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boston college eagles women 's basketball
https://en.wikipedia.org/wiki/Boston_College_Eagles_women%27s_basketball
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17891626-2.html.csv
unique
stephanie murphy is the only 6-4 player on the team roster .
{'scope': 'all', 'row': '6', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '6 - 4', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'height', '6 - 4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose height record fuzzily matches to 6 - 4 .', 'tostr': 'filter_eq { all_rows ; height ; 6 - 4 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; height ; 6 - 4 } }', 'tointer': 'select the rows whose height record fuzzily matches to 6 - 4 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'height', '6 - 4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose height record fuzzily matches to 6 - 4 .', 'tostr': 'filter_eq { all_rows ; height ; 6 - 4 }'}, 'name'], 'result': 'stephanie murphy', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; height ; 6 - 4 } ; name }'}, 'stephanie murphy'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; height ; 6 - 4 } ; name } ; stephanie murphy }', 'tointer': 'the name record of this unqiue row is stephanie murphy .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; height ; 6 - 4 } } ; eq { hop { filter_eq { all_rows ; height ; 6 - 4 } ; name } ; stephanie murphy } } = true', 'tointer': 'select the rows whose height record fuzzily matches to 6 - 4 . there is only one such row in the table . the name record of this unqiue row is stephanie murphy .'}
and { only { filter_eq { all_rows ; height ; 6 - 4 } } ; eq { hop { filter_eq { all_rows ; height ; 6 - 4 } ; name } ; stephanie murphy } } = true
select the rows whose height record fuzzily matches to 6 - 4 . there is only one such row in the table . the name record of this unqiue row is stephanie murphy .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'height_7': 7, '6 - 4_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'stephanie murphy_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'height_7': 'height', '6 - 4_8': '6 - 4', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'stephanie murphy_10': 'stephanie murphy'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'height_7': [0], '6 - 4_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'stephanie murphy_10': [3]}
['name', 'number', 'position', 'height', 'year', 'hometown']
[['shayra brown', '20', 'point guard', '5 - 9', 'fr', 'brooklyn , new york'], ['korina chapman', '33', 'forward', '5 - 11', 'fr', 'albuquerque , new mexico'], ['kristen doherty', '21', 'guard / forward', '5 - 11', 'fr', 'holtsville , new york'], ['alyssa fressle', '1', 'guard', '5 - 10', 'rsjr', 'highlands ranch , colorado'], ['tessah holt', '3', 'guard', '5 - 5', 'rsfr', 'fayetteville , georgia'], ['stephanie murphy', '32', 'forward / center', '6 - 4', 'sr', 'londonderry , new hampshire'], ['tiffany ruffin', '31', 'point guard', '5 - 7', 'fr', 'winnacunnet , new hampshire'], ['katie zenevitch', '45', 'forward / center', '6 - 3', 'fr', 'methuen , massachusetts'], ['kerri shields', '10', 'guard', '5 - 9', 'so', 'radnor , pennsylvania'], ['jaclyn thoman', '11', 'guard', '5 - 9', 'sr', 'highlands ranch , colorado'], ['carolyn swords', '30', 'center', '6 - 6', 'sr', 'sudbury , massachusetts']]
larry davidson
https://en.wikipedia.org/wiki/Larry_Davidson
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20107762-1.html.csv
superlative
the season in which larry davidson scored the most overall points was the 2007-08 season .
{'scope': 'all', 'col_superlative': '12', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; points }'}, 'year'], 'result': '2007 - 08', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; points } ; year }'}, '2007 - 08'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; points } ; year } ; 2007 - 08 } = true', 'tointer': 'select the row whose points record of all rows is maximum . the year record of this row is 2007 - 08 .'}
eq { hop { argmax { all_rows ; points } ; year } ; 2007 - 08 } = true
select the row whose points record of all rows is maximum . the year record of this row is 2007 - 08 .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, 'year_6': 6, '2007 - 08_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', 'year_6': 'year', '2007 - 08_7': '2007 - 08'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], 'year_6': [1], '2007 - 08_7': [2]}
['year', 'team', 'games', 'mins', 'fg %', '3p %', 'ft %', 'rebounds', 'assists', 'steals', 'blocks', 'points']
[['2004 - 05', 'hunter pirates', '30', '385:33', '43.4', '34.4', '66.7', '3.2', '0.4', '0.4', '0.5', '4.0'], ['2005 - 06', 'hunter pirates', '19', '440:44', '44.9', '26.8', '71.1', '6.8', '1.1', '0.4', '0.8', '8.0'], ['2006 - 07', 'singapore slingers', '33', '650:56', '53.0', '33.3', '77.6', '4.3', '0.8', '0.3', '0.5', '6.9'], ['2007 - 08', 'wollongong hawks', '30', '744:27', '49.4', '30.9', '73.8', '7.2', '1.2', '0.6', '0.5', '10.5'], ['2008 - 09', 'wollongong hawks', '17', '328:06', '45.2', '28.6', '73.1', '4.4', '1.1', '0.6', '1.1', '6.5'], ['2009 - 10', 'wollongong hawks', '32', '849:39', '48.5', '45.1', '65.2', '6.8', '2.0', '0.7', '1.3', '9.7']]
2002 jacksonville jaguars season
https://en.wikipedia.org/wiki/2002_Jacksonville_Jaguars_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17977772-1.html.csv
unique
in the 2002 jacksonville jaguars season , of the players picked in round 7 , the only one from oregon was steve smith .
{'scope': 'subset', 'row': '8', 'col': '6', 'col_other': '4', 'criterion': 'equal', 'value': 'oregon', 'subset': {'col': '1', 'criterion': 'equal', 'value': '7'}}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'round', '7'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; round ; 7 }', 'tointer': 'select the rows whose round record is equal to 7 .'}, 'college', 'oregon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose round record is equal to 7 . among these rows , select the rows whose college record fuzzily matches to oregon .', 'tostr': 'filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } }', 'tointer': 'select the rows whose round record is equal to 7 . among these rows , select the rows whose college record fuzzily matches to oregon . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'round', '7'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; round ; 7 }', 'tointer': 'select the rows whose round record is equal to 7 .'}, 'college', 'oregon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose round record is equal to 7 . among these rows , select the rows whose college record fuzzily matches to oregon .', 'tostr': 'filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon }'}, 'name'], 'result': 'steve smith', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } ; name }'}, 'steve smith'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } ; name } ; steve smith }', 'tointer': 'the name record of this unqiue row is steve smith .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } } ; eq { hop { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } ; name } ; steve smith } } = true', 'tointer': 'select the rows whose round record is equal to 7 . among these rows , select the rows whose college record fuzzily matches to oregon . there is only one such row in the table . the name record of this unqiue row is steve smith .'}
and { only { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } } ; eq { hop { filter_eq { filter_eq { all_rows ; round ; 7 } ; college ; oregon } ; name } ; steve smith } } = true
select the rows whose round record is equal to 7 . among these rows , select the rows whose college record fuzzily matches to oregon . there is only one such row in the table . the name record of this unqiue row is steve smith .
8
6
{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_eq_0': 0, 'all_rows_7': 7, 'round_8': 8, '7_9': 9, 'college_10': 10, 'oregon_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'name_12': 12, 'steve smith_13': 13}
{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_eq_0': 'filter_eq', 'all_rows_7': 'all_rows', 'round_8': 'round', '7_9': '7', 'college_10': 'college', 'oregon_11': 'oregon', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'name_12': 'name', 'steve smith_13': 'steve smith'}
{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_eq_0': [1], 'all_rows_7': [0], 'round_8': [0], '7_9': [0], 'college_10': [1], 'oregon_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'name_12': [3], 'steve smith_13': [4]}
['round', 'pick', 'overall', 'name', 'position', 'college']
[['1', '9', '9', 'john henderson', 'defensive tackle', 'tennessee'], ['2', '8', '40', 'mike pearson', 'offensive tackle', 'florida'], ['3', '24', '89', 'akin ayodele', 'linebacker', 'purdue'], ['4', '10', '108', 'david garrard', 'quarterback', 'east carolina'], ['4', '20', '118', 'chris luzar', 'tight end', 'virginia'], ['6', '8', '180', 'clenton ballard', 'defensive tackle', 'southwest texas state'], ['7', '11', '222', 'kendall newson', 'wide receiver', 'middle tennessee state'], ['7', '36', '247', 'steve smith', 'defensive back', 'oregon'], ['7', '37', '248', 'hayden epstein', 'kicker', 'michigan']]
corruption in india
https://en.wikipedia.org/wiki/Corruption_in_India
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14496392-1.html.csv
count
a total of three states in india had an anti-corruption index score of 0.29 for the years 2006-10 .
{'scope': 'all', 'criterion': 'equal', 'value': '0.29', 'result': '3', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', '2006 - 10', '0.29'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2006 - 10 record is equal to 0.29 .', 'tostr': 'filter_eq { all_rows ; 2006 - 10 ; 0.29 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; 2006 - 10 ; 0.29 } }', 'tointer': 'select the rows whose 2006 - 10 record is equal to 0.29 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; 2006 - 10 ; 0.29 } } ; 3 } = true', 'tointer': 'select the rows whose 2006 - 10 record is equal to 0.29 . the number of such rows is 3 .'}
eq { count { filter_eq { all_rows ; 2006 - 10 ; 0.29 } } ; 3 } = true
select the rows whose 2006 - 10 record is equal to 0.29 . 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, '2006 - 10_5': 5, '0.29_6': 6, '3_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', '2006 - 10_5': '2006 - 10', '0.29_6': '0.29', '3_7': '3'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], '2006 - 10_5': [0], '0.29_6': [0], '3_7': [2]}
['state', '1990 - 95', '1996 - 00', '2001 - 05', '2006 - 10']
[['bihar', '0.41', '0.30', '0.43', '0.88'], ['gujarat', '0.48', '0.57', '0.64', '0.69'], ['andhra pradesh', '0.53', '0.73', '0.55', '0.61'], ['punjab', '0.32', '0.46', '0.46', '0.60'], ['jammu & kashmir', '0.13', '0.32', '0.17', '0.40'], ['haryana', '0.33', '0.60', '0.31', '0.37'], ['himachal pradesh', '0.26', '0.14', '0.23', '0.35'], ['tamil nadu', '0.19', '0.20', '0.24', '0.29'], ['madhya pradesh', '0.23', '0.22', '0.31', '0.29'], ['karnataka', '0.24', '0.19', '0.20', '0.29'], ['rajasthan', '0.27', '0.23', '0.26', '0.27'], ['kerala', '0.16', '0.20', '0.22', '0.27'], ['maharashtra', '0.45', '0.29', '0.27', '0.26'], ['uttar pradesh', '0.11', '0.11', '0.16', '0.21'], ['orissa', '0.22', '0.16', '0.15', '0.19'], ['assam', '0.21', '0.02', '0.14', '0.17'], ['west bengal', '0.11', '0.08', '0.03', '0.01']]
dragons ' den ( uk )
https://en.wikipedia.org/wiki/Dragons%27_Den_%28UK%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12617978-7.html.csv
ordinal
in dragons ' den , the 2nd earliest air date was when the entrepreneur was gary taylor .
{'row': '2', 'col': '2', 'order': '2', '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', 'first aired', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first aired ; 2 }'}, 'entrepreneur ( s )'], 'result': 'gary taylor', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first aired ; 2 } ; entrepreneur ( s ) }'}, 'gary taylor'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first aired ; 2 } ; entrepreneur ( s ) } ; gary taylor } = true', 'tointer': 'select the row whose first aired record of all rows is 2nd minimum . the entrepreneur ( s ) record of this row is gary taylor .'}
eq { hop { nth_argmin { all_rows ; first aired ; 2 } ; entrepreneur ( s ) } ; gary taylor } = true
select the row whose first aired record of all rows is 2nd minimum . the entrepreneur ( s ) record of this row is gary taylor .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first aired_5': 5, '2_6': 6, 'entrepreneur (s)_7': 7, 'gary taylor_8': 8}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'first aired_5': 'first aired', '2_6': '2', 'entrepreneur (s)_7': 'entrepreneur ( s )', 'gary taylor_8': 'gary taylor'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first aired_5': [0], '2_6': [0], 'entrepreneur (s)_7': [1], 'gary taylor_8': [2]}
['episode', 'first aired', 'entrepreneur ( s )', 'company or product name', 'money requested', 'investing dragon ( s )']
[['episode 1', '3 august 2006', 'james seddon', 'eggxactly', '75000', 'richard farleigh & peter jones'], ['episode 2', '10 august 2006', 'gary taylor', 'alpine cleaning', '200000', 'deborah meaden & theo paphitis'], ['episode 3', '17 august 2006', 'matthew hazell', 'first light solutions', '100000', 'richard farleigh'], ['episode 4', '24 august 2006', 'ian chamings', 'mixalbum', '150000', 'deborah meaden & theo paphitis'], ['episode 5', '31 august 2006', 'richard lee & daren duraidi', 'dr cap', '150000', 'duncan bannatyne'], ['episode 6', '7 september 2006', 'stephen bellis', 'nuts poker league', '50000 ( but received 65000 )', 'theo paphitis & deborah meaden'], ['episode 7', '14 september 2006', 'peter sesay', 'autosafe', '100000', 'peter jones & duncan bannatyne'], ['episode 8', '21 september 2006', 'ian daintith & richard adams', 'coin metrics', '200000', 'deborah meaden & theo paphitis']]
kuomintang
https://en.wikipedia.org/wiki/Kuomintang
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16903-2.html.csv
majority
in kuomintang , there has been an a majority of at least 29000 votes .
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '29000', 'subset': None}
{'func': 'most_greater_eq', 'args': ['all_rows', 'total votes', '29000'], 'result': True, 'ind': 0, 'tointer': 'for the total votes records of all rows , most of them are greater than or equal to 29000 .', 'tostr': 'most_greater_eq { all_rows ; total votes ; 29000 } = true'}
most_greater_eq { all_rows ; total votes ; 29000 } = true
for the total votes records of all rows , most of them are greater than or equal to 29000 .
1
1
{'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'total votes_3': 3, '29000_4': 4}
{'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'total votes_3': 'total votes', '29000_4': '29000'}
{'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'total votes_3': [0], '29000_4': [0]}
['election', 'total votes', 'share of votes', 'outcome of election', 'election leader']
[['1992', '5030725', '53.0 %', '1 seats , government', 'lee teng - hui'], ['1995', '4349089', '46.1 %', '10 seats , government', 'lee teng - hui'], ['1998', '4659679', '46.4 %', '38 seats , government', 'lee teng - hui'], ['2001', '2949371', '31.3 %', '46 seats , opposition coalition ( pan - blue )', 'lien chan'], ['2004', '3190081', '34.9 %', '11 seats , opposition coalition ( pan - blue )', 'lien chan'], ['2008', '5291512', '53.5 %', '2 seats , opposition coalition ( pan - blue )', 'wu po - hsiung'], ['2012', '5863379', '44.5 %', '17 seats , government ( pan - blue )', 'ma ying - jeou']]
2007 - 08 new orleans hornets season
https://en.wikipedia.org/wiki/2007%E2%80%9308_New_Orleans_Hornets_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11963536-11.html.csv
aggregation
the average attendance for new orleans hornets games played at the at & t center was 18797 .
{'scope': 'subset', 'col': '8', 'type': 'average', 'result': '18797', 'subset': {'col': '8', 'criterion': 'fuzzily_match', 'value': 'at & t center'}}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'at & t center'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; location attendance ; at & t center }', 'tointer': 'select the rows whose location attendance record fuzzily matches to at & t center .'}, 'location attendance'], 'result': '18797', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; location attendance ; at & t center } ; location attendance }'}, '18797'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; location attendance ; at & t center } ; location attendance } ; 18797 } = true', 'tointer': 'select the rows whose location attendance record fuzzily matches to at & t center . the average of the location attendance record of these rows is 18797 .'}
round_eq { avg { filter_eq { all_rows ; location attendance ; at & t center } ; location attendance } ; 18797 } = true
select the rows whose location attendance record fuzzily matches to at & t center . the average of the location attendance record of these rows is 18797 .
3
3
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, 'at&t center_6': 6, 'location attendance_7': 7, '18797_8': 8}
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', 'at&t center_6': 'at & t center', 'location attendance_7': 'location attendance', '18797_8': '18797'}
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], 'at&t center_6': [0], 'location attendance_7': [1], '18797_8': [2]}
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series']
[['1', 'may 3', 'san antonio', '101 - 82', 'west ( 30 )', 'chandler ( 15 )', 'paul ( 13 )', 'new orleans arena 18040', '1 - 0'], ['2', 'may 5', 'san antonio', '102 - 84', 'paul ( 30 )', 'chandler ( 11 )', 'paul ( 12 )', 'new orleans arena 17927', '2 - 0'], ['3', 'may 8', 'san antonio', '99 - 110', 'paul ( 35 )', 'west ( 12 )', 'paul ( 9 )', 'at & t center 18797', '2 - 1'], ['4', 'may 11', 'san antonio', '80 - 100', 'paul ( 23 )', 'armstrong , paul ( 6 )', 'paul ( 5 )', 'at & t center 18797', '2 - 2'], ['5', 'may 13', 'san antonio', '101 - 79', 'west ( 38 )', 'west ( 14 )', 'paul ( 14 )', 'new orleans arena 18246', '3 - 2'], ['6', 'may 15', 'san antonio', '80 - 99', 'paul ( 21 )', 'five - way tie ( 6 )', 'paul ( 8 )', 'at & t center 18797', '3 - 3'], ['7', 'may 19', 'san antonio', '82 - 91', 'west ( 20 )', 'chandler ( 15 )', 'paul ( 14 )', 'new orleans arena 18235', '3 - 4']]
list of state leaders in 990s bc
https://en.wikipedia.org/wiki/List_of_state_leaders_in_990s_BC
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17310478-8.html.csv
count
three of the people held the title of duke .
{'scope': 'all', 'criterion': 'equal', 'value': 'duke', 'result': '3', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'title', 'duke'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose title record fuzzily matches to duke .', 'tostr': 'filter_eq { all_rows ; title ; duke }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; title ; duke } }', 'tointer': 'select the rows whose title record fuzzily matches to duke . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; title ; duke } } ; 3 } = true', 'tointer': 'select the rows whose title record fuzzily matches to duke . the number of such rows is 3 .'}
eq { count { filter_eq { all_rows ; title ; duke } } ; 3 } = true
select the rows whose title record fuzzily matches to duke . 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, 'title_5': 5, 'duke_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', 'title_5': 'title', 'duke_6': 'duke', '3_7': '3'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'title_5': [0], 'duke_6': [0], '3_7': [2]}
['state', 'type', 'name', 'title', 'royal house', 'from']
[['cao', 'sovereign', 'zhong', 'lord', '-', '1002 bc'], ['lu', 'sovereign', 'bo qin', 'ruler', 'ji', '1043 bc'], ['lu', 'sovereign', 'kao', 'duke', 'ji', '997 bc'], ['lu', 'sovereign', 'yang', 'duke', 'ji', '993 bc'], ['qi', 'sovereign', 'ding', 'duke', '-', '999 bc']]
wpxn - tv
https://en.wikipedia.org/wiki/WPXN-TV
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-188003-1.html.csv
unique
31.1 is the only wpxn - tv channel with a 16:9 aspect ratio .
