_rows ; episode title ; big brotherly love } ; original air date } } = true', 'tointer': 'select the rows whose episode title record fuzzily matches to remember . take the original air date record of this row . select the rows whose episode title record fuzzily matches to big brotherly love . take the original air date record of this row . the first record is less than the second record .'}
|
less { hop { filter_eq { all_rows ; episode title ; remember } ; original air date } ; hop { filter_eq { all_rows ; episode title ; big brotherly love } ; original air date } } = true
|
select the rows whose episode title record fuzzily matches to remember . take the original air date record of this row . select the rows whose episode title record fuzzily matches to big brotherly love . take the original air date record of this row . the first record is less than the second record .
|
5
|
5
|
{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'episode title_7': 7, 'remember_8': 8, 'original air date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'episode title_11': 11, 'big brotherly love_12': 12, 'original air date_13': 13}
|
{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'episode title_7': 'episode title', 'remember_8': 'remember', 'original air date_9': 'original air date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'episode title_11': 'episode title', 'big brotherly love_12': 'big brotherly love', 'original air date_13': 'original air date'}
|
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'episode title_7': [0], 'remember_8': [0], 'original air date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'episode title_11': [1], 'big brotherly love_12': [1], 'original air date_13': [3]}
|
['series', 'episode title', 'directed by', 'written by', 'original air date', 'prod code']
|
[['1', 'pilot', 'terry hughes', 'jonathan schmock & jim vallely', 'september 16 , 1995', '1'], ['2', 'such a bargain', 'terry hughes', 'jim halkett', 'september 17 , 1995', '3'], ['3', 'the liberty bell show', 'terry hughes', 'craig hoffman', 'september 24 , 1995', '5'], ['4', "a midsummer 's nightmare", 'terry hughes', 'pamela eells', 'october 1 , 1995', '6'], ['5', 'uptown girl', 'terry hughes', 'michelle j wolff', 'october 8 , 1995', '2'], ['6', 'the comic con', 'terry hughes', 'john levenstein', 'october 25 , 1995', '8'], ['7', 'the sleepover show', 'terry hughes', 'eddie gorodetsky', 'october 29 , 1995', '4'], ['8', 'witchcraft', 'terry hughes', 'jonathan schmock', 'october 30 , 1995', '7'], ['9', 'bait and switch', 'terry hughes', 'john levenstein', 'november 12 , 1995', '10'], ['10', 'outbreak !', 'terry hughes', 'craig hoffman', 'november 19 , 1995', '11'], ['11', 'a roman holiday', 'terry hughes', 'jim vallely', 'december 18 , 1995', '13'], ['12', 'once around the block', 'terry hughes', 'jonathan schmock', 'march 4 , 1996', '12'], ['13', 'remember', 'terry hughes', 'pamela eells', 'march 11 , 1996', '16'], ['14', 'big brotherly love', 'terry hughes', 'jonathan schmock & jim vallely', 'march 18 , 1996', '18'], ['15', 'bride and prejudice', 'terry hughes', 'michelle j wolff', 'march 25 , 1996', '15']]
|
law & order ( season 1 )
|
https://en.wikipedia.org/wiki/Law_%26_Order_%28season_1%29
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2618061-1.html.csv
|
unique
|
" indifference " was the only episode directed by james quinn .