{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': '16:9', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'aspect', '16:9'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose aspect record fuzzily matches to 16:9 .', 'tostr': 'filter_eq { all_rows ; aspect ; 16:9 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; aspect ; 16:9 } }', 'tointer': 'select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'aspect', '16:9'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose aspect record fuzzily matches to 16:9 .', 'tostr': 'filter_eq { all_rows ; aspect ; 16:9 }'}, 'channel'], 'result': '31.1', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel }'}, '31.1'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 31.1 }', 'tointer': 'the channel record of this unqiue row is 31.1 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 31.1 } } = true', 'tointer': 'select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 31.1 .'}
and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 31.1 } } = true
select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 31.1 .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'aspect_7': 7, '16:9_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'channel_9': 9, '31.1_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'aspect_7': 'aspect', '16:9_8': '16:9', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'channel_9': 'channel', '31.1_10': '31.1'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'aspect_7': [0], '16:9_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'channel_9': [2], '31.1_10': [3]}
['channel', 'video', 'aspect', 'psip short name', 'network']
[['31.1', '720p', '16:9', 'ion', 'ion television'], ['31.2', '480i', '4:3', 'qubo', 'qubo'], ['31.3', '480i', '4:3', 'ionlife', 'ion life'], ['31.4', '480i', '4:3', 'shop', 'ion shop'], ['31.5', '480i', '4:3', 'qvc', 'qvc']]
1929 vfl season
https://en.wikipedia.org/wiki/1929_VFL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10767118-15.html.csv
ordinal
princes park venue recorded the highest crowd participation during the 1929 vfl season .
{'row': '5', 'col': '6', 'order': '1', 'col_other': '5', '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', 'crowd', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crowd ; 1 }'}, 'venue'], 'result': 'princes park', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crowd ; 1 } ; venue }'}, 'princes park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park } = true', 'tointer': 'select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .'}
eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park } = true
select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, '1_6': 6, 'venue_7': 7, 'princes park_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', 'crowd_5': 'crowd', '1_6': '1', 'venue_7': 'venue', 'princes park_8': 'princes park'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], '1_6': [0], 'venue_7': [1], 'princes park_8': [2]}
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
[['melbourne', '15.17 ( 107 )', 'north melbourne', '6.14 ( 50 )', 'mcg', '8421', '10 august 1929'], ['footscray', '11.6 ( 72 )', 'richmond', '14.15 ( 99 )', 'western oval', '13000', '10 august 1929'], ['essendon', '15.10 ( 100 )', 'hawthorn', '13.14 ( 92 )', 'windy hill', '11000', '10 august 1929'], ['collingwood', '13.9 ( 87 )', 'geelong', '8.12 ( 60 )', 'victoria park', '14000', '10 august 1929'], ['carlton', '17.17 ( 119 )', 'south melbourne', '11.15 ( 81 )', 'princes park', '20000', '10 august 1929'], ['st kilda', '21.16 ( 142 )', 'fitzroy', '10.15 ( 75 )', 'junction oval', '14500', '10 august 1929']]
2001 lff lyga
https://en.wikipedia.org/wiki/2001_LFF_Lyga
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18018214-1.html.csv
superlative
in the 2001 lff lyga , the club fbk kaunas had the most goals scored .
{'scope': 'all', 'col_superlative': '7', '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', 'goals scored'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; goals scored }'}, 'club'], 'result': 'fbk kaunas', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; goals scored } ; club }'}, 'fbk kaunas'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; goals scored } ; club } ; fbk kaunas } = true', 'tointer': 'select the row whose goals scored record of all rows is maximum . the club record of this row is fbk kaunas .'}
eq { hop { argmax { all_rows ; goals scored } ; club } ; fbk kaunas } = true
select the row whose goals scored record of all rows is maximum . the club record of this row is fbk kaunas .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'goals scored_5': 5, 'club_6': 6, 'fbk kaunas_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'goals scored_5': 'goals scored', 'club_6': 'club', 'fbk kaunas_7': 'fbk kaunas'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'goals scored_5': [0], 'club_6': [1], 'fbk kaunas_7': [2]}
['position', 'club', 'games played', 'wins', 'draws', 'loses', 'goals scored', 'goals conceded', 'points']
[['1', 'fbk kaunas', '36', '26', '7', '3', '76', '13', '85'], ['2', 'fk atlantas', '36', '19', '12', '5', '66', '29', '69'], ['3', 'fk žalgiris vilnius', '36', '20', '9', '7', '64', '39', '69'], ['4', 'fk ekranas', '36', '15', '10', '11', '58', '38', '55'], ['5', 'inkaras kaunas', '36', '11', '12', '13', '50', '44', '45'], ['6', 'geležinis vilkas vilnius', '36', '10', '6', '20', '42', '69', '36'], ['7', 'nevėžis kėdainiai', '36', '8', '11', '17', '33', '54', '35'], ['8', 'sakalas šiauliai', '36', '7', '13', '16', '32', '61', '34'], ['9', 'vėtra rūdiškės', '36', '7', '11', '18', '32', '57', '32']]
1980 buffalo bills season
https://en.wikipedia.org/wiki/1980_Buffalo_Bills_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16677887-2.html.csv
superlative
in the 1980 buffalo bills season , the highest attendance was on november 23rd .
{'scope': 'all', 'col_superlative': '9', 'row_superlative': '12', '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 }'}, 'date'], 'result': 'nov 23', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, 'nov 23'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; nov 23 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is nov 23 .'}
eq { hop { argmax { all_rows ; attendance } ; date } ; nov 23 } = true
select the row whose attendance record of all rows is maximum . the date record of this row is nov 23 .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, 'nov 23_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', 'date_6': 'date', 'nov 23_7': 'nov 23'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], 'nov 23_7': [2]}
['game', 'date', 'opponent', 'result', 'bills points', 'opponents', 'bills first downs', 'record', 'attendance']
[['1', 'sept 7', 'miami dolphins', 'win', '17', '7', '22', '1 - 0', '79598'], ['2', 'sept 14', 'new york jets', 'win', '20', '10', '22', '2 - 0', '65315'], ['3', 'sept 21', 'new orleans saints', 'win', '35', '26', '26', '3 - 0', '51154'], ['4', 'sept 28', 'oakland raiders', 'win', '24', '7', '25', '4 - 0', '77259'], ['5', 'oct 5', 'san diego chargers', 'win', '26', '24', '14', '5 - 0', '51982'], ['6', 'oct 12', 'baltimore colts', 'loss', '12', '17', '24', '5 - 1', '73634'], ['7', 'oct 19', 'miami dolphins', 'loss', '14', '17', '18', '5 - 2', '41636'], ['8', 'oct 26', 'new england patriots', 'win', '31', '13', '21', '6 - 2', '75092'], ['9', 'nov 2', 'atlanta falcons', 'loss', '14', '30', '20', '6 - 3', '57959'], ['10', 'nov 9', 'new york jets', 'win', '31', '24', '17', '7 - 3', '45677'], ['11', 'nov 16', 'cincinnati bengals', 'win', '14', '0', '22', '8 - 3', '40836'], ['12', 'nov 23', 'pittsburgh steelers', 'win', '28', '13', '23', '9 - 3', '79659'], ['13', 'nov 30', 'baltimore colts', 'loss', '24', '28', '24', '9 - 4', '36184'], ['14', 'dec 7', 'los angeles rams', 'win', '10', '7', '15', '10 - 4', '77133'], ['15', 'dec 14', 'new england patriots', 'loss', '2', '24', '28', '10 - 5', '58324']]
2008 - 09 cardiff city f.c. season
https://en.wikipedia.org/wiki/2008%E2%80%9309_Cardiff_City_F.C._season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17596418-5.html.csv
count
there are 2 players loaned from aston villa .
{'scope': 'all', 'criterion': 'equal', 'value': 'aston villa', 'result': '2', 'col': '3', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'loan club', 'aston villa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose loan club record fuzzily matches to aston villa .', 'tostr': 'filter_eq { all_rows ; loan club ; aston villa }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; loan club ; aston villa } }', 'tointer': 'select the rows whose loan club record fuzzily matches to aston villa . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; loan club ; aston villa } } ; 2 } = true', 'tointer': 'select the rows whose loan club record fuzzily matches to aston villa . the number of such rows is 2 .'}
eq { count { filter_eq { all_rows ; loan club ; aston villa } } ; 2 } = true
select the rows whose loan club record fuzzily matches to aston villa . 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, 'loan club_5': 5, 'aston villa_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', 'loan club_5': 'loan club', 'aston villa_6': 'aston villa', '2_7': '2'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'loan club_5': [0], 'aston villa_6': [0], '2_7': [2]}
['name', 'country', 'loan club', 'started', 'ended', 'start source', 'end source']
[['heaton', 'eng', 'manchester united', '5 may', '30 june', 'bbc sport', 'south wales echo'], ['e johnson', 'usa', 'fulham', '22 august', '30 june', 'bbc sport', 'south wales echo'], ['chopra', 'eng', 'sunderland', '6 november', '30 december', 'bbc sport', 'bbc sport'], ['routledge', 'eng', 'aston villa', '20 november', '2 january', 'cardiff city', 'bbc sport'], ['owusu - abeyie', 'ghana', 'spartak moscow', '31 january', '30 june', 'bbc sport', 'south wales echo'], ['chopra', 'eng', 'sunderland', '2 february', '30 june', 'bbc sport', 'south wales echo'], ['konstantopoulos', 'gre', 'coventry city', '9 february', '30 june', 'bbc sport', 'south wales echo'], ['taylor', 'eng', 'aston villa', '13 march', '30 june', 'bbc sport', 'south wales echo']]
list of earthquakes in iran
https://en.wikipedia.org/wiki/List_of_earthquakes_in_Iran
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10677198-2.html.csv
superlative
1990 manjil - rudbar earthquake recorded the most earthquake fatalities in iran .
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'fatalities'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; fatalities }'}, 'name'], 'result': '1990 manjil - rudbar earthquake', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; fatalities } ; name }'}, '1990 manjil - rudbar earthquake'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; fatalities } ; name } ; 1990 manjil - rudbar earthquake } = true', 'tointer': 'select the row whose fatalities record of all rows is maximum . the name record of this row is 1990 manjil - rudbar earthquake .'}
eq { hop { argmax { all_rows ; fatalities } ; name } ; 1990 manjil - rudbar earthquake } = true
select the row whose fatalities record of all rows is maximum . the name record of this row is 1990 manjil - rudbar earthquake .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'fatalities_5': 5, 'name_6': 6, '1990 manjil - rudbar earthquake_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'fatalities_5': 'fatalities', 'name_6': 'name', '1990 manjil - rudbar earthquake_7': '1990 manjil - rudbar earthquake'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'fatalities_5': [0], 'name_6': [1], '1990 manjil - rudbar earthquake_7': [2]}
['date', 'epicenter', 'magnitude', 'fatalities', 'name']
[['march 14 , 1998', 'golbaf', '6.6', '5', '1998 golbaf earthquake'], ['may 10 , 1997', 'birjand - qaen', '7.3', '1567', '1997 qayen earthquake'], ['february 28 , 1997', 'ardabil', '6.0', '1100', '1997 ardabil earthquake'], ['june 20 , 1990', 'manjil ( - rudbar )', '7.4', 'least 40000', '1990 manjil - rudbar earthquake'], ['july 28 , 1981', 'southern iran', '7.3', '1500', 'july 1981 southern iran earthquake'], ['june 11 , 1981', 'southern iran', '6.9', '3000', 'june 1981 southern iran earthquake'], ['september 16 , 1978', 'tabas', '7.8', '15000', '1978 tabas earthquake'], ['april 10 , 1972', 'qir', '7.1', '5054', '1972 southern iran earthquake'], ['august 31 , 1968', 'dasht - e - bayaz - ferdows', '7.3', 'least 7000', '1968 dasht - e bayaz and ferdows earthquake'], ['february 10 , 1965', 'bostanabad - e bala', '5.1', '20', '1965 bostanabad - e bala earthquake'], ['september 1 , 1962', "bou'in - zahra", '7.1', '12225', "1962 bou'in - zahra earthquake"], ['december 13 , 1957', 'sahneh', '7.1', '1130', '1957 sahneh earthquake'], ['july 2 , 1957', 'm훮zandar훮n', '7.1', '1200', '1957 m훮zandar훮n earthquake'], ['february 12 , 1953', 'torud', '6.5', '970', '1953 torud earthquake'], ['august 5 , 1947', 'pasni', '7.3', '500', '1947 pasni earthquake'], ['may 6 , 1930', 'salmas', '7.2', '2 , 500', '1930 salmas earthquake'], ['may 1 , 1929', 'koppeh dagh', '7.4', '3800', '1929 koppeh dagh earthquake'], ['may 25 , 1923', 'torbat - e heydariyeh', '5.7', '2200', '1923 torbat - e heydariyeh earthquake'], ['january 23 , 1909', 'silakhor of borujerd', '7.3', '6000', '1909 borujerd earthquake']]
uci road world championships - women 's time trial
https://en.wikipedia.org/wiki/UCI_Road_World_Championships_%E2%80%93_Women%27s_time_trial
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14892457-3.html.csv
majority
of the nation listed for medalists in the uci road world championships - women 's time trial the majority have won less than 2 gold medals .
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '2', 'subset': None}
{'func': 'most_less', 'args': ['all_rows', 'gold', '2'], 'result': True, 'ind': 0, 'tointer': 'for the gold records of all rows , most of them are less than 2 .', 'tostr': 'most_less { all_rows ; gold ; 2 } = true'}
most_less { all_rows ; gold ; 2 } = true
for the gold records of all rows , most of them are less than 2 .
1
1
{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'gold_3': 3, '2_4': 4}
{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'gold_3': 'gold', '2_4': '2'}
{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'gold_3': [0], '2_4': [0]}
['rank', 'nation', 'gold', 'silver', 'bronze', 'total']
[['1', 'united states', '5', '2', '3', '10'], ['2', 'france', '4', '2', '1', '7'], ['3', 'germany', '3', '3', '3', '9'], ['4', 'netherlands', '3', '0', '0', '3'], ['5', 'switzerland', '2', '3', '1', '6'], ['6', 'russia', '1', '2', '2', '5'], ['7', 'spain', '1', '1', '1', '3'], ['8', 'united kingdom', '1', '0', '1', '2'], ['9', 'new zealand', '0', '2', '2', '4'], ['10', 'canada', '0', '2', '0', '2'], ['11', 'australia', '0', '1', '1', '2'], ['11', 'austria', '0', '1', '1', '2'], ['11', 'italy', '0', '1', '1', '2'], ['14', 'lithuania', '0', '0', '2', '2'], ['15', 'denmark', '0', '0', '1', '1']]
inbee park
https://en.wikipedia.org/wiki/Inbee_Park
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18198579-6.html.csv
superlative
the highest amount of earnings that inbee park received was in 2013 .
{'scope': 'all', 'col_superlative': '7', 'row_superlative': '10', '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', 'earnings'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; earnings }'}, 'year'], 'result': '2013', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; earnings } ; year }'}, '2013'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; earnings } ; year } ; 2013 } = true', 'tointer': 'select the row whose earnings record of all rows is maximum . the year record of this row is 2013 .'}
eq { hop { argmax { all_rows ; earnings } ; year } ; 2013 } = true
select the row whose earnings record of all rows is maximum . the year record of this row is 2013 .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'earnings_5': 5, 'year_6': 6, '2013_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'earnings_5': 'earnings', 'year_6': 'year', '2013_7': '2013'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'earnings_5': [0], 'year_6': [1], '2013_7': [2]}
['year', 'tournaments played', 'cuts made', 'wins', 'top 10s', 'best finish', 'earnings', 'money list rank', 'scoring average', 'scoring rank']
[['2004', '2', '1', '0', '1', 't8', 'n / a', 'n / a', '72.60', 'n / a'], ['2005', '2', '1', '0', '1', '5', 'n / a', 'n / a', '71.00', 'n / a'], ['2006', '2', '2', '0', '0', 't35', '5406', 'n / a', '73.86', 'n / a'], ['2007', '26', '18', '0', '2', 't2', '380263', '37', '73.19', '72'], ['2008', '26', '22', '1', '7', '1', '1138370', '8', '71.78', '26'], ['2009', '23', '16', '0', '2', 't5', '271303', '50', '72.55', '67'], ['2010', '19', '19', '0', '11', '2', '825477', '11', '70.83', '9'], ['2011', '16', '15', '0', '3', 't6', '365231', '31', '72.00', '27'], ['2012', '24', '23', '2', '12', '1', '2287080', '1', '70.21', '1'], ['2013', '21', '20', '6', '9', '1', '2335460', '1', '69.934', '3'], ['totals', '161', '137', '9', '46', 'n / a', '7603184', 'n / a', 'n / a', 'n / a']]
atlanta falcons draft history
https://en.wikipedia.org/wiki/Atlanta_Falcons_draft_history
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15198842-48.html.csv
aggregation
atlanta falcons ' two corner back picks have an average overall score of 41 .
{'scope': 'subset', 'col': '3', 'type': 'average', 'result': '41', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'cornerback'}}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'cornerback'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; position ; cornerback }', 'tointer': 'select the rows whose position record fuzzily matches to cornerback .'}, 'overall'], 'result': '41', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; position ; cornerback } ; overall }'}, '41'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; position ; cornerback } ; overall } ; 41 } = true', 'tointer': 'select the rows whose position record fuzzily matches to cornerback . the average of the overall record of these rows is 41 .'}
round_eq { avg { filter_eq { all_rows ; position ; cornerback } ; overall } ; 41 } = true
select the rows whose position record fuzzily matches to cornerback . the average of the overall record of these rows is 41 .
3
3
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'position_5': 5, 'cornerback_6': 6, 'overall_7': 7, '41_8': 8}
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'position_5': 'position', 'cornerback_6': 'cornerback', 'overall_7': 'overall', '41_8': '41'}
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'cornerback_6': [0], 'overall_7': [1], '41_8': [2]}
['round', 'pick', 'overall', 'name', 'position', 'college']
[['1', '22', '22', 'desmond trufant', 'cornerback', 'washington'], ['2', '28', '60', 'robert alford', 'cornerback', 'southeastern louisiana'], ['4', '30', '127', 'malliciah goodman', 'defensive end', 'clemson'], ['4', '36', '133', 'levine toilolo', 'tight end', 'stanford'], ['5', '20', '153', 'stansly maponga', 'defensive end', 'tcu'], ['7', '37', '243', 'kemal ishmael', 'safety', 'central florida'], ['7', '38', '244', 'zeke motta', 'safety', 'notre dame'], ['7', '43', '249', 'sean renfree', 'quarterback', 'duke']]
american idol ( season 10 )
https://en.wikipedia.org/wiki/American_Idol_%28season_10%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27455867-1.html.csv
count
there were 7 episode air dates of the american idol ( season 10 ) .
{'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '7', 'col': '1', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'episode air date'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose episode air date record is arbitrary .', 'tostr': 'filter_all { all_rows ; episode air date }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; episode air date } }', 'tointer': 'select the rows whose episode air date record is arbitrary . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; episode air date } } ; 7 } = true', 'tointer': 'select the rows whose episode air date record is arbitrary . the number of such rows is 7 .'}
eq { count { filter_all { all_rows ; episode air date } } ; 7 } = true
select the rows whose episode air date record is arbitrary . the number of such rows is 7 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'episode air date_5': 5, '7_6': 6}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'episode air date_5': 'episode air date', '7_6': '7'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'episode air date_5': [0], '7_6': [2]}
['episode air date', 'audition city', 'first audition date', 'audition venue', 'callback audition date', 'callback venue', 'golden tickets']
[['january 19 , 2011', 'east rutherford , new jersey', 'august 3 , 2010', 'izod center', 'september 28 - 30 , 2010', 'liberty house restaurant', '51'], ['january 20 , 2011', 'new orleans , louisiana', 'july 26 , 2010', 'new orleans arena', 'october 17 - 18 , 2010', 'hilton riverside hotel', '37'], ['january 26 , 2011', 'milwaukee , wisconsin', 'july 21 , 2010', 'bradley center', 'october 2 - 3 , 2010', 'milwaukee art museum', '53'], ['january 27 , 2011', 'nashville , tennessee', 'july 17 , 2010', 'bridgestone arena', 'october 25 - 26 , 2010', 'ryman auditorium', '56 1'], ['february 2 , 2011', 'austin , texas', 'august 11 , 2010', 'frank erwin center', 'october 8 - 9 , 2010', 'barton creek resort & spa', '50'], ['february 3 , 2011', 'los angeles , california', 'september 22 , 2010', 'the forum', 'november 3 - 4 , 2010', 'at & t center', '30 1'], ['february 9 , 2011', 'san francisco , california', 'august 19 , 2010', 'at & t park', 'november 9 - 10 , 2010', 'westin st francis', '47 1']]
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
aggregation
the galaxies in the dorado group have an average apparent magnitude of 11.7 .
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '11.7', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'apparent magnitude'], 'result': '11.7', 'ind': 0, 'tostr': 'avg { all_rows ; apparent magnitude }'}, '11.7'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; apparent magnitude } ; 11.7 } = true', 'tointer': 'the average of the apparent magnitude record of all rows is 11.7 .'}
round_eq { avg { all_rows ; apparent magnitude } ; 11.7 } = true
the average of the apparent magnitude record of all rows is 11.7 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'apparent magnitude_4': 4, '11.7_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'apparent magnitude_4': 'apparent magnitude', '11.7_5': '11.7'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'apparent magnitude_4': [0], '11.7_5': [1]}
['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']]
list of high schools in indiana
https://en.wikipedia.org/wiki/List_of_high_schools_in_Indiana
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1984697-85.html.csv
comparative
emmanuel christian school is less than half the size of wabash high school .
{'row_1': '1', 'row_2': '5', 'col': '4', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school', 'emmanuel christian school'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school record fuzzily matches to emmanuel christian school .', 'tostr': 'filter_eq { all_rows ; school ; emmanuel christian school }'}, 'size'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school ; emmanuel christian school } ; size }', 'tointer': 'select the rows whose school record fuzzily matches to emmanuel christian school . take the size record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school', 'wabash high school'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose school record fuzzily matches to wabash high school .', 'tostr': 'filter_eq { all_rows ; school ; wabash high school }'}, 'size'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; school ; wabash high school } ; size }', 'tointer': 'select the rows whose school record fuzzily matches to wabash high school . take the size record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; school ; emmanuel christian school } ; size } ; hop { filter_eq { all_rows ; school ; wabash high school } ; size } } = true', 'tointer': 'select the rows whose school record fuzzily matches to emmanuel christian school . take the size record of this row . select the rows whose school record fuzzily matches to wabash high school . take the size record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; school ; emmanuel christian school } ; size } ; hop { filter_eq { all_rows ; school ; wabash high school } ; size } } = true
select the rows whose school record fuzzily matches to emmanuel christian school . take the size record of this row . select the rows whose school record fuzzily matches to wabash high school . take the size 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, 'school_7': 7, 'emmanuel christian school_8': 8, 'size_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'school_11': 11, 'wabash high school_12': 12, 'size_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', 'school_7': 'school', 'emmanuel christian school_8': 'emmanuel christian school', 'size_9': 'size', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'school_11': 'school', 'wabash high school_12': 'wabash high school', 'size_13': 'size'}
{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'school_7': [0], 'emmanuel christian school_8': [0], 'size_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'school_11': [1], 'wabash high school_12': [1], 'size_13': [3]}
['school', 'city / town', 'website', 'size', 'principal', 'grades', 'idoe profile']
[['emmanuel christian school', 'wabash', '-', '105', 'doug phillips', 'pk - 12', 'snapshot'], ['manchester junior - senior high school', 'north manchester', 'website', '715', 'ms nancy alspaugh', '07 - 12', 'snapshot'], ['northfield junior - senior high school', 'wabash', 'website', '604', 'mike keaffaber', '07 - 12', 'snapshot'], ['southwood junior - senior high school', 'wabash', 'website', '634', 'tim drake', '07 - 12', 'snapshot'], ['wabash high school', 'wabash', 'website', '462', 'josh blossom', '09 - 12', 'snapshot']]
patty schnyder
https://en.wikipedia.org/wiki/Patty_Schnyder
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1547798-3.html.csv
count
patty schnyder played against ágnes szávay in the final of tournaments a total of two times .
{'scope': 'all', 'criterion': 'equal', 'value': 'ágnes szávay', 'result': '2', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent in the final', 'ágnes szávay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent in the final record fuzzily matches to ágnes szávay .', 'tostr': 'filter_eq { all_rows ; opponent in the final ; ágnes szávay }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; opponent in the final ; ágnes szávay } }', 'tointer': 'select the rows whose opponent in the final record fuzzily matches to ágnes szávay . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; opponent in the final ; ágnes szávay } } ; 2 } = true', 'tointer': 'select the rows whose opponent in the final record fuzzily matches to ágnes szávay . the number of such rows is 2 .'}
eq { count { filter_eq { all_rows ; opponent in the final ; ágnes szávay } } ; 2 } = true
select the rows whose opponent in the final record fuzzily matches to ágnes szávay . 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, 'opponent in the final_5': 5, 'ágnes szávay_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', 'opponent in the final_5': 'opponent in the final', 'ágnes szávay_6': 'ágnes szávay', '2_7': '2'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'opponent in the final_5': [0], 'ágnes szávay_6': [0], '2_7': [2]}
['date', 'tournament', 'surface', 'opponent in the final', 'score']
[['15 september 1996', 'karlovy vary , czech republic', 'clay', 'ruxandra dragomir', '6 - 2 , 3 - 6 , 6 - 4'], ['28 september 1998', 'munich , germany', 'hard ( i )', 'venus williams', '6 - 2 , 3 - 6 , 6 - 2'], ['16 july 2000', 'klagenfurt , austria', 'clay', 'barbara schett', '5 - 7 , 6 - 4 , 6 - 4'], ['12 july 2001', 'vienna , austria', 'clay', 'iroda tulyaganova', '6 - 3 , 6 - 2'], ['21 april 2002', 'hilton head , south carolina , usa', 'clay', 'iva majoli', '7 - 6 ( 5 ) , 6 - 4'], ['15 may 2005', 'rome , italy', 'clay', 'amélie mauresmo', '2 - 6 , 6 - 3 , 6 - 4'], ['23 october 2005', 'zürich , switzerland', 'carpet ( i )', 'lindsay davenport', '7 - 6 ( 5 ) , 6 - 3'], ['30 october 2005', 'linz , austria', 'hard ( i )', 'nadia petrova', '4 - 6 , 6 - 3 , 6 - 1'], ['16 april 2006', 'charleston , south carolina , usa', 'clay', 'nadia petrova', '6 - 3 , 4 - 6 , 6 - 1'], ['30 july 2006', 'stanford , california , usa', 'hard', 'kim clijsters', '6 - 4 , 6 - 2'], ['16 april 2007', 'san diego , california , usa', 'hard', 'maria sharapova', '6 - 2 , 3 - 6 , 6 - 0'], ['28 october 2007', 'linz , austria', 'hard ( i )', 'daniela hantuchová', '6 - 4 , 6 - 2'], ['9 march 2008', 'bangalore , india', 'hard', 'serena williams', '7 - 5 , 6 - 3'], ['12 july 2009', 'budapest , hungary', 'clay', 'ágnes szávay', '2 - 6 , 6 - 4 , 6 - 2'], ['11 july 2010', 'budapest , hungary', 'clay', 'ágnes szávay', '6 - 2 , 6 - 4'], ['17 october 2010', 'linz , austria', 'hard ( i )', 'ana ivanovic', '6 - 1 , 6 - 2']]
toronto raptors all - time roster
https://en.wikipedia.org/wiki/Toronto_Raptors_all-time_roster
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10015132-11.html.csv
count
2 players on the toronto raptors all - time roster play the forward - center position .
{'scope': 'all', 'criterion': 'equal', 'value': 'forward-center', 'result': '2', 'col': '3', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'forward-center'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to forward-center .', 'tostr': 'filter_eq { all_rows ; position ; forward-center }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; forward-center } }', 'tointer': 'select the rows whose position record fuzzily matches to forward-center . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; forward-center } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to forward-center . the number of such rows is 2 .'}
eq { count { filter_eq { all_rows ; position ; forward-center } } ; 2 } = true
select the rows whose position record fuzzily matches to forward-center . the number of such rows is 2 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'position_5': 5, 'forward-center_6': 6, '2_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'position_5': 'position', 'forward-center_6': 'forward-center', '2_7': '2'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'forward-center_6': [0], '2_7': [2]}
['player', 'nationality', 'position', 'years in toronto', 'school / club team']
[['antonio lang', 'united states', 'guard - forward', '1999 - 2000', 'duke'], ['voshon lenard', 'united states', 'guard', '2002 - 03', 'minnesota'], ['martin lewis', 'united states', 'guard - forward', '1996 - 97', 'butler cc ( ks )'], ['brad lohaus', 'united states', 'forward - center', '1996', 'iowa'], ['art long', 'united states', 'forward - center', '2002 - 03', 'cincinnati'], ['john long', 'united states', 'guard', '1996 - 97', 'detroit'], ['kyle lowry', 'united states', 'guard', '2012 - present', 'villanova'], ['john lucas iii', 'united states', 'guard', '2012 - 2013', 'oklahoma state']]
2007 formula nippon season
https://en.wikipedia.org/wiki/2007_Formula_Nippon_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14330477-2.html.csv
unique
only one race was run in the month of may .
{'scope': 'all', 'row': '3', 'col': '3', 'col_other': 'n/a', 'criterion': 'fuzzily_match', 'value': 'may', 'subset': None}
{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'may'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to may .', 'tostr': 'filter_eq { all_rows ; date ; may }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; date ; may } } = true', 'tointer': 'select the rows whose date record fuzzily matches to may . there is only one such row in the table .'}
only { filter_eq { all_rows ; date ; may } } = true
select the rows whose date record fuzzily matches to may . 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, 'date_4': 4, 'may_5': 5}
{'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'may_5': 'may'}
{'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'may_5': [0]}
['round', 'track', 'date', 'pole position', 'fastest race lap', 'winner', 'team']
[['1', 'fuji', '1 april', 'benoît tréluyer', 'benoît tréluyer', 'benoît tréluyer', 'team impul'], ['2', 'suzuka', '15 april', 'tsugio matsuda', 'hiroki yoshimoto', 'satoshi motoyama', 'team impul'], ['3', 'motegi', '20 may', 'tsugio matsuda', 'takashi kogure', 'takashi kogure', 'nakajima racing'], ['4', 'okayama', '10 june', 'takashi kogure', 'tsugio matsuda', 'ronnie quintarelli', 'team boss inging'], ['5', 'suzuka', '8 july', 'tsugio matsuda', 'tsugio matsuda', 'satoshi motoyama', 'team impul'], ['6', 'fuji', '26 august', 'satoshi motoyama', 'loïc duval', 'andré lotterer', "tom 's racing"], ['7', 'sugo', '16 september', 'takashi kogure', 'naoki yokomizo', 'takashi kogure', 'nakajima racing'], ['8', 'motegi', '21 october', 'takashi kogure', 'takashi kogure', 'takashi kogure', 'nakajima racing'], ['9', 'suzuka', '18 november', 'takashi kogure', 'andré lotterer', 'satoshi motoyama', 'team impul']]
melville , saskatchewan
https://en.wikipedia.org/wiki/Melville%2C_Saskatchewan
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1034685-1.html.csv
count
harvard broadcasting is the owner of two radio channels in melville , saskatchewan .
{'scope': 'all', 'criterion': 'equal', 'value': 'harvard broadcasting', 'result': '2', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'owner', 'harvard broadcasting'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose owner record fuzzily matches to harvard broadcasting .', 'tostr': 'filter_eq { all_rows ; owner ; harvard broadcasting }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; owner ; harvard broadcasting } }', 'tointer': 'select the rows whose owner record fuzzily matches to harvard broadcasting . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; owner ; harvard broadcasting } } ; 2 } = true', 'tointer': 'select the rows whose owner record fuzzily matches to harvard broadcasting . the number of such rows is 2 .'}
eq { count { filter_eq { all_rows ; owner ; harvard broadcasting } } ; 2 } = true
select the rows whose owner record fuzzily matches to harvard broadcasting . the number of such rows is 2 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'owner_5': 5, 'harvard broadcasting_6': 6, '2_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'owner_5': 'owner', 'harvard broadcasting_6': 'harvard broadcasting', '2_7': '2'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'owner_5': [0], 'harvard broadcasting_6': [0], '2_7': [2]}
['frequency', 'call sign', 'branding', 'format', 'owner']
[['am 940', 'cjgx', 'gx94', 'country music', 'harvard broadcasting'], ['fm 91.7', 'cbk - fm3', 'cbc radio 2', 'public broadcasting', 'canadian broadcasting corporation'], ['fm 92.9', 'cjlr - fm - 5', 'mbc radio', 'first nationscommunity radio', 'missinipi broadcasting corporation'], ['fm 94.1', 'cfgw - fm', 'fox fm', 'hot adult contemporary', 'harvard broadcasting'], ['fm 98.5', 'cjjc - fm', '98.5 the rock', 'christian music', 'dennis m dyck']]
2007 - 08 fis ski jumping world cup
https://en.wikipedia.org/wiki/2007%E2%80%9308_FIS_Ski_Jumping_World_Cup
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14407512-23.html.csv
aggregation
the average overall nt points for competitors in the 2007 - 08 fis ski jumping world cup was 375.6 .
{'scope': 'all', 'col': '7', 'type': 'average', 'result': '375.6', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'overall nt points'], 'result': '375.6', 'ind': 0, 'tostr': 'avg { all_rows ; overall nt points }'}, '375.6'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; overall nt points } ; 375.6 } = true', 'tointer': 'the average of the overall nt points record of all rows is 375.6 .'}
round_eq { avg { all_rows ; overall nt points } ; 375.6 } = true
the average of the overall nt points record of all rows is 375.6 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'overall nt points_4': 4, '375.6_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'overall nt points_4': 'overall nt points', '375.6_5': '375.6'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'overall nt points_4': [0], '375.6_5': [1]}
['rank', 'name', 'nationality', '1st ( m )', '2nd ( m )', 'points', 'overall nt points', 'overall wc points ( rank )']
[['1', 'janne ahonen', 'fin', '122.5', '126.0', '248.3', '378.7 ( 2 )', '1098 ( 3 )'], ['2', 'anders bardal', 'nor', '117.5', '128.0', '240.4', '373.0 ( 5 )', '788 ( 5 )'], ['3', 'tom hilde', 'nor', '121.5', '122.5', '237.2', '373.2 ( 4 )', '1027 ( 4 )'], ['4', 'gregor schlierenzauer', 'aut', '114.5', '129.0', '236.8', '374.4 ( 3 )', '1161 ( 2 )'], ['5', 'janne happonen', 'fin', '118.0', '125.5', '236.3', '378.9 ( 1 )', '554 ( 11 )']]
1945 vfl season
https://en.wikipedia.org/wiki/1945_VFL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809271-8.html.csv
count
in the 1945 vfl season , among the games where home team scored more than 15 points ( 15.00 ) , two of the games drew attendance over 10,000 .
{'scope': 'subset', 'criterion': 'greater_than', 'value': '10000', 'result': '2', 'col': '6', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': '15'}}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'home team score', '15'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; home team score ; 15 }', 'tointer': 'select the rows whose home team score record is greater than 15 .'}, 'crowd', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team score record is greater than 15 . among these rows , select the rows whose crowd record is greater than 10000 .', 'tostr': 'filter_greater { filter_greater { all_rows ; home team score ; 15 } ; crowd ; 10000 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater { all_rows ; home team score ; 15 } ; crowd ; 10000 } }', 'tointer': 'select the rows whose home team score record is greater than 15 . among these rows , select the rows whose crowd record is greater than 10000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater { all_rows ; home team score ; 15 } ; crowd ; 10000 } } ; 2 } = true', 'tointer': 'select the rows whose home team score record is greater than 15 . among these rows , select the rows whose crowd record is greater than 10000 . the number of such rows is 2 .'}
eq { count { filter_greater { filter_greater { all_rows ; home team score ; 15 } ; crowd ; 10000 } } ; 2 } = true
select the rows whose home team score record is greater than 15 . among these rows , select the rows whose crowd record is greater than 10000 . the number of such rows is 2 .
4
4
{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'home team score_6': 6, '15_7': 7, 'crowd_8': 8, '10000_9': 9, '2_10': 10}
{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'home team score_6': 'home team score', '15_7': '15', 'crowd_8': 'crowd', '10000_9': '10000', '2_10': '2'}
{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'home team score_6': [0], '15_7': [0], 'crowd_8': [1], '10000_9': [1], '2_10': [3]}
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
[['richmond', '16.15 ( 111 )', 'st kilda', '13.6 ( 84 )', 'punt road oval', '15000', '9 june 1945'], ['south melbourne', '12.16 ( 88 )', 'melbourne', '9.4 ( 58 )', 'junction oval', '18000', '9 june 1945'], ['north melbourne', '16.10 ( 106 )', 'hawthorn', '10.8 ( 68 )', 'arden street oval', '7000', '9 june 1945'], ['footscray', '12.15 ( 87 )', 'essendon', '7.14 ( 56 )', 'western oval', '23000', '9 june 1945'], ['fitzroy', '23.15 ( 153 )', 'collingwood', '8.10 ( 58 )', 'brunswick street oval', '20000', '9 june 1945'], ['geelong', '10.9 ( 69 )', 'carlton', '14.21 ( 105 )', 'kardinia park', '11000', '9 june 1945']]
list of tallest buildings in germany
https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Germany
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11328656-3.html.csv
superlative
the commerzbank tower has the most floors than any of the other buildings in germany .
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'floors'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; floors }'}, 'name'], 'result': 'commerzbank tower', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; floors } ; name }'}, 'commerzbank tower'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; floors } ; name } ; commerzbank tower } = true', 'tointer': 'select the row whose floors record of all rows is maximum . the name record of this row is commerzbank tower .'}
eq { hop { argmax { all_rows ; floors } ; name } ; commerzbank tower } = true
select the row whose floors record of all rows is maximum . the name record of this row is commerzbank 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, 'commerzbank 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', 'commerzbank tower_7': 'commerzbank tower'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'floors_5': [0], 'name_6': [1], 'commerzbank tower_7': [2]}
['name', 'city', 'height ( m )', 'height ( ft )', 'floors', 'years as tallest']
[['commerzbank tower', 'frankfurt', '259', '850', '56', '1997 - present'], ['messeturm', 'frankfurt', '257', '843', '55', '1990 - 1997'], ['silberturm', 'frankfurt', '166', '545', '32', '1978 - 1990'], ['westend gate', 'frankfurt', '159', '522', '47', '1976 - 1978'], ['colonia - hochhaus', 'cologne', '147', '482', '42', '1973 - 1976'], ['city - hochhaus leipzig', 'leipzig', '143', '468', '36', '1972 - 1973'], ['bayer - hochhaus', 'leverkusen', '122', '400', '29', '1963 - 1972'], ['friedrich - engelhorn - hochhaus', 'ludwigshafen', '102', '335', '28', '1957 - 1963']]
list of ministers for the police force of luxembourg
https://en.wikipedia.org/wiki/List_of_Ministers_for_the_Police_Force_of_Luxembourg
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16620096-1.html.csv
superlative
of the ministers for the police force of luxembourg , the one with the earliest start date was eugène schaus .