|
{'scope': 'all', 'row': '5', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'james quinn', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'james quinn'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to james quinn .', 'tostr': 'filter_eq { all_rows ; directed by ; james quinn }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; directed by ; james quinn } }', 'tointer': 'select the rows whose directed by record fuzzily matches to james quinn . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'james quinn'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose directed by record fuzzily matches to james quinn .', 'tostr': 'filter_eq { all_rows ; directed by ; james quinn }'}, 'title'], 'result': 'indifference', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; directed by ; james quinn } ; title }'}, 'indifference'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; directed by ; james quinn } ; title } ; indifference }', 'tointer': 'the title record of this unqiue row is indifference .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; directed by ; james quinn } } ; eq { hop { filter_eq { all_rows ; directed by ; james quinn } ; title } ; indifference } } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to james quinn . there is only one such row in the table . the title record of this unqiue row is indifference .'}
|
and { only { filter_eq { all_rows ; directed by ; james quinn } } ; eq { hop { filter_eq { all_rows ; directed by ; james quinn } ; title } ; indifference } } = true
|
select the rows whose directed by record fuzzily matches to james quinn . there is only one such row in the table . the title record of this unqiue row is indifference .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'directed by_7': 7, 'james quinn_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'indifference_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'directed by_7': 'directed by', 'james quinn_8': 'james quinn', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'indifference_10': 'indifference'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'directed by_7': [0], 'james quinn_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'indifference_10': [3]}
|
['no in series', 'title', 'directed by', 'written by', 'original air date', 'production code']
|
[['2', 'subterranean homeboy blues', 'ew swackhamer', 'robert palm', 'september 20 , 1990', '66205'], ['4', 'kiss the girls and make them die', 'charles correll', 'teleplay : robert stuart nathan story : dick wolf', 'october 11 , 1990', '66210'], ['6', "everybody 's favorite bagman", 'john patterson', 'dick wolf', 'october 30 , 1990', '83543'], ['7', 'by hooker , by crook', 'martin davidson', 'david black', 'november 13 , 1990', '66203'], ['9', 'indifference', 'james quinn', 'robert palm', 'november 27 , 1990', '66207'], ['11', 'out of the half - light', 'e w swackhamer', 'michael duggan', 'december 11 , 1990', '66202'], ['14', 'the violence of summer', 'don scardino', 'michael duggan', 'february 5 , 1991', '66219'], ['17', 'mushrooms', 'daniel sackheim', 'robert palm', 'february 26 , 1991', '66218']]
|
2006 japanese television dramas
|
https://en.wikipedia.org/wiki/2006_Japanese_television_dramas
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18540022-3.html.csv
|
aggregation
|
the average number of episodes for 2006 japanese television dramas is 10.72 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '10.73', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'episodes'], 'result': '10.73', 'ind': 0, 'tostr': 'avg { all_rows ; episodes }'}, '10.73'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; episodes } ; 10.73 } = true', 'tointer': 'the average of the episodes record of all rows is 10.73 .'}
|
round_eq { avg { all_rows ; episodes } ; 10.73 } = true
|
the average of the episodes record of all rows is 10.73 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'episodes_4': 4, '10.73_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'episodes_4': 'episodes', '10.73_5': '10.73'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'episodes_4': [0], '10.73_5': [1]}
|
['japanese title', 'romaji title', 'tv station', 'episodes', 'average ratings']
|
[['サプリ', 'sapuri', 'fuji tv', '11', '14.2 %'], ['不信のとき ～ ウーマン ・ ウォーズ ～', 'fushin no toki ~ woman wars ~', 'fuji tv', '12', '12.9 %'], ['結婚できない男', 'kekkon dekinai otoko', 'fuji tv', '12', '17.1 %'], ['ダンドリ 。 ～ dance ☆ drill ～', 'dandori ~ dance ☆ drill ~', 'fuji tv', '11', '8.9 %'], ['誰よりもママを愛す', 'dare yorimo mama wo ai su', 'tbs', '11', '10.4 %'], ['花嫁は厄年ッ !', 'hanayome wa yakudoshi !', 'tbs', '12', '12.0 %'], ['タイヨウのうた', 'taiyou no uta', 'tbs', '10', '10.3 %'], ['レガッタ ～ 君といた永遠 ～', 'regatta ~ kimi to ita eien ~', 'tv - asahi', '9', '5.4 %'], ['下北サンデーズ', 'shimokita sundays', 'tv - asahi', '9', '7.3 %'], ['caとお呼びっ !', 'ca to oyobbi !', 'ntv', '11', '9.5 %'], ['マイ ☆ ボス マイ ☆ ヒーロー', 'my ☆ boss my ☆ hero', 'ntv', '10', '18.9 %']]
|
1978 - 79 new york rangers season
|
https://en.wikipedia.org/wiki/1978%E2%80%9379_New_York_Rangers_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14056030-13.html.csv
|
count
|
two players from northern michigan university were drafted in the 1978 - 79 new york rangers season .