{'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'start date'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; start date }'}, 'minister'], 'result': 'eugène schaus', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; start date } ; minister }'}, 'eugène schaus'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; start date } ; minister } ; eugène schaus } = true', 'tointer': 'select the row whose start date record of all rows is minimum . the minister record of this row is eugène schaus .'}
eq { hop { argmin { all_rows ; start date } ; minister } ; eugène schaus } = true
select the row whose start date record of all rows is minimum . the minister record of this row is eugène schaus .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'start date_5': 5, 'minister_6': 6, 'eugène schaus_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'start date_5': 'start date', 'minister_6': 'minister', 'eugène schaus_7': 'eugène schaus'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'start date_5': [0], 'minister_6': [1], 'eugène schaus_7': [2]}
['minister', 'party', 'start date', 'end date', 'prime minister']
[['eugène schaus', 'dp', '6 february 1969', '15 june 1974', 'pierre werner'], ['émile krieps', 'dp', '15 june 1974', '16 july 1979', 'gaston thorn'], ['émile krieps', 'dp', '16 july 1979', '20 july 1984', 'pierre werner'], ['marc fischbach', 'csv', '20 july 1984', '14 july 1989', 'jacques santer'], ['jacques poos', 'lsap', '14 july 1989', '13 july 1994', 'jacques santer'], ['alex bodry', 'lsap', '13 july 1994', '26 january 1995', 'jacques santer'], ['alex bodry', 'lsap', '26 january 1995', '7 august 1999', 'jean - claude juncker']]
2008 indiana fever season
https://en.wikipedia.org/wiki/2008_Indiana_Fever_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17104539-10.html.csv
comparative
the indian fever 's game on july 16 had a higher attendance than the game on july 18 .
{'row_1': '5', 'row_2': '6', 'col': '8', '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', 'date', 'july 16'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to july 16 .', 'tostr': 'filter_eq { all_rows ; date ; july 16 }'}, 'location / attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; july 16 } ; location / attendance }', 'tointer': 'select the rows whose date record fuzzily matches to july 16 . take the location / attendance record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'july 18'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to july 18 .', 'tostr': 'filter_eq { all_rows ; date ; july 18 }'}, 'location / attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; july 18 } ; location / attendance }', 'tointer': 'select the rows whose date record fuzzily matches to july 18 . take the location / attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; july 16 } ; location / attendance } ; hop { filter_eq { all_rows ; date ; july 18 } ; location / attendance } } = true', 'tointer': 'select the rows whose date record fuzzily matches to july 16 . take the location / attendance record of this row . select the rows whose date record fuzzily matches to july 18 . take the location / attendance record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; date ; july 16 } ; location / attendance } ; hop { filter_eq { all_rows ; date ; july 18 } ; location / attendance } } = true
select the rows whose date record fuzzily matches to july 16 . take the location / attendance record of this row . select the rows whose date record fuzzily matches to july 18 . take the location / attendance record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'july 16_8': 8, 'location / attendance_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'july 18_12': 12, 'location / attendance_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'july 16_8': 'july 16', 'location / attendance_9': 'location / attendance', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'july 18_12': 'july 18', 'location / attendance_13': 'location / attendance'}
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'july 16_8': [0], 'location / attendance_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'july 18_12': [1], 'location / attendance_13': [3]}
['game', 'date', 'opponent', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record']
[['16', 'july 2', 'chicago', 'w 74 - 67', 'catchings ( 18 )', 'sutton - brown ( 12 )', 'catchings , douglas ( 3 )', 'conseco fieldhouse 6196', '8 - 8'], ['17', 'july 5', 'connecticut', 'w 81 - 74', 'douglas , sutton - brown ( 18 )', 'sutton - brown ( 9 )', 'douglas ( 5 )', 'conseco fieldhouse 6329', '9 - 8'], ['18', 'july 8', 'washington', 'l 50 - 48', 'hoffman ( 16 )', 'hoffman ( 9 )', 'bevilaqua ( 4 )', 'verizon center 7587', '9 - 9'], ['19', 'july 12', 'chicago', 'w 66 - 57', 'douglas ( 25 )', 'catchings ( 8 )', 'catchings ( 4 )', 'conseco fieldhouse 7134', '10 - 9'], ['20', 'july 16', 'atlanta', 'l 81 - 77', 'catchings ( 18 )', 'catchings ( 12 )', 'catchings ( 5 )', 'conseco fieldhouse 9303', '10 - 10'], ['21', 'july 18', 'seattle', 'l 65 - 59', 'sutton - brown ( 12 )', 'sutton - brown ( 7 )', 'bevilaqua , bond ( 3 )', 'conseco fieldhouse 7450', '10 - 11'], ['22', 'july 19', 'new york liberty outdoor classic', 'w 71 - 55', 'douglas ( 20 )', 'catchings , sutton - brown ( 9 )', 'catchings , douglas ( 4 )', 'arthur ashe stadium 19393', '11 - 11'], ['23', 'july 22', 'chicago', 'l 68 - 60', 'douglas , sutton - brown ( 14 )', 'sutton - brown ( 10 )', 'catchings ( 4 )', 'uic pavilion 3035', '11 - 12'], ['24', 'july 24', 'minnesota', 'l 84 - 80', 'catchings , hoffman ( 17 )', 'sutton - brown ( 9 )', 'catchings ( 9 )', 'conseco fieldhouse 6010', '11 - 13'], ['25', 'july 26', 'sacramento', 'l 70 - 62', 'douglas ( 23 )', 'hoffman ( 8 )', 'catchings , white ( 4 )', 'arco arena 7082', '11 - 14']]
list of intel pentium dual - core microprocessors
https://en.wikipedia.org/wiki/List_of_Intel_Pentium_Dual-Core_microprocessors
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11602313-4.html.csv
count
5 of the 6 processors were included as oem .
{'scope': 'all', 'criterion': 'equal', 'value': 'oem', 'result': '5', 'col': '12', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'release price ( usd )', 'oem'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose release price ( usd ) record fuzzily matches to oem .', 'tostr': 'filter_eq { all_rows ; release price ( usd ) ; oem }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; release price ( usd ) ; oem } }', 'tointer': 'select the rows whose release price ( usd ) record fuzzily matches to oem . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; release price ( usd ) ; oem } } ; 5 } = true', 'tointer': 'select the rows whose release price ( usd ) record fuzzily matches to oem . the number of such rows is 5 .'}
eq { count { filter_eq { all_rows ; release price ( usd ) ; oem } } ; 5 } = true
select the rows whose release price ( usd ) record fuzzily matches to oem . 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, 'release price ( usd )_5': 5, 'oem_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', 'release price ( usd )_5': 'release price ( usd )', 'oem_6': 'oem', '5_7': '5'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'release price ( usd )_5': [0], 'oem_6': [0], '5_7': [2]}
['model number', 'sspec number', 'frequency', 'l2 cache', 'fsb', 'mult', 'voltage', 'tdp', 'socket', 'release date', 'part number ( s )', 'release price ( usd )']
[['pentium dual - core t2310', 'slaec ( m0 )', '1.47 ghz', '1 mb', '533 mt / s', '11', '1.075 - 1.175 v', '35 w', 'socket p', 'q4 2007', 'lf80537 ge0201 m', '90'], ['pentium dual - core t2330', 'sla4k ( m0 )', '1.6 ghz', '1 mb', '533 mt / s', '12', '1.075 - 1.175 v', '35 w', 'socket p', 'q4 2007', 'lf80537 ge0251 mn', 'oem'], ['pentium dual - core t2370', 'sla4j ( m0 )', '1.73 ghz', '1 mb', '533 mt / s', '13', '1.075 - 1.175 v', '35 w', 'socket p', 'q4 2007', 'lf80537 ge0301 m', 'oem'], ['pentium dual - core t2390', 'sla4h ( m0 )', '1.87 ghz', '1 mb', '533 mt / s', '14', '1.075 - 1.175 v', '35 w', 'socket p', 'q2 2008', 'lf80537 ge0361 m', 'oem'], ['pentium dual - core t2410', 'sla4 g ( m0 )', '2 ghz', '1 mb', '533 mt / s', '15', '1.075 - 1.175 v', '35 w', 'socket p', 'q3 2008', 'lf80537 ge0411 m', 'oem'], ['pentium dual - core t3200', 'slavg ( m0 )', '2 ghz', '1 mb', '667 mt / s', '12', '1.075 - 1.175 v', '35 w', 'socket p', 'q4 2008', 'lf80537 gf0411 m', 'oem']]
1956 formula one season
https://en.wikipedia.org/wiki/1956_Formula_One_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140112-5.html.csv
ordinal
the iv glover trophywas the second earliest race in the 1956 formula one season .
{'row': '2', 'col': '3', 'order': '2', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'race name'], 'result': 'iv glover trophy', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; race name }'}, 'iv glover trophy'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; race name } ; iv glover trophy } = true', 'tointer': 'select the row whose date record of all rows is 2nd minimum . the race name record of this row is iv glover trophy .'}
eq { hop { nth_argmin { all_rows ; date ; 2 } ; race name } ; iv glover trophy } = true
select the row whose date record of all rows is 2nd minimum . the race name record of this row is iv glover trophy .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'race name_7': 7, 'iv glover trophy_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', 'date_5': 'date', '2_6': '2', 'race name_7': 'race name', 'iv glover trophy_8': 'iv glover trophy'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'race name_7': [1], 'iv glover trophy_8': [2]}
['race name', 'circuit', 'date', 'winning driver', 'constructor', 'report']
[['x gran premio ciudad de buenos aires', 'mendoza', '5 february', 'juan manuel fangio', 'lancia - ferrari', 'report'], ['iv glover trophy', 'goodwood', '2 april', 'stirling moss', 'maserati', 'report'], ['vi gran premio di siracusa', 'syracuse', '15 april', 'juan manuel fangio', 'lancia - ferrari', 'report'], ['xi barc aintree 200', 'aintree', '21 april', 'stirling moss', 'maserati', 'report'], ['vii brdc international trophy', 'silverstone', '5 may', 'stirling moss', 'vanwall', 'report'], ['ix gran premio di napoli', 'posillipo', '6 may', 'robert manzon', 'gordini', 'report'], ['i aintree 100', 'aintree', '24 june', 'horace gould', 'maserati', 'report'], ['i vanwall trophy', 'snetterton', '22 july', 'roy salvadori', 'maserati', 'report'], ['iv grand prix de caen', 'caen', '26 august', 'harry schell', 'maserati', 'report'], ['ii sussex trophy', 'goodwood', '8 september', 'roy salvadori', 'cooper - climax', 'report'], ['i brscc formula 1 race', 'brands hatch', '14 october', 'archie scott brown', 'connaught - alta', 'report']]
2005 masters tournament
https://en.wikipedia.org/wiki/2005_Masters_Tournament
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16147528-5.html.csv
aggregation
in the 2005 masters tournament , the average number of strokes to par was -3.4 .
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '-3.4', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'to par'], 'result': '-3.4', 'ind': 0, 'tostr': 'avg { all_rows ; to par }'}, '-3.4'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; to par } ; -3.4 } = true', 'tointer': 'the average of the to par record of all rows is -3.4 .'}
round_eq { avg { all_rows ; to par } ; -3.4 } = true
the average of the to par record of all rows is -3.4 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'to par_4': 4, '-3.4_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'to par_4': 'to par', '-3.4_5': '-3.4'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'to par_4': [0], '-3.4_5': [1]}
['place', 'player', 'country', 'score', 'to par']
[['1', 'chris dimarco', 'united states', '67 + 67 = 134', '- 10'], ['2', 'thomas bjørn', 'denmark', '71 + 67 = 138', '- 6'], ['3', 'tiger woods', 'united states', '74 + 66 = 140', '- 4'], ['t4', 'david howell', 'england', '72 + 69 = 141', '- 3'], ['t4', 'vijay singh', 'fiji', '68 + 73 = 141', '- 3'], ['t6', 'mark hensby', 'australia', '69 + 73 = 142', '- 2'], ['t6', 'phil mickelson', 'united states', '70 + 72 = 142', '- 2'], ['t6', 'ryan moore ( a )', 'united states', '71 + 71 = 142', '- 2'], ['t9', 'jim furyk', 'united states', '76 + 67 = 143', '- 1'], ['t9', 'kirk triplett', 'united states', '75 + 68 = 143', '- 1']]
tourism
https://en.wikipedia.org/wiki/Tourism
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29789-3.html.csv
aggregation
the average international expenditure in 2011 for the top 9 countries was about 45-48 billion .
{'scope': 'all', 'col': '4', 'type': 'average', 'result': '45-48 billion', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'international tourism expenditure 2011'], 'result': '45-48 billion', 'ind': 0, 'tostr': 'avg { all_rows ; international tourism expenditure 2011 }'}, '45-48 billion'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; international tourism expenditure 2011 } ; 45-48 billion } = true', 'tointer': 'the average of the international tourism expenditure 2011 record of all rows is 45-48 billion .'}
round_eq { avg { all_rows ; international tourism expenditure 2011 } ; 45-48 billion } = true
the average of the international tourism expenditure 2011 record of all rows is 45-48 billion .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'international tourism expenditure 2011_4': 4, '45-48 billion_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'international tourism expenditure 2011_4': 'international tourism expenditure 2011', '45-48 billion_5': '45-48 billion'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'international tourism expenditure 2011_4': [0], '45-48 billion_5': [1]}
['rank 2012', 'country', 'unwto region', 'international tourism expenditure 2011', 'international tourism expenditure 2012', '% change']
[['1', 'china', 'asia', '72.3 billion', '102.0 billion', '40.5'], ['2', 'germany', 'europe', '85.9 billion', '83.8 billion', '2.4'], ['3', 'united states', 'north america', '78.7 billion', '83.7 billion', '6.6'], ['4', 'united kingdom', 'europe', '51.0 billion', '52.3 billion', '2.5'], ['5', 'russia', 'europe', '32.5 billion', '42.8 billion', '31.6'], ['6', 'france', 'europe', '44.1 billion', '38.1 billion', '13.6'], ['7', 'canada', 'north america', '33.3 billion', '35.2 billion', '5.7'], ['8', 'japan', 'asia', '27.2 billion', '28.1 billion', '3.3'], ['9', 'australia', 'oceania', '26.7 billion', '27.6 billion', '3.4']]
cale yarborough
https://en.wikipedia.org/wiki/Cale_Yarborough
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1145778-1.html.csv
comparative
cal yarborough finished more laps in 1967 than he finished in 1971 .
{'row_1': '2', 'row_2': '3', 'col': '6', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1967'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1967 .', 'tostr': 'filter_eq { all_rows ; year ; 1967 }'}, 'laps'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1967 } ; laps }', 'tointer': 'select the rows whose year record fuzzily matches to 1967 . take the laps record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1971'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1971 .', 'tostr': 'filter_eq { all_rows ; year ; 1971 }'}, 'laps'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1971 } ; laps }', 'tointer': 'select the rows whose year record fuzzily matches to 1971 . take the laps record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 1967 } ; laps } ; hop { filter_eq { all_rows ; year ; 1971 } ; laps } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1967 . take the laps record of this row . select the rows whose year record fuzzily matches to 1971 . take the laps record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; year ; 1967 } ; laps } ; hop { filter_eq { all_rows ; year ; 1971 } ; laps } } = true
select the rows whose year record fuzzily matches to 1967 . take the laps record of this row . select the rows whose year record fuzzily matches to 1971 . take the laps record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1967_8': 8, 'laps_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1971_12': 12, 'laps_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1967_8': '1967', 'laps_9': 'laps', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1971_12': '1971', 'laps_13': 'laps'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1967_8': [0], 'laps_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1971_12': [1], 'laps_13': [3]}
['year', 'start', 'qual', 'rank', 'finish', 'laps']
[['1966', '24', '159.794', '15', '28', '0'], ['1967', '20', '162.830', '30', '17', '176'], ['1971', '14', '170.770', '19', '16', '140'], ['1972', '32', '178.864', '33', '10', '193']]
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
majority
most of the incumbents of the 1810 united states house of representatives elections were from the democratic - republican party .
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'democratic - republican', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'party', 'democratic - republican'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , most of them fuzzily match to democratic - republican .', 'tostr': 'most_eq { all_rows ; party ; democratic - republican } = true'}
most_eq { all_rows ; party ; democratic - republican } = true
for the party records of all rows , most of them fuzzily match to democratic - republican .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democratic - republican_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democratic - republican_4': 'democratic - republican'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democratic - republican_4': [0]}
['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 %']]
2008 - 09 2 . bundesliga
https://en.wikipedia.org/wiki/2008%E2%80%9309_2._Bundesliga
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17327260-3.html.csv
unique
uwe wolf ( interim ) was the only manager whose manner of departure was released from duties in the 2008 - 09 2 . bundesliga season .
{'scope': 'all', 'row': '12', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'released from duties', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manner of departure', 'released from duties'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manner of departure record fuzzily matches to released from duties .', 'tostr': 'filter_eq { all_rows ; manner of departure ; released from duties }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; manner of departure ; released from duties } }', 'tointer': 'select the rows whose manner of departure record fuzzily matches to released from duties . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manner of departure', 'released from duties'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manner of departure record fuzzily matches to released from duties .', 'tostr': 'filter_eq { all_rows ; manner of departure ; released from duties }'}, 'outgoing manager'], 'result': 'uwe wolf ( interim )', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; manner of departure ; released from duties } ; outgoing manager }'}, 'uwe wolf ( interim )'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; manner of departure ; released from duties } ; outgoing manager } ; uwe wolf ( interim ) }', 'tointer': 'the outgoing manager record of this unqiue row is uwe wolf ( interim ) .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; manner of departure ; released from duties } } ; eq { hop { filter_eq { all_rows ; manner of departure ; released from duties } ; outgoing manager } ; uwe wolf ( interim ) } } = true', 'tointer': 'select the rows whose manner of departure record fuzzily matches to released from duties . there is only one such row in the table . the outgoing manager record of this unqiue row is uwe wolf ( interim ) .'}
and { only { filter_eq { all_rows ; manner of departure ; released from duties } } ; eq { hop { filter_eq { all_rows ; manner of departure ; released from duties } ; outgoing manager } ; uwe wolf ( interim ) } } = true
select the rows whose manner of departure record fuzzily matches to released from duties . there is only one such row in the table . the outgoing manager record of this unqiue row is uwe wolf ( interim ) .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'manner of departure_7': 7, 'released from duties_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'outgoing manager_9': 9, 'uwe wolf (interim)_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'manner of departure_7': 'manner of departure', 'released from duties_8': 'released from duties', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'outgoing manager_9': 'outgoing manager', 'uwe wolf (interim)_10': 'uwe wolf ( interim )'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'manner of departure_7': [0], 'released from duties_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'outgoing manager_9': [2], 'uwe wolf (interim)_10': [3]}
['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment']
[['1 . fc nuremberg', 'thomas von heesen', 'resigned', '28 august 2008', 'michael oenning', '5 september 2008'], ['msv duisburg', 'rudolf bommer', 'sacked', '9 november 2008', 'peter neururer', '16 november 2008'], ['fc hansa rostock', 'frank pagelsdorf', 'sacked', '10 november 2008', 'dieter eilts', '21 november 2008'], ['sv wehen wiesbaden', 'christian hock', 'sacked', '17 december 2008', 'wolfgang frank', '19 december 2008'], ['tsv 1860 munich', 'marco kurz', 'sacked', '24 february 2009', 'uwe wolf ( interim )', '24 february 2009'], ['rot weiss ahlen', 'christian wück', 'sacked', '3 march 2009', 'stefan emmerling', '16 april 2009'], ['fc hansa rostock', 'dieter eilts', 'sacked', '6 march 2009', 'andreas zachhuber', '8 march 2009'], ['sv wehen wiesbaden', 'wolfgang frank', 'sacked', '23 march 2009', 'sandro schwarz ( interim )', '23 march 2009'], ['fc augsburg', 'holger fach', 'sacked', '13 april 2009', 'jos luhukay', '14 april 2009'], ['fc ingolstadt 04', 'thorsten fink', 'sacked', '21 april 2009', 'horst köppel', '26 april 2009'], ['1 . fc kaiserslautern', 'milan šašić', 'sacked', '4 may 2009', 'alois schwartz ( interim )', '4 may 2009'], ['tsv 1860 munich', 'uwe wolf ( interim )', 'released from duties', '13 may 2009', 'ewald lienen', '13 may 2009']]
list of r. l. stine 's the haunting hour episodes
https://en.wikipedia.org/wiki/List_of_R._L._Stine%27s_The_Haunting_Hour_episodes
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29196086-4.html.csv
unique
checking out is the only episode of r. l. stine 's the haunting hour that was aired in the month of january .