|
{'scope': 'all', 'criterion': 'equal', 'value': 'northern michigan university ( ncaa )', 'result': '2', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college / junior / club team ( league )', 'northern michigan university ( ncaa )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college / junior / club team ( league ) record fuzzily matches to northern michigan university ( ncaa ) .', 'tostr': 'filter_eq { all_rows ; college / junior / club team ( league ) ; northern michigan university ( ncaa ) }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; college / junior / club team ( league ) ; northern michigan university ( ncaa ) } }', 'tointer': 'select the rows whose college / junior / club team ( league ) record fuzzily matches to northern michigan university ( ncaa ) . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; college / junior / club team ( league ) ; northern michigan university ( ncaa ) } } ; 2 } = true', 'tointer': 'select the rows whose college / junior / club team ( league ) record fuzzily matches to northern michigan university ( ncaa ) . the number of such rows is 2 .'}
|
eq { count { filter_eq { all_rows ; college / junior / club team ( league ) ; northern michigan university ( ncaa ) } } ; 2 } = true
|
select the rows whose college / junior / club team ( league ) record fuzzily matches to northern michigan university ( ncaa ) . 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, 'college / junior / club team (league)_5': 5, 'northern michigan university (ncaa)_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', 'college / junior / club team (league)_5': 'college / junior / club team ( league )', 'northern michigan university (ncaa)_6': 'northern michigan university ( ncaa )', '2_7': '2'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'college / junior / club team (league)_5': [0], 'northern michigan university (ncaa)_6': [0], '2_7': [2]}
|
['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )']
|
[['2', 'don maloney', 'lw', 'canada', 'kitchener rangers ( oha )'], ['3', 'ray markham', 'c', 'canada', 'flin flon bombers ( wchl )'], ['3', 'dean turner', 'd', 'united states', 'university of michigan ( ncaa )'], ['4', 'dave silk', 'rw', 'united states', 'boston university ( ncaa )'], ['4', 'andre dore', 'd', 'canada', 'quebec remparts ( qmjhl )'], ['5', 'mike mcdougall', 'rw', 'united states', 'port huron flags ( ihl )'], ['6', 'tom laidlaw', 'd', 'canada', 'northern michigan university ( ncaa )'], ['7', 'dan clark', 'd', 'canada', 'milwaukee admirals ( ihl )'], ['8', 'greg kostenko', 'd', 'canada', 'ohio state university ( ncaa )'], ['9', 'brian mcdavid', 'd', 'canada', 'kitchener rangers ( oha )'], ['10', 'mark rodrigues', 'g', 'united states', 'yale university ( ncaa )'], ['11', 'steve weeks', 'g', 'canada', 'northern michigan university ( ncaa )'], ['12', 'pierre daigneault', 'lw', 'canada', 'montreal juniors ( qmjhl )'], ['13', 'chris mclaughlin', 'd', 'united states', 'dartmouth college ( ncaa )'], ['14', 'todd johnson', 'c', 'united states', 'boston university ( ncaa )'], ['15', 'dan mccarthy', 'c', 'canada', 'sudbury wolves ( oha )']]
|
list of fc barcelona records and statistics
|
https://en.wikipedia.org/wiki/List_of_FC_Barcelona_records_and_statistics
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14707564-7.html.csv
|
unique
|
lionel messi is the only player from argentina in the list of fc barcelona records and statistics .
|
{'scope': 'all', 'row': '2', 'col': '2', 'col_other': '3', 'criterion': 'equal', 'value': 'argentina', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'argentina'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to argentina .', 'tostr': 'filter_eq { all_rows ; nationality ; argentina }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nationality ; argentina } }', 'tointer': 'select the rows whose nationality record fuzzily matches to argentina . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'argentina'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to argentina .', 'tostr': 'filter_eq { all_rows ; nationality ; argentina }'}, 'name'], 'result': 'lionel messi', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nationality ; argentina } ; name }'}, 'lionel messi'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; nationality ; argentina } ; name } ; lionel messi }', 'tointer': 'the name record of this unqiue row is lionel messi .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; nationality ; argentina } } ; eq { hop { filter_eq { all_rows ; nationality ; argentina } ; name } ; lionel messi } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to argentina . there is only one such row in the table . the name record of this unqiue row is lionel messi .'}
|
and { only { filter_eq { all_rows ; nationality ; argentina } } ; eq { hop { filter_eq { all_rows ; nationality ; argentina } ; name } ; lionel messi } } = true
|
select the rows whose nationality record fuzzily matches to argentina . there is only one such row in the table . the name record of this unqiue row is lionel messi .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nationality_7': 7, 'argentina_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'lionel messi_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nationality_7': 'nationality', 'argentina_8': 'argentina', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'lionel messi_10': 'lionel messi'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'nationality_7': [0], 'argentina_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'lionel messi_10': [3]}
|
['ranking', 'nationality', 'name', 'goals', 'years']
|
[['1', 'philippines', 'paulino alcántara', '369', '1912 - 1916 , 1918 - 1927'], ['2', 'argentina', 'lionel messi', '352', '2004 -'], ['3', 'spain', 'josep samitier', '333', '1919 - 1932'], ['4', 'spain', 'césar rodríguez', '301', '1942 - 1955'], ['5', 'hungary', 'ladislao kubala', '280', '1950 - 1961'], ['6', 'spain', 'josep escolà', '223', '1934 - 1949'], ['7', 'spain', 'ángel arocha', '215', '1926 - 1933'], ['8', 'spain', 'vicenç martínez', '200', '1912 - 1923'], ['9', 'spain', 'carles rexach', '195', '1965 - 1981'], ['10', 'spain', 'mariano martín', '188', '1939 - 1946']]
|
1968 vfl season
|
https://en.wikipedia.org/wiki/1968_VFL_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10808933-8.html.csv
|
count
|
there were 6 game venues used during the 1968 vfl season .