{'scope': 'all', 'row': '12', 'col': '6', 'col_other': '3', 'criterion': 'fuzzily_match', 'value': 'january', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'january'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose original air date record fuzzily matches to january .', 'tostr': 'filter_eq { all_rows ; original air date ; january }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; original air date ; january } }', 'tointer': 'select the rows whose original air date record fuzzily matches to january . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'original air date', 'january'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose original air date record fuzzily matches to january .', 'tostr': 'filter_eq { all_rows ; original air date ; january }'}, 'title'], 'result': 'checking out', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; original air date ; january } ; title }'}, 'checking out'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; original air date ; january } ; title } ; checking out }', 'tointer': 'the title record of this unqiue row is checking out .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; original air date ; january } } ; eq { hop { filter_eq { all_rows ; original air date ; january } ; title } ; checking out } } = true', 'tointer': 'select the rows whose original air date record fuzzily matches to january . there is only one such row in the table . the title record of this unqiue row is checking out .'}
and { only { filter_eq { all_rows ; original air date ; january } } ; eq { hop { filter_eq { all_rows ; original air date ; january } ; title } ; checking out } } = true
select the rows whose original air date record fuzzily matches to january . there is only one such row in the table . the title record of this unqiue row is checking out .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'original air date_7': 7, 'january_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'checking out_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'original air date_7': 'original air date', 'january_8': 'january', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'checking out_10': 'checking out'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'original air date_7': [0], 'january_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'checking out_10': [3]}
['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date']
[['41', '1', 'grampires ( part 1 )', 'neill fearnley', 'erik patterson & jessica scott', 'october 13 , 2012'], ['42', '2', 'grampires ( part 2 )', 'neill fearnley', 'erik patterson & jessica scott', 'october 13 , 2012'], ['43', '3', 'the cast', 'ken friss', 'craig s phillips & harold hayes jr', 'october 20 , 2012'], ['44', '4', 'the weeping woman', 'neill fearnley', 'harold hayes jr & craig s phillips', 'october 27 , 2012'], ['45', '5', 'intruders', 'ken friss', 'jack monaco', 'november 3 , 2012'], ['47', '7', 'red eye', 'ken friss', 'natalie lapointe & greg yolen', 'november 17 , 2012'], ['48', '8', 'my imaginary friend', 'james head', 'melody fox', 'november 24 , 2012'], ['49', '9', 'poof de fromage', 'ken friss', 'erik patterson & jessica scott', 'december 1 , 2012'], ['50', '10', 'the golem ( part 1 )', 'neill fearnley', 'jack monaco', 'december 8 , 2012'], ['51', '11', 'the golem ( part 2 )', 'neill fearnley', 'jack monaco', 'december 8 , 2012'], ['52', '12', 'the girl in the painting', 'ken friss', 'jack monaco', 'december 15 , 2012'], ['53', '13', 'checking out', 'james head', 'melody fox', 'january 19 , 2013']]
scott ferrozzo
https://en.wikipedia.org/wiki/Scott_Ferrozzo
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17958251-2.html.csv
count
two of scott ferrozzo 's fights were at the event ufc 12 .
{'scope': 'all', 'criterion': 'equal', 'value': 'ufc 12', 'result': '2', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'ufc 12'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose event record fuzzily matches to ufc 12 .', 'tostr': 'filter_eq { all_rows ; event ; ufc 12 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; event ; ufc 12 } }', 'tointer': 'select the rows whose event record fuzzily matches to ufc 12 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; event ; ufc 12 } } ; 2 } = true', 'tointer': 'select the rows whose event record fuzzily matches to ufc 12 . the number of such rows is 2 .'}
eq { count { filter_eq { all_rows ; event ; ufc 12 } } ; 2 } = true
select the rows whose event record fuzzily matches to ufc 12 . 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, 'event_5': 5, 'ufc 12_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', 'event_5': 'event', 'ufc 12_6': 'ufc 12', '2_7': '2'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'event_5': [0], 'ufc 12_6': [0], '2_7': [2]}
['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location']
[['loss', '4 - 2', 'vitor belfort', 'tko ( punches )', 'ufc 12', '1', '0:43', 'dothan , alabama , united states'], ['win', '4 - 1', 'jim mullen', 'tko ( punches )', 'ufc 12', '1', '8:02', 'dothan , alabama , united states'], ['win', '3 - 1', 'tank abbott', 'decision ( unanimous )', 'ufc 11', '1', '15:00', 'augusta , georgia , united states'], ['win', '2 - 1', 'sam fulton', 'submission ( strikes )', 'ufc 11', '1', '9:00', 'augusta , georgia , united states'], ['win', '1 - 1', 'steve grinnow', 'ko', 'atlanta fights', '1', '11:58', 'atlanta , georgia , united states'], ['loss', '0 - 1', 'jerry bohlander', 'submission ( guillotine choke )', 'ufc 8', '1', '9:03', 'san juan , puerto rico']]
list of tvb series ( 1998 )
https://en.wikipedia.org/wiki/List_of_TVB_series_%281998%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18493407-2.html.csv
ordinal
the 1998 tvb series with the second largest number of episodes was " burning flame . " .
{'row': '7', 'col': '3', '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', 'number of episodes', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; number of episodes ; 2 }'}, 'english title ( chinese title )'], 'result': 'burning flame 烈火雄心', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; number of episodes ; 2 } ; english title ( chinese title ) }'}, 'burning flame 烈火雄心'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; number of episodes ; 2 } ; english title ( chinese title ) } ; burning flame 烈火雄心 } = true', 'tointer': 'select the row whose number of episodes record of all rows is 2nd maximum . the english title ( chinese title ) record of this row is burning flame 烈火雄心 .'}
eq { hop { nth_argmax { all_rows ; number of episodes ; 2 } ; english title ( chinese title ) } ; burning flame 烈火雄心 } = true
select the row whose number of episodes record of all rows is 2nd maximum . the english title ( chinese title ) record of this row is burning flame 烈火雄心 .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'number of episodes_5': 5, '2_6': 6, 'english title (chinese title)_7': 7, 'burning flame 烈火雄心_8': 8}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'number of episodes_5': 'number of episodes', '2_6': '2', 'english title (chinese title)_7': 'english title ( chinese title )', 'burning flame 烈火雄心_8': 'burning flame 烈火雄心'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'number of episodes_5': [0], '2_6': [0], 'english title (chinese title)_7': [1], 'burning flame 烈火雄心_8': [2]}
['airing date', 'english title ( chinese title )', 'number of episodes', 'genre', 'official website']
[['19 jan - 13 feb', 'a measure of love 緣來沒法擋', '20', 'modern drama', 'official website'], ['16 feb - 9 may', 'secret of the heart 天地豪情', '62', 'costume drama', 'official website'], ['11 may - 9 jun', 'crimes of passion 掃黃先鋒', '22', 'modern action', 'official website'], ['6 jul - 31 jul', 'armed reaction 陀槍師姐', '20', 'modern action', 'official website'], ['3 aug - 28 aug', 'rural hero 離島特警', '20', 'modern action', 'official website'], ['31 aug - 10 oct', 'healing hands 妙手仁心', '32', 'modern drama', 'official website'], ['12 oct - 4 dec', 'burning flame 烈火雄心', '43', 'modern action', 'official website'], ['7 dec 1998 - 1 jan 1999', 'till when do us part 冤家宜結不宜解', '20', 'modern drama', 'website']]
list of amusement park rankings
https://en.wikipedia.org/wiki/List_of_amusement_park_rankings
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16578883-7.html.csv
unique
summerland is the only one of the water parks in the amusement park rankings that is located in japan .
{'scope': 'all', 'row': '14', 'col': '3', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'japan', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', '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': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; location ; japan } }', 'tointer': 'select the rows whose location record fuzzily matches to japan . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', '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 }'}, 'water park'], 'result': 'summerland', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; location ; japan } ; water park }'}, 'summerland'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; location ; japan } ; water park } ; summerland }', 'tointer': 'the water park record of this unqiue row is summerland .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; location ; japan } } ; eq { hop { filter_eq { all_rows ; location ; japan } ; water park } ; summerland } } = true', 'tointer': 'select the rows whose location record fuzzily matches to japan . there is only one such row in the table . the water park record of this unqiue row is summerland .'}
and { only { filter_eq { all_rows ; location ; japan } } ; eq { hop { filter_eq { all_rows ; location ; japan } ; water park } ; summerland } } = true
select the rows whose location record fuzzily matches to japan . there is only one such row in the table . the water park record of this unqiue row is summerland .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'location_7': 7, 'japan_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'water park_9': 9, 'summerland_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'location_7': 'location', 'japan_8': 'japan', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'water park_9': 'water park', 'summerland_10': 'summerland'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'location_7': [0], 'japan_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'water park_9': [2], 'summerland_10': [3]}
['rank', 'water park', 'location', '2011', '2012']
[['1', 'typhoon lagoon at walt disney world resort', 'lake buena vista , florida , usa', '2058000', '2100000'], ['2', 'chime - long water park', 'guangzhou , china', '1900000', '2021000'], ['3', 'blizzard beach at walt disney world resort', 'lake buena vista , florida , usa', '1891000', '1929000'], ['4', 'ocean world', 'gangwon - do , south korea', '1726000', '1720000'], ['5', 'aquatica', 'orlando , florida , usa', '1500000', '1538000'], ['6', 'caribbean bay at everland resort', 'gyeonggi - do , south korea', '1497000', '1508000'], ['7', 'aquaventure', 'dubai , united arab emirates', '1200000', '1300000'], ['8', "wet 'n wild orlando", 'orlando , florida , usa', '1223000', '1247000'], ['9', "wet 'n' wild water world", 'gold coast , queensland , australia', '1200000', '1200000'], ['10', 'sunway lagoon', 'kuala lumpur , malaysia', '1040000', '1200000'], ['11', 'resom spa castle', 'chungcheongnam - do , south korea', '1034000', '1158000'], ['12', 'schlitterbahn', 'new braunfels , texas , usa', '982000', '1017000'], ['13', 'atlantis water adventure', 'jakarta , indonesia', '950000', '1000000'], ['14', 'summerland', 'tokyo , japan', '850000', '990000'], ['15', 'happy magic water cube', 'beijing , china', '768000', '968000'], ['16', 'the jungle water adventure', 'bogor , indonesia', '871000', '951000'], ['17', 'wild wadi water park', 'dubai , united arab emirates', '890000', '860000'], ['18', 'siam water park', 'tenerife , spain', '800000', '800000'], ['19', 'ocean park water adventure', 'jakarta , indonesia', '600000', '750000'], ['20', 'water country usa', 'williamsburg , virginia , usa', '723000', '748000']]
1937 vfl season
https://en.wikipedia.org/wiki/1937_VFL_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10806194-8.html.csv
majority
the majority of games on 12 june in the 1937 vfl season had a crowd of less than 20000 .
{'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '20000', 'subset': {'col': '7', 'criterion': 'equal', 'value': '12 june 1937'}}
{'func': 'most_less', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '12 june 1937'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 12 june 1937 }', 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 .'}, 'crowd', '20000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 12 june 1937 . for the crowd records of these rows , most of them are less than 20000 .', 'tostr': 'most_less { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } = true'}
most_less { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 } = true
select the rows whose date record fuzzily matches to 12 june 1937 . for the crowd records of these rows , most of them are less than 20000 .
2
2
{'most_less_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, '12 june 1937_5': 5, 'crowd_6': 6, '20000_7': 7}
{'most_less_1': 'most_less', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', '12 june 1937_5': '12 june 1937', 'crowd_6': 'crowd', '20000_7': '20000'}
{'most_less_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], '12 june 1937_5': [0], 'crowd_6': [1], '20000_7': [1]}
['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']
[['geelong', '16.13 ( 109 )', 'st kilda', '11.15 ( 81 )', 'corio oval', '12600', '12 june 1937'], ['essendon', '13.11 ( 89 )', 'collingwood', '19.14 ( 128 )', 'windy hill', '13000', '12 june 1937'], ['richmond', '14.24 ( 108 )', 'carlton', '13.19 ( 97 )', 'punt road oval', '27000', '12 june 1937'], ['hawthorn', '12.10 ( 82 )', 'melbourne', '15.15 ( 105 )', 'glenferrie oval', '18000', '14 june 1937'], ['fitzroy', '14.15 ( 99 )', 'footscray', '8.14 ( 62 )', 'brunswick street oval', '20000', '14 june 1937'], ['south melbourne', '16.18 ( 114 )', 'north melbourne', '10.10 ( 70 )', 'lake oval', '16000', '14 june 1937']]
nhpc limited
https://en.wikipedia.org/wiki/NHPC_Limited
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18145877-2.html.csv
superlative
the highest total capacity for a nhpc limited power plant in the state of jammu & kashmir is 330 .
{'scope': 'subset', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '2,3', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'jammu & kashmir'}}
{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'state', 'jammu & kashmir'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; state ; jammu & kashmir }', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir .'}, 'total capacity ( mw )'], 'result': '330', 'ind': 1, 'tostr': 'max { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) }', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir . the maximum total capacity ( mw ) record of these rows is 330 .'}, '330'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; 330 }', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir . the maximum total capacity ( mw ) record of these rows is 330 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'state', 'jammu & kashmir'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; state ; jammu & kashmir }', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir .'}, 'total capacity ( mw )'], 'result': None, 'ind': 3, 'tostr': 'argmax { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) }'}, 'power plant'], 'result': 'kishenganga', 'ind': 4, 'tostr': 'hop { argmax { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; power plant }'}, 'kishenganga'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; power plant } ; kishenganga }', 'tointer': 'the power plant record of the row with superlative total capacity ( mw ) record is kishenganga .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { max { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; 330 } ; eq { hop { argmax { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; power plant } ; kishenganga } } = true', 'tointer': 'select the rows whose state record fuzzily matches to jammu & kashmir . the maximum total capacity ( mw ) record of these rows is 330 . the power plant record of the row with superlative total capacity ( mw ) record is kishenganga .'}
and { eq { max { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; 330 } ; eq { hop { argmax { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; power plant } ; kishenganga } } = true
select the rows whose state record fuzzily matches to jammu & kashmir . the maximum total capacity ( mw ) record of these rows is 330 . the power plant record of the row with superlative total capacity ( mw ) record is kishenganga .
8
7
{'and_6': 6, 'result_7': 7, 'eq_2': 2, 'max_1': 1, 'filter_str_eq_0': 0, 'all_rows_8': 8, 'state_9': 9, 'jammu & kashmir_10': 10, 'total capacity (mw)_11': 11, '330_12': 12, 'str_eq_5': 5, 'str_hop_4': 4, 'argmax_3': 3, 'total capacity (mw)_13': 13, 'power plant_14': 14, 'kishenganga_15': 15}
{'and_6': 'and', 'result_7': 'true', 'eq_2': 'eq', 'max_1': 'max', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_8': 'all_rows', 'state_9': 'state', 'jammu & kashmir_10': 'jammu & kashmir', 'total capacity (mw)_11': 'total capacity ( mw )', '330_12': '330', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'argmax_3': 'argmax', 'total capacity (mw)_13': 'total capacity ( mw )', 'power plant_14': 'power plant', 'kishenganga_15': 'kishenganga'}
{'and_6': [7], 'result_7': [], 'eq_2': [6], 'max_1': [2], 'filter_str_eq_0': [1, 3], 'all_rows_8': [0], 'state_9': [0], 'jammu & kashmir_10': [0], 'total capacity (mw)_11': [1], '330_12': [2], 'str_eq_5': [6], 'str_hop_4': [5], 'argmax_3': [4], 'total capacity (mw)_13': [3], 'power plant_14': [4], 'kishenganga_15': [5]}
['sno', 'power plant', 'state', 'total capacity ( mw )', 'completion schedule']
[['1', 'kishenganga', 'jammu & kashmir', '330', '2016'], ['2', 'parbati - ii', 'himachal pradesh', '800', '2013'], ['3', 'subansiri ( lower )', 'assam', '2000', '2014'], ['4', 'teesta low dam - iv', 'west bengal', '160', '2011'], ['5', 'parbati - iii', 'himachal pradesh', '520', '2012'], ['6', 'nimmo - bazgo', 'jammu & kashmir', '45', '2011'], ['7', 'chutak', 'jammu & kashmir', '44', '2011'], ['8', 'uri - ii', 'jammu & kashmir', '240', '2011']]
2002 miami dolphins season
https://en.wikipedia.org/wiki/2002_Miami_Dolphins_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18925638-1.html.csv
comparative
the match on september 8 , 2002 had a higher attendance than the match on september 15 , 2002 .
{'row_1': '1', 'row_2': '2', 'col': '6', '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', 'date', 'september 8 , 2002'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to september 8 , 2002 .', 'tostr': 'filter_eq { all_rows ; date ; september 8 , 2002 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; september 8 , 2002 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to september 8 , 2002 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'september 15 , 2002'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to september 15 , 2002 .', 'tostr': 'filter_eq { all_rows ; date ; september 15 , 2002 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; september 15 , 2002 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to september 15 , 2002 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; september 8 , 2002 } ; attendance } ; hop { filter_eq { all_rows ; date ; september 15 , 2002 } ; attendance } } = true', 'tointer': 'select the rows whose date record fuzzily matches to september 8 , 2002 . take the attendance record of this row . select the rows whose date record fuzzily matches to september 15 , 2002 . take the attendance record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; date ; september 8 , 2002 } ; attendance } ; hop { filter_eq { all_rows ; date ; september 15 , 2002 } ; attendance } } = true
select the rows whose date record fuzzily matches to september 8 , 2002 . take the attendance record of this row . select the rows whose date record fuzzily matches to september 15 , 2002 . take the attendance 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, 'date_7': 7, 'september 8 , 2002_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'september 15 , 2002_12': 12, 'attendance_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', 'date_7': 'date', 'september 8 , 2002_8': 'september 8 , 2002', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'september 15 , 2002_12': 'september 15 , 2002', 'attendance_13': 'attendance'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'september 8 , 2002_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'september 15 , 2002_12': [1], 'attendance_13': [3]}
['week', 'date', 'opponent', 'result', 'tv time', 'attendance']
[['1', 'september 8 , 2002', 'detroit lions', 'w 49 - 21', 'fox 1:00 pm', '72216'], ['2', 'september 15 , 2002', 'indianapolis colts', 'w 21 - 13', 'cbs 1:00 pm', '56650'], ['3', 'september 22 , 2002', 'new york jets', 'w 30 - 3', 'cbs 1:00 pm', '73426'], ['4', 'september 29 , 2002', 'kansas city chiefs', 'l 48 - 30', 'cbs 1:00 pm', '78178'], ['5', 'october 6 , 2002', 'new england patriots', 'w 26 - 13', 'cbs 1:00 pm', '73369'], ['6', 'october 13 , 2002', 'denver broncos', 'w 24 - 22', 'espn 8:30 pm', '75941'], ['7', 'october 20 , 2002', 'buffalo bills', 'l 23 - 10', 'cbs 1:00 pm', '73180'], ['9', 'november 4 , 2002', 'green bay packers', 'l 24 - 10', 'abc 9:00 pm', '63284'], ['10', 'november 10 , 2002', 'new york jets', 'l 13 - 10', 'espn 8:30 pm', '78920'], ['11', 'november 17 , 2002', 'baltimore ravens', 'w 26 - 7', 'cbs 4:15 pm', '73013'], ['12', 'november 24 , 2002', 'san diego chargers', 'w 30 - 3', 'cbs 1:00 pm', '73138'], ['13', 'december 1 , 2002', 'buffalo bills', 'l 38 - 21', 'cbs 1:00 pm', '73287'], ['14', 'december 9 , 2002', 'chicago bears', 'w 27 - 9', 'abc 9:00 pm', '73609'], ['15', 'december 15 , 2002', 'oakland raiders', 'w 23 - 17', 'cbs 1:00 pm', '73572'], ['16', 'december 21 , 2002', 'minnesota vikings', 'l 20 - 17', 'cbs 12:30 pm', '64285'], ['17', 'december 29 , 2002', 'new england patriots', 'l 27 - 24', 'cbs 1:00 pm', '68436']]
amor en custodia ( tv series )
https://en.wikipedia.org/wiki/Amor_en_Custodia_%28TV_series%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19938261-2.html.csv
aggregation
the average viewership in millions for amor en custodia was 9.1 .