|
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '5', 'subset': None}
|
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'venue'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record is arbitrary .', 'tostr': 'filter_all { all_rows ; venue }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; venue } }', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; venue } } ; 6 } = true', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}
|
eq { count { filter_all { all_rows ; venue } } ; 6 } = true
|
select the rows whose venue record is arbitrary . the number of such rows is 6 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'venue_5': 5, '6_6': 6}
|
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'venue_5': 'venue', '6_6': '6'}
|
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'venue_5': [0], '6_6': [2]}
|
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
|
[['footscray', '9.8 ( 62 )', 'south melbourne', '11.9 ( 75 )', 'western oval', '14755', '8 june 1968'], ['collingwood', '11.13 ( 79 )', 'melbourne', '13.7 ( 85 )', 'victoria park', '24375', '8 june 1968'], ['north melbourne', '9.11 ( 65 )', 'geelong', '13.11 ( 89 )', 'arden street oval', '13209', '8 june 1968'], ['richmond', '11.15 ( 81 )', 'hawthorn', '11.14 ( 80 )', 'mcg', '31325', '10 june 1968'], ['st kilda', '16.17 ( 113 )', 'essendon', '8.9 ( 57 )', 'moorabbin oval', '43231', '10 june 1968'], ['fitzroy', '4.10 ( 34 )', 'carlton', '13.15 ( 93 )', 'princes park', '19306', '10 june 1968']]
|
2007 - 08 football league trophy
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Football_League_Trophy
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12962079-4.html.csv
|
superlative
|
for the 2007-08 football league trophy , the largest attendance was when the home team was swansea city .
|
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '5', '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', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'home team'], 'result': 'swansea city', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; home team }'}, 'swansea city'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; home team } ; swansea city } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the home team record of this row is swansea city .'}
|
eq { hop { argmax { all_rows ; attendance } ; home team } ; swansea city } = true
|
select the row whose attendance record of all rows is maximum . the home team record of this row is swansea city .
|
3
|
3
|
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'home team_6': 6, 'swansea city_7': 7}
|
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'home team_6': 'home team', 'swansea city_7': 'swansea city'}
|
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'home team_6': [1], 'swansea city_7': [2]}
|
['tie no', 'home team', 'score', 'away team', 'attendance']
|
[['1', 'hereford united', '0 - 0', 'yeovil town', '1859'], ['yeovil town won 4 - 2 on penalties', 'yeovil town won 4 - 2 on penalties', 'yeovil town won 4 - 2 on penalties', 'yeovil town won 4 - 2 on penalties', 'yeovil town won 4 - 2 on penalties'], ['2', 'bristol rovers', '0 - 1', 'bournemouth', '3313'], ['3', 'swindon town', '1 - 3', 'cheltenham town', '3765'], ['4', 'swansea city', '2 - 0', 'wycombe wanderers', '5922'], ['5', 'milton keynes dons', '3 - 1', 'peterborough united', '5087'], ['6', 'brighton & hove albion', '2 - 1', 'barnet', '1995'], ['7', 'leyton orient', '0 - 1', 'dagenham & redbridge', '2397'], ['8', 'gillingham', '4 - 3', 'luton town', '1417']]
|
2003 - 04 toronto raptors season
|
https://en.wikipedia.org/wiki/2003%E2%80%9304_Toronto_Raptors_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15869204-9.html.csv
|
majority
|
donyell marshall had the majority of high rebounds performances in the 2003 - 04 toronto raptors season .
|
{'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'donyell marshall', 'subset': None}
|
{'func': 'most_str_eq', 'args': ['all_rows', 'high rebounds', 'donyell marshall'], 'result': True, 'ind': 0, 'tointer': 'for the high rebounds records of all rows , most of them fuzzily match to donyell marshall .', 'tostr': 'most_eq { all_rows ; high rebounds ; donyell marshall } = true'}
|
most_eq { all_rows ; high rebounds ; donyell marshall } = true
|
for the high rebounds records of all rows , most of them fuzzily match to donyell marshall .
|
1
|
1
|
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high rebounds_3': 3, 'donyell marshall_4': 4}
|
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high rebounds_3': 'high rebounds', 'donyell marshall_4': 'donyell marshall'}
|
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high rebounds_3': [0], 'donyell marshall_4': [0]}
|
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
|
[['75', 'april 2', 'indiana', 'l 64 - 84 ( ot )', 'jalen rose ( 22 )', 'chris bosh ( 7 )', 'rod strickland ( 4 )', 'conseco fieldhouse 17775', '30 - 45'], ['76', 'april 4', 'milwaukee', 'l 83 - 90 ( ot )', 'jalen rose ( 21 )', 'donyell marshall ( 16 )', 'jalen rose ( 7 )', 'air canada centre 17276', '30 - 46'], ['77', 'april 6', 'cleveland', 'w 87 - 86 ( ot )', 'vince carter ( 32 )', 'donyell marshall ( 11 )', 'jalen rose ( 6 )', 'gund arena 20071', '31 - 46'], ['78', 'april 7', 'indiana', 'l 90 - 94 ( ot )', 'donyell marshall ( 26 )', 'donyell marshall ( 10 )', 'jalen rose ( 8 )', 'air canada centre 17554', '31 - 47'], ['79', 'april 9', 'detroit', 'l 66 - 74 ( ot )', 'chris bosh , vince carter ( 18 )', 'donyell marshall ( 11 )', 'vince carter ( 5 )', 'the palace of auburn hills 22076', '31 - 48'], ['80', 'april 11', 'chicago', 'l 108 - 114 ( ot )', 'jalen rose ( 32 )', 'donyell marshall ( 16 )', 'jalen rose ( 6 )', 'air canada centre 17362', '31 - 49'], ['81', 'april 13', 'detroit', 'w 87 - 78 ( ot )', 'donyell marshall ( 27 )', 'donyell marshall ( 16 )', 'morris peterson , jalen rose ( 5 )', 'air canada centre 18273', '32 - 49']]
|
2007 pga championship
|
https://en.wikipedia.org/wiki/2007_PGA_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12333215-2.html.csv
|
aggregation
|
in the 2007 pga championship , the average total was 284.5 .
|
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '284.5', 'subset': None}
|
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total'], 'result': '284.5', 'ind': 0, 'tostr': 'avg { all_rows ; total }'}, '284.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total } ; 284.5 } = true', 'tointer': 'the average of the total record of all rows is 284.5 .'}
|
round_eq { avg { all_rows ; total } ; 284.5 } = true
|
the average of the total record of all rows is 284.5 .
|
2
|
2
|
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total_4': 4, '284.5_5': 5}
|
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total_4': 'total', '284.5_5': '284.5'}
|
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total_4': [0], '284.5_5': [1]}
|
['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish']
|
[['tiger woods', 'united states', '1999 , 2000 , 2006', '272', '8', '1'], ['john daly', 'united states', '1991', '286', '+ 6', 't32'], ['shaun micheel', 'united states', '2003', '286', '+ 6', 't32'], ['phil mickelson', 'united states', '2005', '286', '+ 6', 't32'], ['david toms', 'united states', '2001', '288', '+ 8', 't42'], ['bob tway', 'united states', '1986', '289', '+ 9', 't50']]
|
2007 - 08 los angeles kings season
|
https://en.wikipedia.org/wiki/2007%E2%80%9308_Los_Angeles_Kings_season
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11821711-5.html.csv
|
unique
|
in the 2007 - 08 los angeles kings season , when the decision was labarbera , the only time attendance was over 18000 was on november 3 .