{'scope': 'all', 'col': '7', 'type': 'average', 'result': '9.1', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'viewers ( in millions )'], 'result': '9.1', 'ind': 0, 'tostr': 'avg { all_rows ; viewers ( in millions ) }'}, '9.1'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; viewers ( in millions ) } ; 9.1 } = true', 'tointer': 'the average of the viewers ( in millions ) record of all rows is 9.1 .'}
round_eq { avg { all_rows ; viewers ( in millions ) } ; 9.1 } = true
the average of the viewers ( in millions ) record of all rows is 9.1 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'viewers (in millions)_4': 4, '9.1_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'viewers (in millions)_4': 'viewers ( in millions )', '9.1_5': '9.1'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'viewers (in millions)_4': [0], '9.1_5': [1]}
['season', 'timeslot ( edt )', 'season premiere', 'season finale', 'tv season', 'rank ( spanish language )', 'viewers ( in millions )']
[['2', 'monday - friday 9:00 pm', 'january 16 , 2006', 'april 6 , 2006', '2006', '1', '9.4'], ['3', 'monday - friday 9:00 pm', 'april 10 , 2006', 'september 1 , 2006', '2006', '1', '9.9'], ['4', 'monday - friday 9:00 pm', 'september 4 , 2006', 'january 6 , 2007', '2006 - 2007', '2', '9.7'], ['5', 'monday - friday 9:00 pm', 'january 8 , 2007', 'march 28 , 2007', '2007', '1', '10.1'], ['6', 'monday - friday 9:00 pm', 'april 2 , 2007', 'august 9 , 2007', '2007', '3', '7.9'], ['7', 'monday - friday 9:00 pm', 'august 13 , 2007', 'december 10 , 2007', '2007', '2', '9.9'], ['8', 'monday - friday 9:00 pm', 'december 13 , 2007', 'february 15 , 2008', '2007 - 2008', '7', '5.1'], ['9', 'monday - friday 9:00 pm', 'february 18 , 2008', 'may 30 , 2008', '2008', '1', '10.4'], ['10', 'monday - friday 10:00 pm', 'june 2 , 2008', 'august 29 , 2008', '2008', '1', '9.4']]
rousimar palhares
https://en.wikipedia.org/wiki/Rousimar_Palhares
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17440284-2.html.csv
majority
the majority of the results ended in a win for rousimar palhares .
{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'win', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'res', 'win'], 'result': True, 'ind': 0, 'tointer': 'for the res records of all rows , most of them fuzzily match to win .', 'tostr': 'most_eq { all_rows ; res ; win } = true'}
most_eq { all_rows ; res ; win } = true
for the res records of all rows , most of them fuzzily match to win .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'res_3': 3, 'win_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'res_3': 'res', 'win_4': 'win'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'res_3': [0], 'win_4': [0]}
['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location']
[['win', '15 - 5', 'mike pierce', 'submission ( heel hook )', 'ufc fight night : maia vs shields', '1', '0:31', 'barueri , são paulo , brazil'], ['loss', '14 - 5', 'hector lombard', 'ko ( punches )', 'ufc on fx : sotiropoulos vs pearson', '1', '3:38', 'gold coast , queensland , australia'], ['loss', '14 - 4', 'alan belcher', 'tko ( punches & elbows )', 'ufc on fox : diaz vs miller', '1', '4:18', 'east rutherford , new jersey , united states'], ['win', '14 - 3', 'mike massenzio', 'submission ( heel hook )', 'ufc 142', '1', '1:03', 'rio de janeiro , rio de janeiro , brazil'], ['win', '13 - 3', 'dan miller', 'decision ( unanimous )', 'ufc 134', '3', '5:00', 'rio de janeiro , rio de janeiro , brazil'], ['win', '12 - 3', 'david branch', 'submission ( kneebar )', 'ufc live : sanchez vs kampmann', '2', '1:44', 'louisville , kentucky , united states'], ['loss', '11 - 3', 'nate marquardt', 'tko ( punches )', 'ufc fight night : marquardt vs palhares', '1', '3:28', 'austin , texas , united states'], ['win', '11 - 2', 'tomasz drwal', 'submission ( heel hook )', 'ufc 111', '1', '0:45', 'newark , new jersey , united states'], ['win', '10 - 2', 'lucio linhares', 'submission ( heel hook )', 'ufc 107', '2', '3:21', 'memphis , tennessee , united states'], ['win', '9 - 2', 'jeremy horn', 'decision ( unanimous )', 'ufc 93', '3', '5:00', 'dublin , ireland'], ['loss', '8 - 2', 'dan henderson', 'decision ( unanimous )', 'ufc 88', '3', '5:00', 'atlanta , georgia , united states'], ['win', '8 - 1', 'ivan salaverry', 'submission ( armbar )', 'ufc 84', '1', '2:36', 'las vegas , nevada , united states'], ['win', '7 - 1', 'daniel acacio', 'submission ( heel hook )', 'fury fc 5 : final conflict', '1', '1:22', 'são paulo , brazil'], ['win', '6 - 1', 'fabio nascimento', 'submission ( heel hook )', 'fury fc 5 : final conflict', '1', '2:45', 'são paulo , brazil'], ['win', '5 - 1', 'flavio luiz moura', 'submission ( heel hook )', 'fury fc 4 : high voltage', '1', '1:21', 'teresopolis , brazil'], ['win', '4 - 1', 'helio dipp', 'submission ( rear naked choke )', 'floripa fight 3', '1', '1:40', 'florianópolis , brazil'], ['win', '3 - 1', 'claudio mattos', 'tko ( injury )', 'storm samurai 12', '1', '4:58', 'curitiba , brazil'], ['loss', '2 - 1', 'arthur cesar jacintho', 'decision ( split )', 'rio mma challenger 2', '3', '5:00', 'rio de janeiro , brazil'], ['win', '2 - 0', 'renan moraes', 'submission ( armbar )', 'gold fighters championship 1', '1', 'n / a', 'rio de janeiro , brazil'], ['win', '1 - 0', 'bruno bastos', 'decision ( split )', 'floripa fight 2', '3', '5:00', 'florianópolis , brazil']]
kstp - tv
https://en.wikipedia.org/wiki/KSTP-TV
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1406855-1.html.csv
count
only 2 of the channels have 720p video , and the rest are in 480p .
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': '720p', 'result': '2', 'col': '3', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'video', '720p'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose video record fuzzily matches to 720p .', 'tostr': 'filter_eq { all_rows ; video ; 720p }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; video ; 720p } }', 'tointer': 'select the rows whose video record fuzzily matches to 720p . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; video ; 720p } } ; 2 } = true', 'tointer': 'select the rows whose video record fuzzily matches to 720p . the number of such rows is 2 .'}
eq { count { filter_eq { all_rows ; video ; 720p } } ; 2 } = true
select the rows whose video record fuzzily matches to 720p . 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, 'video_5': 5, '720p_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', 'video_5': 'video', '720p_6': '720p', '2_7': '2'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'video_5': [0], '720p_6': [0], '2_7': [2]}
['channel', 'station', 'video', 'aspect', 'psip short name', 'programming']
[['5.1', 'kstp - tv', '720p', '16:9', 'kstpdt1', 'main kstp - tv programming / abc'], ['5.2', 'kstc - tv', '720p', '16:9', 'kstcdt2', 'main kstc - tv programming'], ['5.3', 'kstc - tv', '480i', '16:9', 'kstcdt3', 'me - tv'], ['5.4', 'kstc - tv', '480i', '16:9', 'kstcdt4', 'antenna tv'], ['5.5', 'kstc - tv', '480i', '16:9', 'kstpdt2', 'live well network'], ['5.6', 'kstc - tv', '480i', '16:9', 'kstcdt6', 'this tv']]
chris dimarco
https://en.wikipedia.org/wiki/Chris_DiMarco
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1598015-5.html.csv
unique
the only event chris dimarco has played in 10 times is the pga championship .
{'scope': 'all', 'row': '4', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': '10', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'events', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose events record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; events ; 10 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; events ; 10 } }', 'tointer': 'select the rows whose events record is equal to 10 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'events', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose events record is equal to 10 .', 'tostr': 'filter_eq { all_rows ; events ; 10 }'}, 'tournament'], 'result': 'pga championship', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; events ; 10 } ; tournament }'}, 'pga championship'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; events ; 10 } ; tournament } ; pga championship }', 'tointer': 'the tournament record of this unqiue row is pga championship .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; events ; 10 } } ; eq { hop { filter_eq { all_rows ; events ; 10 } ; tournament } ; pga championship } } = true', 'tointer': 'select the rows whose events record is equal to 10 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .'}
and { only { filter_eq { all_rows ; events ; 10 } } ; eq { hop { filter_eq { all_rows ; events ; 10 } ; tournament } ; pga championship } } = true
select the rows whose events record is equal to 10 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'events_7': 7, '10_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'pga championship_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'events_7': 'events', '10_8': '10', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'pga championship_10': 'pga championship'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'events_7': [0], '10_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'pga championship_10': [3]}
['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made']
[['masters tournament', '0', '1', '3', '4', '7', '4'], ['us open', '0', '0', '1', '3', '8', '6'], ['the open championship', '0', '1', '1', '2', '8', '6'], ['pga championship', '0', '1', '1', '4', '10', '8'], ['totals', '0', '3', '6', '13', '33', '24']]
1993 cincinnati bengals season
https://en.wikipedia.org/wiki/1993_Cincinnati_Bengals_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17860844-2.html.csv
aggregation
the average attendance for games played in september during the 1993 cincinnati bengals season was 56,592 .
{'scope': 'subset', 'col': '5', 'type': 'average', 'result': '56,592', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'september'}}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'september'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; september }', 'tointer': 'select the rows whose date record fuzzily matches to september .'}, 'attendance'], 'result': '56,592', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; date ; september } ; attendance }'}, '56,592'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; date ; september } ; attendance } ; 56,592 } = true', 'tointer': 'select the rows whose date record fuzzily matches to september . the average of the attendance record of these rows is 56,592 .'}
round_eq { avg { filter_eq { all_rows ; date ; september } ; attendance } ; 56,592 } = true
select the rows whose date record fuzzily matches to september . the average of the attendance record of these rows is 56,592 .
3
3
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, 'september_6': 6, 'attendance_7': 7, '56,592_8': 8}
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', 'september_6': 'september', 'attendance_7': 'attendance', '56,592_8': '56,592'}
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'september_6': [0], 'attendance_7': [1], '56,592_8': [2]}
['week', 'date', 'opponent', 'result', 'attendance']
[['1', 'september 5 , 1993', 'cleveland browns', 'l 14 - 27', '75508'], ['2', 'september 12 , 1993', 'indianapolis colts', 'l 6 - 9', '50299'], ['3', 'september 19 , 1993', 'pittsburgh steelers', 'l 7 - 34', '53682'], ['4', 'september 26 , 1993', 'seattle seahawks', 'l 10 - 19', '46880'], ['6', 'october 10 , 1993', 'kansas city chiefs', 'l 15 - 17', '75394'], ['7', 'october 17 , 1993', 'cleveland browns', 'l 17 - 28', '55647'], ['8', 'october 24 , 1993', 'houston oilers', 'l 12 - 28', '50039'], ['10', 'november 7 , 1993', 'pittsburgh steelers', 'l 16 - 24', '51202'], ['11', 'november 14 , 1993', 'houston oilers', 'l 3 - 38', '42347'], ['12', 'november 21 , 1993', 'new york jets', 'l 12 - 17', '64264'], ['13', 'november 28 , 1993', 'los angeles raiders', 'w 16 - 10', '43272'], ['14', 'december 5 , 1993', 'san francisco 49ers', 'l 8 - 21', '60039'], ['15', 'december 12 , 1993', 'new england patriots', 'l 2 - 7', '29794'], ['16', 'december 19 , 1993', 'los angeles rams', 'w 15 - 3', '36612'], ['17', 'december 26 , 1993', 'atlanta falcons', 'w 21 - 17', '27014'], ['18', 'january 2 , 1994', 'new orleans saints', 'l 13 - 20', '58036']]
list of schools in the otago region
https://en.wikipedia.org/wiki/List_of_schools_in_the_Otago_Region
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12303251-3.html.csv
aggregation
the average roll for the schools in the otago region is about 338.66 .
{'scope': 'all', 'col': '7', 'type': 'average', 'result': '338.66', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'roll'], 'result': '338.66', 'ind': 0, 'tostr': 'avg { all_rows ; roll }'}, '338.66'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; roll } ; 338.66 } = true', 'tointer': 'the average of the roll record of all rows is 338.66 .'}
round_eq { avg { all_rows ; roll } ; 338.66 } = true
the average of the roll record of all rows is 338.66 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'roll_4': 4, '338.66_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'roll_4': 'roll', '338.66_5': '338.66'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'roll_4': [0], '338.66_5': [1]}
['name', 'years', 'gender', 'area', 'authority', 'decile', 'roll']
[['arrowtown school', '1 - 8', 'coed', 'arrowtown', 'state', '10', '498'], ['glenorchy school', '1 - 8', 'coed', 'glenorchy', 'state', '9', '28'], ['hawea flat school', '1 - 6', 'coed', 'hawea flat', 'state', '10', '160'], ['holy family school', '1 - 8', 'coed', 'wanaka', 'state integrated', '10', '122'], ['kingsview school', '1 - 8', 'coed', 'frankton', 'state integrated', '10', '20'], ['makarora primary school', '1 - 8', 'coed', 'makarora', 'state', '7', '15'], ['mount aspiring college', '7 - 13', 'coed', 'wanaka', 'state', '10', '711'], ['queenstown school', '1 - 6', 'coed', 'queenstown', 'state', '10', '627'], ['remarkables primary school', '1 - 8', 'coed', 'frankton', 'state', '10', '495'], ["st joseph 's school", '1 - 8', 'coed', 'queenstown', 'state integrated', '10', '141'], ['wakatipu high school', '9 - 13', 'coed', 'queenstown', 'state', '10', '720'], ['wanaka primary school', '1 - 6', 'coed', 'wanaka', 'state', '10', '527']]
peaches & herb
https://en.wikipedia.org/wiki/Peaches_%26_Herb
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1862179-1.html.csv
unique
demasiado candente is the only title by peaches & herb to be released in the country of argentina .
{'scope': 'all', 'row': '6', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': 'argentina', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country of release', 'argentina'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country of release record fuzzily matches to argentina .', 'tostr': 'filter_eq { all_rows ; country of release ; argentina }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country of release ; argentina } }', 'tointer': 'select the rows whose country of release 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', 'country of release', 'argentina'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country of release record fuzzily matches to argentina .', 'tostr': 'filter_eq { all_rows ; country of release ; argentina }'}, 'title'], 'result': 'demasiado candente', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country of release ; argentina } ; title }'}, 'demasiado candente'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country of release ; argentina } ; title } ; demasiado candente }', 'tointer': 'the title record of this unqiue row is demasiado candente .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country of release ; argentina } } ; eq { hop { filter_eq { all_rows ; country of release ; argentina } ; title } ; demasiado candente } } = true', 'tointer': 'select the rows whose country of release record fuzzily matches to argentina . there is only one such row in the table . the title record of this unqiue row is demasiado candente .'}
and { only { filter_eq { all_rows ; country of release ; argentina } } ; eq { hop { filter_eq { all_rows ; country of release ; argentina } ; title } ; demasiado candente } } = true
select the rows whose country of release record fuzzily matches to argentina . there is only one such row in the table . the title record of this unqiue row is demasiado candente .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country of release_7': 7, 'argentina_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'demasiado candente_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country of release_7': 'country of release', 'argentina_8': 'argentina', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'demasiado candente_10': 'demasiado candente'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country of release_7': [0], 'argentina_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'demasiado candente_10': [3]}
['title', 'label', 'year of release', 'country of release', 'peaches :']
[['for your love', 'date', '1967', 'usa', 'francine barker'], ["let 's fall in love", 'date', '1967', 'usa', 'francine barker'], ['peaches & herb', 'mca', '1977', 'usa', 'linda greene'], ['2 hot', 'mvp / polydor', '1978', 'usa', 'linda greene'], ['twice the fire', 'mvp / polydor', '1979', 'usa', 'linda greene'], ['demasiado candente', 'mvp / polydor', '1979', 'argentina', 'linda greene'], ['worth the wait', 'mvp / polydor', '1980', 'usa', 'linda greene'], ["sayin ' something", 'mvp / polydor', '1981', 'usa', 'linda greene'], ['remember', 'the entertainment co / columbia', '1983', 'usa', 'linda greene'], ['colors of love', 'imagen', '2009', 'usa', 'meritxell negre']]
chet miller
https://en.wikipedia.org/wiki/Chet_Miller
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1252058-1.html.csv
comparative
chet miller finished more laps in 1931 than he did in 1930 .
{'row_1': '2', 'row_2': '1', 'col': '6', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1931'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1931 .', 'tostr': 'filter_eq { all_rows ; year ; 1931 }'}, 'laps'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1931 } ; laps }', 'tointer': 'select the rows whose year record fuzzily matches to 1931 . take the laps record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1930'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1930 .', 'tostr': 'filter_eq { all_rows ; year ; 1930 }'}, 'laps'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1930 } ; laps }', 'tointer': 'select the rows whose year record fuzzily matches to 1930 . take the laps record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 1931 } ; laps } ; hop { filter_eq { all_rows ; year ; 1930 } ; laps } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1931 . take the laps record of this row . select the rows whose year record fuzzily matches to 1930 . take the laps record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; year ; 1931 } ; laps } ; hop { filter_eq { all_rows ; year ; 1930 } ; laps } } = true
select the rows whose year record fuzzily matches to 1931 . take the laps record of this row . select the rows whose year record fuzzily matches to 1930 . take the laps record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1931_8': 8, 'laps_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1930_12': 12, 'laps_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1931_8': '1931', 'laps_9': 'laps', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1930_12': '1930', 'laps_13': 'laps'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1931_8': [0], 'laps_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1930_12': [1], 'laps_13': [3]}
['year', 'start', 'qual', 'rank', 'finish', 'laps']
[['1930', '15', '97.360', '23', '13', '161'], ['1931', '15', '106.185', '25', '10', '200'], ['1932', '29', '111.053', '23', '21', '125'], ['1933', '32', '112.025', '23', '20', '163'], ['1934', '32', '109.252', '29', '33', '11'], ['1935', '17', '113.552', '24', '10', '200'], ['1936', '3', '117.675', '3', '5', '200'], ['1937', '13', '119.213', '13', '30', '36'], ['1938', '5', '121.898', '9', '3', '200'], ['1939', '5', '126.318', '8', '21', '109'], ['1940', '27', '121.392', '27', '17', '189'], ['1941', '9', '121.540', '23', '6', '200'], ['1946', '17', '124.649', '8', '18', '64'], ['1948', '19', '127.249', '8', '20', '108'], ['1951', '28', '135.798', '3', '25', '56'], ['1952', '27', '139.034', '1', '30', '41']]
2010 - 11 scottish premier league
https://en.wikipedia.org/wiki/2010%E2%80%9311_Scottish_Premier_League
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26980923-2.html.csv
aggregation
the average capacity of the premier league stadiums is around 20000-22000 seats .