|
{'scope': 'subset', 'row': '2', 'col': '6', 'col_other': '1,5', 'criterion': 'greater_than', 'value': '18000', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'labarbera'}}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'decision', 'labarbera'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; decision ; labarbera }', 'tointer': 'select the rows whose decision record fuzzily matches to labarbera .'}, 'attendance', '18000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose decision record fuzzily matches to labarbera . among these rows , select the rows whose attendance record is greater than 18000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } }', 'tointer': 'select the rows whose decision record fuzzily matches to labarbera . among these rows , select the rows whose attendance record is greater than 18000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'decision', 'labarbera'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; decision ; labarbera }', 'tointer': 'select the rows whose decision record fuzzily matches to labarbera .'}, 'attendance', '18000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose decision record fuzzily matches to labarbera . among these rows , select the rows whose attendance record is greater than 18000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 }'}, 'date'], 'result': 'november 3', 'ind': 3, 'tostr': 'hop { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } ; date }'}, 'november 3'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } ; date } ; november 3 }', 'tointer': 'the date record of this unqiue row is november 3 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } ; date } ; november 3 } } = true', 'tointer': 'select the rows whose decision record fuzzily matches to labarbera . among these rows , select the rows whose attendance record is greater than 18000 . there is only one such row in the table . the date record of this unqiue row is november 3 .'}
|
and { only { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; decision ; labarbera } ; attendance ; 18000 } ; date } ; november 3 } } = true
|
select the rows whose decision record fuzzily matches to labarbera . among these rows , select the rows whose attendance record is greater than 18000 . there is only one such row in the table . the date record of this unqiue row is november 3 .
|
8
|
6
|
{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'decision_8': 8, 'labarbera_9': 9, 'attendance_10': 10, '18000_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'date_12': 12, 'november 3_13': 13}
|
{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'decision_8': 'decision', 'labarbera_9': 'labarbera', 'attendance_10': 'attendance', '18000_11': '18000', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'date_12': 'date', 'november 3_13': 'november 3'}
|
{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_greater_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'decision_8': [0], 'labarbera_9': [0], 'attendance_10': [1], '18000_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'date_12': [3], 'november 3_13': [4]}
|
['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record']
|
[['november 2', 'los angeles', '5 - 2', 'san jose', 'aubin', '17496', '7 - 7 - 0'], ['november 3', 'san jose', '3 - 1', 'los angeles', 'labarbera', '18118', '7 - 8 - 0'], ['november 10', 'dallas', '5 - 6', 'los angeles', 'aubin', '18118', '8 - 8 - 0'], ['november 13', 'los angeles', '3 - 4', 'anaheim', 'labarbera', '17174', '8 - 8 - 1'], ['november 15', 'anaheim', '6 - 3', 'los angeles', 'aubin', '18118', '8 - 9 - 1'], ['november 17', 'phoenix', '1 - 0', 'los angeles', 'labarbera', '15659', '8 - 10 - 1'], ['november 19', 'los angeles', '0 - 3', 'dallas', 'labarbera', '17208', '8 - 11 - 1'], ['november 21', 'los angeles', '1 - 4', 'phoenix', 'labarbera', '12161', '8 - 12 - 1'], ['november 24', 'los angeles', '2 - 1', 'san jose', 'labarbera', '17496', '9 - 12 - 1'], ['november 25', 'los angeles', '2 - 3', 'anaheim', 'labarbera', '17174', '9 - 13 - 1'], ['november 28', 'los angeles', '3 - 2', 'san jose', 'labarbera', '17071', '10 - 13 - 1']]
|
denis gremelmayr
|
https://en.wikipedia.org/wiki/Denis_Gremelmayr
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15209396-2.html.csv
|
superlative
|
for denis gremelmayr , the earliest tournament on a clay surface was on january 8 , 2001 .
|
{'scope': 'subset', 'col_superlative': '1', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '3', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'clay'}}
|
{'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; surface ; clay }', 'tointer': 'select the rows whose surface record fuzzily matches to clay .'}, 'date'], 'result': 'january 8 , 2001', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; surface ; clay } ; date }', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the minimum date record of these rows is january 8 , 2001 .'}, 'january 8 , 2001'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; surface ; clay } ; date } ; january 8 , 2001 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the minimum date record of these rows is january 8 , 2001 .'}
|
eq { min { filter_eq { all_rows ; surface ; clay } ; date } ; january 8 , 2001 } = true
|
select the rows whose surface record fuzzily matches to clay . the minimum date record of these rows is january 8 , 2001 .