{'scope': 'all', 'col': '3', 'type': 'average', 'result': '22188.1', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'capacity'], 'result': '22188.1', 'ind': 0, 'tostr': 'avg { all_rows ; capacity }'}, '22188.1'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; capacity } ; 22188.1 } = true', 'tointer': 'the average of the capacity record of all rows is 22188.1 .'}
round_eq { avg { all_rows ; capacity } ; 22188.1 } = true
the average of the capacity record of all rows is 22188.1 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'capacity_4': 4, '22188.1_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'capacity_4': 'capacity', '22188.1_5': '22188.1'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'capacity_4': [0], '22188.1_5': [1]}
['team', 'stadium', 'capacity', 'total', 'highest', 'lowest', 'average']
[['aberdeen', 'pittodrie stadium', '22199', '173460', '15307', '5955', '9129'], ['celtic', 'celtic park', '60832', '930395', '58874', '40750', '48968'], ['dundee united', 'tannadice park', '14209', '140391', '11790', '4918', '7389'], ['hamilton academical', 'new douglas park', '6096', '55056', '5356', '2011', '2898'], ['heart of midlothian', 'tynecastle stadium', '17420', '269506', '17420', '12009', '14185'], ['inverness ct', 'caledonian stadium', '7500', '85998', '7547', '3241', '4526'], ['kilmarnock', 'rugby park', '18128', '122106', '16173', '4214', '6427'], ['motherwell', 'fir park', '13742', '99838', '9716', '3324', '5255'], ['rangers', 'ibrox stadium', '51082', '860793', '50248', '41514', '45305'], ['st johnstone', 'mcdiarmid park', '10673', '72982', '6866', '2253', '3841']]
miss andretti
https://en.wikipedia.org/wiki/Miss_Andretti
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14981555-1.html.csv
superlative
miss andretti finished second in one race as to where she finished first in all the other seven races .
{'scope': 'all', 'col_superlative': '1', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': 'n/a', 'subset': None}
{'func': 'eq', 'args': [{'func': 'min', 'args': ['all_rows', 'result'], 'result': '2nd', 'ind': 0, 'tostr': 'min { all_rows ; result }', 'tointer': 'the minimum result record of all rows is 2nd .'}, '2nd'], 'result': True, 'ind': 1, 'tostr': 'eq { min { all_rows ; result } ; 2nd } = true', 'tointer': 'the minimum result record of all rows is 2nd .'}
eq { min { all_rows ; result } ; 2nd } = true
the minimum result record of all rows is 2nd .
2
2
{'eq_1': 1, 'result_2': 2, 'min_0': 0, 'all_rows_3': 3, 'result_4': 4, '2nd_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'min_0': 'min', 'all_rows_3': 'all_rows', 'result_4': 'result', '2nd_5': '2nd'}
{'eq_1': [2], 'result_2': [], 'min_0': [1], 'all_rows_3': [0], 'result_4': [0], '2nd_5': [1]}
['result', 'date', 'race', 'venue', 'group', 'distance', 'weight ( kg )', 'jockey', 'winner / 2nd']
[['won', '29 dec 2004', '3yo hcp restricted maiden', 'pinjarra', 'na', '1200 m', '55', 'k forrester', '2nd - hello doctor'], ['2nd', '26 jan 2005', '3yo hcp restricted fillies', 'belmont', 'na', '1200 m', '52.5', 'k forrester', '1st - lust for dust'], ['won', '12 feb 2005', '3yo hcp restricted', 'belmont', 'na', '1200 m', '52', 'k forrester', "2nd - key 's ace"], ['won', '27 feb 2005', '3yo hcp restricted fillies & mares', 'pinjarra', 'na', '1400 m', '53.5', 'k forrester', '2nd - blondelle'], ['won', '25 apr 2005', '3yo hcp restricted fillies', 'belmont', 'na', '1000 m', '59', 'k forrester', '2nd - final effect'], ['won', '14 may 2005', '3yo hcp restricted', 'belmont', 'na', '1200 m', '52.5', 'k forrester', '2nd - zed power'], ['won', '28 may 2005', '3yo hcp restricted fillies & mares', 'belmont', 'na', '1200 m', '55.5', 'k forrester', '2nd - eroded']]
2007 - 08 boston celtics season
https://en.wikipedia.org/wiki/2007%E2%80%9308_Boston_Celtics_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11959669-6.html.csv
comparative
game 55 against the clippers was a higher scoring game than game 56 against cleveland .
{'row_1': '11', 'row_2': '12', 'col': '4', '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', 'game', '55'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose game record fuzzily matches to 55 .', 'tostr': 'filter_eq { all_rows ; game ; 55 }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; game ; 55 } ; score }', 'tointer': 'select the rows whose game record fuzzily matches to 55 . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game', '56'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose game record fuzzily matches to 56 .', 'tostr': 'filter_eq { all_rows ; game ; 56 }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; game ; 56 } ; score }', 'tointer': 'select the rows whose game record fuzzily matches to 56 . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; game ; 55 } ; score } ; hop { filter_eq { all_rows ; game ; 56 } ; score } } = true', 'tointer': 'select the rows whose game record fuzzily matches to 55 . take the score record of this row . select the rows whose game record fuzzily matches to 56 . take the score record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; game ; 55 } ; score } ; hop { filter_eq { all_rows ; game ; 56 } ; score } } = true
select the rows whose game record fuzzily matches to 55 . take the score record of this row . select the rows whose game record fuzzily matches to 56 . take the score record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'game_7': 7, '55_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'game_11': 11, '56_12': 12, 'score_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'game_7': 'game', '55_8': '55', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'game_11': 'game', '56_12': '56', 'score_13': 'score'}
{'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'game_7': [0], '55_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'game_11': [1], '56_12': [1], 'score_13': [3]}
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
[['45', 'february 5', 'cleveland', '113 - 114', 'allen ( 24 )', 'rondo ( 7 )', 'allen ( 5 )', 'quicken loans arena 20562', '36 - 9'], ['46', 'february 6', 'la clippers', '111 - 100', 'rondo ( 24 )', 'powe ( 10 )', 'rondo ( 8 )', 'td banknorth garden 18624', '37 - 9'], ['47', 'february 8', 'minnesota', '88 - 86', 'pierce ( 18 )', 'powe ( 8 )', 'pierce ( 6 )', 'target center 19511', '38 - 9'], ['48', 'february 10', 'san antonio', '98 - 90', 'pierce ( 35 )', 'rondo ( 11 )', 'rondo ( 12 )', 'td banknorth garden 18624', '39 - 9'], ['49', 'february 12', 'indiana', '104 - 97', 'pierce ( 28 )', 'pierce ( 12 )', 'rondo ( 7 )', 'conseco fieldhouse 13603', '40 - 9'], ['50', 'february 13', 'new york', '111 - 103', 'pierce ( 24 )', 'posey ( 11 )', 'pierce ( 7 )', 'td banknorth garden 18624', '41 - 9'], ['51', 'february 19', 'denver', '118 - 124', 'pierce ( 24 )', 'powe ( 11 )', 'pierce ( 7 )', 'pepsi center 19894', '41 - 10'], ['52', 'february 20', 'golden state', '117 - 119', 'allen ( 32 )', 'garnett ( 15 )', 'allen , rondo ( 6 )', 'oracle arena 20711', '41 - 11'], ['53', 'february 22', 'phoenix', '77 - 85', 'garnett ( 19 )', 'perkins , pierce ( 6 )', 'garnett ( 4 )', 'us airways center 18422', '41 - 12'], ['54', 'february 24', 'portland', '112 - 102', 'pierce ( 30 )', 'garnett , pierce ( 7 )', 'rondo ( 8 )', 'rose garden 20554', '42 - 12'], ['55', 'february 25', 'la clippers', '104 - 76', 'pierce , posey ( 17 )', 'perkins ( 9 )', 'allen ( 7 )', 'staples center 19328', '43 - 12'], ['56', 'february 27', 'cleveland', '92 - 87', 'allen ( 22 )', 'garnett ( 11 )', 'rondo ( 8 )', 'td banknorth garden 18624', '44 - 12']]
list of republic of doyle episodes
https://en.wikipedia.org/wiki/List_of_Republic_of_Doyle_episodes
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27547668-2.html.csv
aggregation
the average number of viewers for the republic of doyle episodes was 872100 .
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '872100', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'viewers'], 'result': '872100', 'ind': 0, 'tostr': 'avg { all_rows ; viewers }'}, '872100'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; viewers } ; 872100 } = true', 'tointer': 'the average of the viewers record of all rows is 872100 .'}
round_eq { avg { all_rows ; viewers } ; 872100 } = true
the average of the viewers record of all rows is 872100 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'viewers_4': 4, '872100_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'viewers_4': 'viewers', '872100_5': '872100'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'viewers_4': [0], '872100_5': [1]}
['', 'title', 'directed by', 'written by', 'viewers', 'original airdate', 'prod code']
[['1', 'fathers and sons', 'mike clattenburg', 'allan hawco , perry chafe and malcolm macrury', '969000', 'january 6 , 2010', '101'], ['2', 'the return of the grievous angel', 'steve dimarco', 'allan hawco and avrum jacobson', '715000', 'january 13 , 2010', '102'], ['3', 'duchess of george', 'mike clattenburg', 'allan hawco , perry chafe and malcolm macrury', '685000', 'january 20 , 2010', '103'], ['5', 'hit and rum', 'steve dimarco', 'matt maclennan', '594000', 'february 3 , 2010', '105'], ['6', 'the one who got away', 'larry mclean', 'jesse mckeown', '1012000', 'february 10 , 2010', '106'], ['7', 'the woman who knew too little', 'robert lieberman', 'jeremy boxen', '1053000', 'march 3 , 2010', '107'], ['8', 'the tell - tale safe', 'jerry ciccoritti', 'john callaghan and steve cochrane', '986000', 'march 10 , 2010', '108'], ['9', 'he sleeps with the chips', 'phil earnshaw', 'perry chafe', '908000', 'march 17 , 2010', '109'], ['10', 'the pen is mightier than the doyle', 'robert lieberman', 'steve cochrane and avrum jacobson', '897000', 'march 24 , 2010', '110'], ['11', 'a horse divided', 'steve scaini', 'jesse mckeown', '902000', 'march 31 , 2010', '111']]
new york state election , 1930
https://en.wikipedia.org/wiki/New_York_state_election%2C_1930
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15563525-1.html.csv
count
four offices in the ny state elections of 1930 had no law preservation ticket listed .
{'scope': 'all', 'criterion': 'equal', 'value': '( none )', 'result': '4', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'law preservation ticket', '( none )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose law preservation ticket record fuzzily matches to ( none ) .', 'tostr': 'filter_eq { all_rows ; law preservation ticket ; ( none ) }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; law preservation ticket ; ( none ) } }', 'tointer': 'select the rows whose law preservation ticket record fuzzily matches to ( none ) . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; law preservation ticket ; ( none ) } } ; 4 } = true', 'tointer': 'select the rows whose law preservation ticket record fuzzily matches to ( none ) . the number of such rows is 4 .'}
eq { count { filter_eq { all_rows ; law preservation ticket ; ( none ) } } ; 4 } = true
select the rows whose law preservation ticket record fuzzily matches to ( none ) . the number of such rows is 4 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'law preservation ticket_5': 5, '(none)_6': 6, '4_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'law preservation ticket_5': 'law preservation ticket', '(none)_6': '( none )', '4_7': '4'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'law preservation ticket_5': [0], '(none)_6': [0], '4_7': [2]}
['office', 'democratic ticket', 'republican ticket', 'law preservation ticket', 'socialist ticket', 'socialist labor ticket']
[['governor', 'franklin d roosevelt', 'charles h tuttle', 'robert p carroll', 'louis waldman', 'jeremiah d crowley'], ['lieutenant governor', 'herbert h lehman', 'caleb h baumes', '( none )', 'elizabeth c roth', 'charles m carlson'], ['comptroller', 'morris s tremaine', 'daniel h conway', '( none )', 'william h hilsdorf', 'john e delee'], ['attorney general', 'john j bennett , jr', 'isadore bookstein', '( none )', 'william karlin', 'august gillhaus'], ['judge of the court of appeals', 'cuthbert w pound', 'cuthbert w pound', '( none )', 'darwin j meserole', 'belle j rosen']]
washington redskins draft history
https://en.wikipedia.org/wiki/Washington_Redskins_draft_history
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17100961-8.html.csv
unique
clyde shugart was the only player the washington redskins drafted from iowa state college .
{'scope': 'all', 'row': '15', 'col': '6', 'col_other': '4', 'criterion': 'equal', 'value': 'iowa state', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college', 'iowa state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to iowa state .', 'tostr': 'filter_eq { all_rows ; college ; iowa state }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; college ; iowa state } }', 'tointer': 'select the rows whose college record fuzzily matches to iowa state . 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', 'iowa state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose college record fuzzily matches to iowa state .', 'tostr': 'filter_eq { all_rows ; college ; iowa state }'}, 'name'], 'result': 'clyde shugart', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; college ; iowa state } ; name }'}, 'clyde shugart'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; college ; iowa state } ; name } ; clyde shugart }', 'tointer': 'the name record of this unqiue row is clyde shugart .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; college ; iowa state } } ; eq { hop { filter_eq { all_rows ; college ; iowa state } ; name } ; clyde shugart } } = true', 'tointer': 'select the rows whose college record fuzzily matches to iowa state . there is only one such row in the table . the name record of this unqiue row is clyde shugart .'}
and { only { filter_eq { all_rows ; college ; iowa state } } ; eq { hop { filter_eq { all_rows ; college ; iowa state } ; name } ; clyde shugart } } = true
select the rows whose college record fuzzily matches to iowa state . there is only one such row in the table . the name record of this unqiue row is clyde shugart .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'college_7': 7, 'iowa state_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'clyde shugart_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', 'iowa state_8': 'iowa state', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'clyde shugart_10': 'clyde shugart'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'college_7': [0], 'iowa state_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'clyde shugart_10': [3]}
['round', 'pick', 'overall', 'name', 'position', 'college']
[['1', '8', '8', 'i b hale', 'ot', 'texas christian'], ['3', '8', '23', 'charley holm', 'rb', 'alabama'], ['5', '8', '38', 'dick todd', 'rb', 'texas a & m'], ['6', '8', '48', 'dave anderson', 'rb', 'california'], ['7', '8', '58', 'quinton lumpkin', 'c', 'georgia'], ['8', '8', '68', 'bo russell', 'ot', 'auburn'], ['9', '8', '78', 'wilbur moore', 'hb', 'minnesota'], ['10', '8', '88', 'jim johnston', 'rb', 'washington'], ['11', '8', '98', 'jim german', 'rb', 'centre'], ['12', '8', '108', "bob o'mara", 'rb', 'duke'], ['13', '8', '118', 'steve slivinski', 'g', 'washington'], ['14', '8', '128', 'bob hoffman', 'rb', 'southern california'], ['15', '8', '138', 'eric tipton', 'rb', 'duke'], ['16', '8', '148', 'dick farman', 'ot', 'washington state'], ['17', '8', '158', 'clyde shugart', 'ot', 'iowa state'], ['18', '8', '168', 'boyd morgan', 'rb', 'southern california'], ['19', '8', '178', 'phil smith', 'ot', "st benedict 's"], ['20', '8', '188', 'paul coop', 'ot', 'centre'], ['21', '3', '193', 'matt kuber', 'g', 'villanova'], ['22', '3', '198', 'al cruver', 'rb', 'washington state']]
schoolhouse rock !
https://en.wikipedia.org/wiki/Schoolhouse_Rock%21
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-191105-3.html.csv
aggregation
the " science rock " episodes of " schoolhouse rock ! " have an average release year of 1978 .
{'scope': 'all', 'col': '5', 'type': 'average', 'result': '1978', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'first aired'], 'result': '1978', 'ind': 0, 'tostr': 'avg { all_rows ; first aired }'}, '1978'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; first aired } ; 1978 } = true', 'tointer': 'the average of the first aired record of all rows is 1978 .'}
round_eq { avg { all_rows ; first aired } ; 1978 } = true
the average of the first aired record of all rows is 1978 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'first aired_4': 4, '1978_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'first aired_4': 'first aired', '1978_5': '1978'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'first aired_4': [0], '1978_5': [1]}
['episode title', 'subject', 'music by', 'performed by', 'first aired']
[['the body machine', 'nutrition and digestive system', 'lynn ahrens', 'bob dorough and jack sheldon', '1979'], ['electricity , electricity', 'electricity', 'bob dorough', 'zachary sanders', '1979'], ['the energy blues', 'energy conservation', 'george newall', 'jack sheldon', '1978'], ['interplanet janet', 'the solar system', 'lynn ahrens', 'lynn ahrens', '1978'], ['telegraph line', 'nervous system', 'lynn ahrens', 'jaime aff and christine langner', '1979'], ['them not - so - dry bones', 'skeletal system', 'george newall', 'jack sheldon', '1979'], ['a victim of gravity', 'gravity', 'lynn ahrens', 'the tokens', '1978']]
sterling marlin
https://en.wikipedia.org/wiki/Sterling_Marlin
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1708014-2.html.csv
superlative
the highest number of top 10 finishes that sterling martin had was in 2005 .
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '15', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'top 10'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; top 10 }'}, 'year'], 'result': '2005', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; top 10 } ; year }'}, '2005'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; top 10 } ; year } ; 2005 } = true', 'tointer': 'select the row whose top 10 record of all rows is maximum . the year record of this row is 2005 .'}
eq { hop { argmax { all_rows ; top 10 } ; year } ; 2005 } = true
select the row whose top 10 record of all rows is maximum . the year record of this row is 2005 .