|
3
|
3
|
{'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'surface_5': 5, 'clay_6': 6, 'date_7': 7, 'january 8 , 2001_8': 8}
|
{'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'surface_5': 'surface', 'clay_6': 'clay', 'date_7': 'date', 'january 8 , 2001_8': 'january 8 , 2001'}
|
{'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'clay_6': [0], 'date_7': [1], 'january 8 , 2001_8': [2]}
|
['date', 'tournament', 'surface', 'opponent', 'score']
|
[['september 25 , 2000', 'kawaguchi', 'hard', 'leigh holland', '6 - 1 , 6 - 2'], ['january 8 , 2001', 'jorhat', 'clay', 'fabio maggi', '7 - 6 , 7 - 5'], ['october 8 , 2001', 'santo domingo', 'clay', 'josé de armas', '6 - 4 , 6 - 0'], ['january 21 , 2002', 'dubai', 'hard', 'jaroslav levinský', 'w / o'], ['august 25 , 2003', 'enschede', 'clay', 'robert lindstedt', '6 - 3 , 3 - 6 , 6 - 3'], ['november 1 , 2004', 'bangkok', 'hard', 'ruben de kleijn', '6 - 4 , 6 - 0'], ['june 27 , 2005', 'heerhugowaard', 'clay', 'nicolas todero', '6 - 4 , 6 - 2'], ['july 4 , 2005', 'kassel', 'clay', 'sascha kloer', '6 - 2 , 6 - 1'], ['september 3 , 2007', 'düsseldorf', 'clay', 'andreas haider - maurer', '6 - 7 , 6 - 2 , 6 - 4'], ['november 5 , 2007', 'eckental', 'carpet', 'roko karanušić', 'w / o'], ['november 3 , 2008', 'eckental', 'carpet', 'roko karanušić', '6 - 2 , 7 - 5'], ['may 23 , 2010', 'cremona', 'hard', 'marius copil', '6 - 4 , 7 - 5'], ['july 5 , 2010', 'scheveningen', 'clay', 'thomas schoorel', '7 - 5 , 6 - 4'], ['july 19 , 2010', 'poznań', 'clay', 'andrey kuznetsov', '6 - 1 , 6 - 2']]
|
2011 capital one world women 's curling championship
|
https://en.wikipedia.org/wiki/2011_Capital_One_World_Women%27s_Curling_Championship
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26745426-2.html.csv
|
comparative
|
wang bingyu had a higher shot percentage than anna sidorova .
|
{'row_1': '2', 'row_2': '6', 'col': '11', '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', 'skip', 'wang bingyu'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose skip record fuzzily matches to wang bingyu .', 'tostr': 'filter_eq { all_rows ; skip ; wang bingyu }'}, 'shot %'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; skip ; wang bingyu } ; shot % }', 'tointer': 'select the rows whose skip record fuzzily matches to wang bingyu . take the shot % record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'skip', 'anna sidorova'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose skip record fuzzily matches to anna sidorova .', 'tostr': 'filter_eq { all_rows ; skip ; anna sidorova }'}, 'shot %'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; skip ; anna sidorova } ; shot % }', 'tointer': 'select the rows whose skip record fuzzily matches to anna sidorova . take the shot % record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; skip ; wang bingyu } ; shot % } ; hop { filter_eq { all_rows ; skip ; anna sidorova } ; shot % } } = true', 'tointer': 'select the rows whose skip record fuzzily matches to wang bingyu . take the shot % record of this row . select the rows whose skip record fuzzily matches to anna sidorova . take the shot % record of this row . the first record is greater than the second record .'}
|
greater { hop { filter_eq { all_rows ; skip ; wang bingyu } ; shot % } ; hop { filter_eq { all_rows ; skip ; anna sidorova } ; shot % } } = true
|
select the rows whose skip record fuzzily matches to wang bingyu . take the shot % record of this row . select the rows whose skip record fuzzily matches to anna sidorova . take the shot % 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, 'skip_7': 7, 'wang bingyu_8': 8, 'shot %_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'skip_11': 11, 'anna sidorova_12': 12, 'shot %_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', 'skip_7': 'skip', 'wang bingyu_8': 'wang bingyu', 'shot %_9': 'shot %', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'skip_11': 'skip', 'anna sidorova_12': 'anna sidorova', 'shot %_13': 'shot %'}
|
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'skip_7': [0], 'wang bingyu_8': [0], 'shot %_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'skip_11': [1], 'anna sidorova_12': [1], 'shot %_13': [3]}
|
['country', 'skip', 'w', 'l', 'pf', 'pa', 'ends won', 'ends lost', 'blank ends', 'stolen ends', 'shot %']
|
[['sweden', 'anette norberg', '9', '2', '67', '53', '40', '41', '12', '8', '73 %'], ['china', 'wang bingyu', '8', '3', '64', '43', '44', '30', '14', '16', '82 %'], ['denmark', 'lene nielsen', '7', '4', '77', '55', '47', '33', '15', '14', '78 %'], ['canada', 'amber holland', '7', '4', '68', '55', '42', '40', '12', '7', '82 %'], ['switzerland', 'mirjam ott', '7', '4', '68', '58', '46', '37', '15', '15', '82 %'], ['russia', 'anna sidorova', '6', '5', '70', '65', '40', '45', '8', '8', '72 %'], ['united states', 'patti lank', '6', '5', '64', '63', '48', '36', '10', '17', '72 %'], ['germany', 'andrea schöpp', '5', '6', '61', '67', '40', '49', '12', '13', '78 %'], ['scotland', 'anna sloan', '4', '7', '49', '69', '33', '43', '15', '6', '76 %'], ['norway', 'linn githmark', '3', '8', '54', '71', '42', '48', '15', '7', '77 %'], ['czech republic', 'anna kubešková', '2', '9', '40', '73', '35', '43', '11', '7', '71 %']]
|
canadian university field lacrosse association
|
https://en.wikipedia.org/wiki/Canadian_University_Field_Lacrosse_Association
|
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18042409-1.html.csv
|
unique
|
only josh wasson is an alumnus of trent university .
|
{'scope': 'all', 'row': '8', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'trent university', 'subset': None}
|
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'alma mater', 'trent university'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose alma mater record fuzzily matches to trent university .', 'tostr': 'filter_eq { all_rows ; alma mater ; trent university }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; alma mater ; trent university } }', 'tointer': 'select the rows whose alma mater record fuzzily matches to trent university . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'alma mater', 'trent university'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose alma mater record fuzzily matches to trent university .', 'tostr': 'filter_eq { all_rows ; alma mater ; trent university }'}, 'player'], 'result': 'josh wasson', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; alma mater ; trent university } ; player }'}, 'josh wasson'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; alma mater ; trent university } ; player } ; josh wasson }', 'tointer': 'the player record of this unqiue row is josh wasson .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; alma mater ; trent university } } ; eq { hop { filter_eq { all_rows ; alma mater ; trent university } ; player } ; josh wasson } } = true', 'tointer': 'select the rows whose alma mater record fuzzily matches to trent university . there is only one such row in the table . the player record of this unqiue row is josh wasson .'}
|
and { only { filter_eq { all_rows ; alma mater ; trent university } } ; eq { hop { filter_eq { all_rows ; alma mater ; trent university } ; player } ; josh wasson } } = true
|
select the rows whose alma mater record fuzzily matches to trent university . there is only one such row in the table . the player record of this unqiue row is josh wasson .
|
6
|
5
|
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'alma mater_7': 7, 'trent university_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'josh wasson_10': 10}
|
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'alma mater_7': 'alma mater', 'trent university_8': 'trent university', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'josh wasson_10': 'josh wasson'}
|
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'alma mater_7': [0], 'trent university_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'josh wasson_10': [3]}
|
['player', 'alma mater', 'national lacrosse league', 'major league lacrosse', 'international competition']
|
[['colin doyle', 'wilfrid laurier university', 'ontario raiders / toronto rock , san jose stealth', 'toronto nationals', 'team canada'], ['steve hoar', 'university of toronto', 'toronto rock', 'toronto nationals', 'team canada'], ['creighton reid', 'university of toronto ( practice squad )', 'toronto rock , colorado mammoth', 'none', 'none'], ['jay thorimbert', 'university of guelph', 'buffalo bandits , boston blazers , minnesota swarm', 'none', 'none'], ['sean thomson', 'university of guelph', 'philadelphia wings , minnesota swarm', 'none', 'none'], ['greg harnett', "bishop 's university", 'calgary roughnecks', 'none', 'none'], ['jon harnett', 'university of guelph', 'boston blazers', 'none', 'none'], ['josh wasson', 'trent university', 'chicago shamrox , toronto rock', 'none', 'none'], ['casey zaph', 'university of toronto', 'rochester knighthawks', 'none', 'none']]
|
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