3
3
{'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'top 10_5': 5, 'year_6': 6, '2005_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'top 10_5': 'top 10', 'year_6': 'year', '2005_7': '2005'}
{'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'top 10_5': [0], 'year_6': [1], '2005_7': [2]}
['year', 'starts', 'wins', 'top 5', 'top 10', 'poles', 'avg start', 'avg finish', 'winnings', 'position', 'team ( s )']
[['1986', '1', '0', '0', '0', '0', '29.0', '29.0', '830', '133rd', '69 hagan racing'], ['1988', '4', '0', '0', '0', '0', '19.2', '17.2', '6406', '46th', '44 hagan racing'], ['1989', '2', '0', '0', '0', '0', '17.5', '32.0', '12475', '77th', '48 hagan racing'], ['1990', '5', '1', '2', '2', '0', '16.8', '14.6', '81690', '48th', '48 fred turner racing'], ['1992', '2', '0', '1', '1', '0', '15.0', '21.5', '13169', '73rd', '10 fred turner racing'], ['1993', '8', '0', '1', '2', '0', '28.1', '18.8', '36493', '41st', '48 fred turner racing'], ['1994', '9', '0', '1', '3', '0', '21.9', '25.0', '49680', '44th', '4 fred turner racing'], ['1995', '1', '0', '0', '0', '0', '7.0', '36.0', '2085', '106th', '22 fred turner racing'], ['1996', '2', '0', '1', '1', '1', '8.5', '12.5', '31285', '60th', '22 fred turner racing 92 martin racing'], ['1997', '3', '0', '0', '0', '0', '27.0', '22.7', '17020', '69th', '92 martin racing 4 phoenix racing'], ['1998', '5', '0', '0', '2', '0', '25.0', '22.0', '35649', '58th', '1 sterling marlin racing'], ['1999', '7', '0', '1', '3', '0', '9.4', '18.7', '67565', '54th', '42 joe gibbs racing 14 sterling marlin racing'], ['2000', '4', '1', '2', '3', '0', '15.0', '14.0', '56575', '62nd', '82 / 01 team sabco'], ['2004', '2', '0', '0', '0', '0', '28.5', '29.0', '36458', '102nd', '1 phoenix racing'], ['2005', '19', '0', '3', '5', '0', '23.6', '20.5', '408295', '29th', '40 / 12 fitzbradshaw racing'], ['2007', '2', '0', '0', '0', '0', '13.5', '20.5', '39605', '106th', '1 phoenix racing'], ['2008', '1', '0', '0', '0', '0', '20.0', '22.0', '25284', '118th', '1 phoenix racing']]
1979 - 80 segunda división
https://en.wikipedia.org/wiki/1979%E2%80%9380_Segunda_Divisi%C3%B3n
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12193971-2.html.csv
comparative
ca osasuna had more wins than real oviedo did .
{'row_1': '3', 'row_2': '11', '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', 'club', 'ca osasuna'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to ca osasuna .', 'tostr': 'filter_eq { all_rows ; club ; ca osasuna }'}, 'wins'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; club ; ca osasuna } ; wins }', 'tointer': 'select the rows whose club record fuzzily matches to ca osasuna . take the wins record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'real oviedo'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose club record fuzzily matches to real oviedo .', 'tostr': 'filter_eq { all_rows ; club ; real oviedo }'}, 'wins'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; club ; real oviedo } ; wins }', 'tointer': 'select the rows whose club record fuzzily matches to real oviedo . take the wins record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; club ; ca osasuna } ; wins } ; hop { filter_eq { all_rows ; club ; real oviedo } ; wins } } = true', 'tointer': 'select the rows whose club record fuzzily matches to ca osasuna . take the wins record of this row . select the rows whose club record fuzzily matches to real oviedo . take the wins record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; club ; ca osasuna } ; wins } ; hop { filter_eq { all_rows ; club ; real oviedo } ; wins } } = true
select the rows whose club record fuzzily matches to ca osasuna . take the wins record of this row . select the rows whose club record fuzzily matches to real oviedo . take the wins 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, 'club_7': 7, 'ca osasuna_8': 8, 'wins_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'club_11': 11, 'real oviedo_12': 12, 'wins_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', 'club_7': 'club', 'ca osasuna_8': 'ca osasuna', 'wins_9': 'wins', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'club_11': 'club', 'real oviedo_12': 'real oviedo', 'wins_13': 'wins'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'club_7': [0], 'ca osasuna_8': [0], 'wins_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'club_11': [1], 'real oviedo_12': [1], 'wins_13': [3]}
['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference']
[['1', 'real murcia', '38', '47 + 9', '19', '9', '10', '58', '39', '+ 19'], ['2', 'real valladolid', '38', '45 + 7', '17', '11', '10', '53', '40', '+ 13'], ['3', 'ca osasuna', '38', '44 + 6', '20', '4', '14', '74', '49', '+ 25'], ['4', 'elche cf', '38', '42 + 4', '15', '12', '11', '46', '41', '+ 5'], ['5', 'cd castellón', '38', '42 + 4', '16', '10', '12', '47', '42', '+ 5'], ['6', 'ce sabadell fc', '38', '41 + 3', '17', '7', '14', '47', '49', '- 2'], ['7', 'castilla cf', '38', '40 + 2', '15', '10', '13', '46', '39', '+ 7'], ['8', 'cádiz cf', '38', '39 + 1', '15', '9', '14', '43', '42', '+ 1'], ['9', 'deportivo alavés', '38', '38', '13', '12', '13', '37', '39', '- 2'], ['10', 'levante ud', '38', '38', '15', '8', '15', '47', '51', '- 4'], ['11', 'real oviedo', '38', '38', '13', '12', '13', '40', '45', '- 5'], ['12', 'recreativo de huelva', '38', '37 - 1', '12', '13', '13', '38', '42', '- 4'], ['13', 'granada cf', '38', '37 - 1', '13', '11', '14', '42', '42', '0'], ['14', 'getafe deportivo', '38', '37 - 1', '13', '11', '14', '44', '50', '- 6'], ['15', 'palencia cf', '38', '36 - 2', '11', '14', '13', '49', '49', '0'], ['16', 'racing de santander', '38', '36 - 2', '10', '16', '12', '34', '39', '- 5'], ['17', 'celta de vigo', '38', '35 - 3', '12', '11', '15', '48', '43', '+ 5'], ['18', 'deportivo de la coruña', '38', '35 - 3', '15', '5', '18', '49', '50', '- 1'], ['19', 'gimnàstic de tarragona', '38', '27 - 11', '8', '11', '19', '29', '61', '- 32'], ['20', 'algeciras cf', '38', '26 - 12', '7', '12', '19', '29', '48', '- 19']]
roberto traven
https://en.wikipedia.org/wiki/Roberto_Traven
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10819986-2.html.csv
comparative
roberto traven 's fight against yukiya naito had more rounds than his fight against maxim tarasov .
{'row_1': '2', 'row_2': '8', 'col': '6', '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', 'opponent', 'yukiya naito'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to yukiya naito .', 'tostr': 'filter_eq { all_rows ; opponent ; yukiya naito }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; yukiya naito } ; round }', 'tointer': 'select the rows whose opponent record fuzzily matches to yukiya naito . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'maxim tarasov'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to maxim tarasov .', 'tostr': 'filter_eq { all_rows ; opponent ; maxim tarasov }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; maxim tarasov } ; round }', 'tointer': 'select the rows whose opponent record fuzzily matches to maxim tarasov . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; yukiya naito } ; round } ; hop { filter_eq { all_rows ; opponent ; maxim tarasov } ; round } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to yukiya naito . take the round record of this row . select the rows whose opponent record fuzzily matches to maxim tarasov . take the round record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; opponent ; yukiya naito } ; round } ; hop { filter_eq { all_rows ; opponent ; maxim tarasov } ; round } } = true
select the rows whose opponent record fuzzily matches to yukiya naito . take the round record of this row . select the rows whose opponent record fuzzily matches to maxim tarasov . take the round 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, 'opponent_7': 7, 'yukiya naito_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'maxim tarasov_12': 12, 'round_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', 'opponent_7': 'opponent', 'yukiya naito_8': 'yukiya naito', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'maxim tarasov_12': 'maxim tarasov', 'round_13': 'round'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'yukiya naito_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'maxim tarasov_12': [1], 'round_13': [3]}
['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location']
[['loss', '6 - 4 - 1', 'john salter', 'ko ( punches )', 'adrenaline mma 3', '1', '2:15', 'birmingham , alabama , united states'], ['draw', '6 - 3 - 1', 'yukiya naito', 'draw', 'warriors realm 3', '3', '5:00', 'brisbane , australia'], ['loss', '6 - 3', 'elvis sinosic', 'ko ( punch )', 'warriors realm 1', '2', '0:35', 'queensland , australia'], ['loss', '6 - 2', 'frank mir', 'submission ( armbar )', 'ufc 34', '1', '1:05', 'las vegas , nevada'], ['win', '6 - 1', 'mikhail borissov', 'decision ( unanimous )', 'rings : king of kings 2000 block a', '2', '5:00', 'tokyo , japan'], ['loss', '5 - 1', 'dave menne', 'decision ( unanimous )', 'rings : king of kings 2000 block a', '3', '5:00', 'tokyo , japan'], ['win', '5 - 0', 'gueorguiev tzvetkov', 'decision ( majority )', 'rings : millennium combine 2', '2', '5:00', 'tokyo , japan'], ['win', '4 - 0', 'maxim tarasov', 'submission ( rear naked choke )', 'absolute fighting championship 2', '1', '2:47', 'moscow , russia'], ['win', '3 - 0', 'leonid efremov', 'submission ( punches )', 'absolute fighting championship 2', '1', '2:54', 'moscow , russia'], ['win', '2 - 0', 'artyom vilgulevsky', 'submission ( rear naked choke )', 'absolute fighting championship 2', '1', '2:28', 'moscow , russia'], ['win', '1 - 0', 'dave berry', 'submission ( strikes )', 'ufc 11', '1', '1:23', 'augusta , georgia , united states']]
list of macintosh models grouped by cpu type
https://en.wikipedia.org/wiki/List_of_Macintosh_models_grouped_by_CPU_type
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18640-1.html.csv
ordinal
the lisa 2 was the second earliest macintosh model to be discontinued .
{'row': '2', 'col': '6', 'order': '2', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'discontinued', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; discontinued ; 2 }'}, 'model'], 'result': 'lisa 2', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; discontinued ; 2 } ; model }'}, 'lisa 2'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; discontinued ; 2 } ; model } ; lisa 2 } = true', 'tointer': 'select the row whose discontinued record of all rows is 2nd minimum . the model record of this row is lisa 2 .'}
eq { hop { nth_argmin { all_rows ; discontinued ; 2 } ; model } ; lisa 2 } = true
select the row whose discontinued record of all rows is 2nd minimum . the model record of this row is lisa 2 .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'discontinued_5': 5, '2_6': 6, 'model_7': 7, 'lisa 2_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', 'discontinued_5': 'discontinued', '2_6': '2', 'model_7': 'model', 'lisa 2_8': 'lisa 2'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'discontinued_5': [0], '2_6': [0], 'model_7': [1], 'lisa 2_8': [2]}
['processor', 'model', 'clock speed ( mhz )', 'l1 cache ( bytes )', 'introduced', 'discontinued']
[['mc68000', 'lisa', '5', '-', 'january 1983', 'january 1984'], ['mc68000', 'lisa 2', '5', '-', 'january 1984', 'january 1985'], ['mc68000', 'macintosh', '8', '-', 'january 1984', 'october 1985'], ['mc68000', 'macintosh 512k', '8', '-', 'september 1984', 'april 1986'], ['mc68000', 'macintosh xl', '5', '-', 'january 1985', 'april 1985'], ['mc68000', 'macintosh plus', '8', '-', 'january 1986', 'october 1990'], ['mc68000', 'macintosh 512ke', '8', '-', 'april 1986', 'september 1987'], ['mc68000', 'macintosh se', '8', '-', 'march 1987', 'august 1989'], ['mc68000', 'macintosh se fdhd', '8', '-', 'august 1989', 'october 1990'], ['mc68000', 'macintosh classic', '8', '-', 'october 1990', 'september 1992'], ['mc68hc000', 'macintosh portable', '16', '-', 'september 1989', 'october 1991'], ['mc68hc000', 'powerbook 100', '16', '-', 'october 1991', 'august 1992']]
geothermal power in new zealand
https://en.wikipedia.org/wiki/Geothermal_power_in_New_Zealand
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15908826-1.html.csv
superlative
wairakei geothermal power station has the highest capacity of those listed .
{'scope': 'all', 'col_superlative': '4', 'row_superlative': '12', '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', 'capacity ( mw )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; capacity ( mw ) }'}, 'name'], 'result': 'wairakei', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; capacity ( mw ) } ; name }'}, 'wairakei'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; capacity ( mw ) } ; name } ; wairakei } = true', 'tointer': 'select the row whose capacity ( mw ) record of all rows is maximum . the name record of this row is wairakei .'}
eq { hop { argmax { all_rows ; capacity ( mw ) } ; name } ; wairakei } = true
select the row whose capacity ( mw ) record of all rows is maximum . the name record of this row is wairakei .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'capacity (mw)_5': 5, 'name_6': 6, 'wairakei_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'capacity (mw)_5': 'capacity ( mw )', 'name_6': 'name', 'wairakei_7': 'wairakei'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'capacity (mw)_5': [0], 'name_6': [1], 'wairakei_7': [2]}
['name', 'location', 'field', 'capacity ( mw )', 'annual generation ( average gwh )', 'commissioned']
[['kawerau ( bope )', 'kawerau , bay of plenty', 'kawerau', '6.3', '35', '1989 , 1993'], ['kawerau ( ka24 )', 'kawerau , bay of plenty', 'kawerau', '8.3', '70', '2008'], ['kawerau ( mrp )', 'kawerau , bay of plenty', 'kawerau', '100', '800', '2008'], ['mokai', 'northwest of taupo', 'mokai', '112', '900', '2000'], ['nga awa purua', 'north of taupo', 'rotokawa', '140', '1100', '2010'], ['ngatamariki', 'north of taupo', 'ngatamariki', '82', '600 ( approx )', '2013'], ['ngawha', 'near kaikohe , northland', 'ngawha', '25', '78', '1998'], ['ohaaki', 'between rotorua and taupo', 'ohaaki', '70', '400', '1989'], ['poihipi', 'north of taupo', 'wairakie', '55', '350', '1997'], ['rotokawa', 'north of taupo', 'rotokawa', '33', '210', '1997'], ['te huka', 'north of taupo', 'tauhara', '23', '170 ( approx )', '2010'], ['wairakei', 'north of taupo', 'wairakei', '161', '1310', '1958 , 2005']]
al - wehdat sc
https://en.wikipedia.org/wiki/Al-Wehdat_SC
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2985714-2.html.csv
comparative
al-wehdat had more draws in the jordan fa shield than in the jordan super cup .
{'row_1': '3', 'row_2': '4', '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', 'tournament', 'jordan fa shield'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to jordan fa shield .', 'tostr': 'filter_eq { all_rows ; tournament ; jordan fa shield }'}, 'draws'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; jordan fa shield } ; draws }', 'tointer': 'select the rows whose tournament record fuzzily matches to jordan fa shield . take the draws record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'jordan super cup'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to jordan super cup .', 'tostr': 'filter_eq { all_rows ; tournament ; jordan super cup }'}, 'draws'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tournament ; jordan super cup } ; draws }', 'tointer': 'select the rows whose tournament record fuzzily matches to jordan super cup . take the draws record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; tournament ; jordan fa shield } ; draws } ; hop { filter_eq { all_rows ; tournament ; jordan super cup } ; draws } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to jordan fa shield . take the draws record of this row . select the rows whose tournament record fuzzily matches to jordan super cup . take the draws record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; tournament ; jordan fa shield } ; draws } ; hop { filter_eq { all_rows ; tournament ; jordan super cup } ; draws } } = true
select the rows whose tournament record fuzzily matches to jordan fa shield . take the draws record of this row . select the rows whose tournament record fuzzily matches to jordan super cup . take the draws record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'tournament_7': 7, 'jordan fa shield_8': 8, 'draws_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tournament_11': 11, 'jordan super cup_12': 12, 'draws_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'tournament_7': 'tournament', 'jordan fa shield_8': 'jordan fa shield', 'draws_9': 'draws', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tournament_11': 'tournament', 'jordan super cup_12': 'jordan super cup', 'draws_13': 'draws'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tournament_7': [0], 'jordan fa shield_8': [0], 'draws_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tournament_11': [1], 'jordan super cup_12': [1], 'draws_13': [3]}
['', 'tournament', 'al - faisaly wins', 'al - wehdat wins', 'draws', 'total', 'al - faisaly goals', 'al - wehdat goals']
[['1', 'jordan premier league', '25', '26', '22', '73', '66', '69'], ['2', 'jordan fa cup', '6', '7', '5', '18', '23', '23'], ['3', 'jordan fa shield', '8', '5', '3', '16', '19', '14'], ['4', 'jordan super cup', '4', '5', '2', '11', '13', '13'], ['5', 'afc cup', '3', '0', '1', '4', '4', '2']]
districts of portugal
https://en.wikipedia.org/wiki/Districts_of_Portugal
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-221375-1.html.csv
aggregation
there are a total of 3,149 parishes in the districts of portugal .
{'scope': 'all', 'col': '3', 'type': 'sum', 'result': '3149', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'parishes'], 'result': '3149', 'ind': 0, 'tostr': 'sum { all_rows ; parishes }'}, '3149'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; parishes } ; 3149 } = true', 'tointer': 'the sum of the parishes record of all rows is 3149 .'}
round_eq { sum { all_rows ; parishes } ; 3149 } = true
the sum of the parishes record of all rows is 3149 .
2
2
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'parishes_4': 4, '3149_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'parishes_4': 'parishes', '3149_5': '3149'}
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'parishes_4': [0], '3149_5': [1]}
['district', 'municipalities', 'parishes', 'province of 1936', 'region']
[['aveiro', '19', '208', 'beira litoral province + douro litoral province', 'norte , centro'], ['beja', '14', '100', 'baixo alentejo', 'alentejo'], ['braga', '14', '515', 'minho', 'norte'], ['bragança', '12', '299', 'trás - os - montes e alto douro province', 'norte'], ['castelo branco', '11', '160', 'beira baixa province', 'centro'], ['coimbra', '17', '209', 'beira baixa province , beira litoral', 'centro'], ['évora', '14', '91', 'alto alentejo', 'alentejo'], ['faro', '16', '84', 'algarve province', 'algarve'], ['leiria', '16', '148', 'beira litoral province , estremadura', 'centro'], ['lisbon', '16', '226', 'estremadura ( partly ribatejo )', 'lisbon ( partly alentejo )'], ['portalegre', '15', '86', 'alto alentejo province ( partly ribatejo )', 'alentejo'], ['porto', '18', '383', 'douro litoral province', 'norte'], ['setúbal', '13', '82', 'estremadura province , baixo alentejo province', 'lisbon , alentejo'], ['viana do castelo', '10', '290', 'minho', 'norte'], ['vila real', '14', '268', 'trás - os - montes e alto douro', 'norte']]
list of tournament performances by tiger woods
https://en.wikipedia.org/wiki/List_of_tournament_performances_by_Tiger_Woods
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13298049-7.html.csv
unique
the only year that tiger woods ' score was higher than 280 was in 1998 .
{'scope': 'all', 'row': '1', 'col': '7', 'col_other': '1', 'criterion': 'greater_than', 'value': '280', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'score', '280'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record is greater than 280 .', 'tostr': 'filter_greater { all_rows ; score ; 280 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; score ; 280 } }', 'tointer': 'select the rows whose score record is greater than 280 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'score', '280'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose score record is greater than 280 .', 'tostr': 'filter_greater { all_rows ; score ; 280 }'}, 'year'], 'result': '1998', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; score ; 280 } ; year }'}, '1998'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; score ; 280 } ; year } ; 1998 }', 'tointer': 'the year record of this unqiue row is 1998 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; score ; 280 } } ; eq { hop { filter_greater { all_rows ; score ; 280 } ; year } ; 1998 } } = true', 'tointer': 'select the rows whose score record is greater than 280 . there is only one such row in the table . the year record of this unqiue row is 1998 .'}
and { only { filter_greater { all_rows ; score ; 280 } } ; eq { hop { filter_greater { all_rows ; score ; 280 } ; year } ; 1998 } } = true
select the rows whose score record is greater than 280 . there is only one such row in the table . the year record of this unqiue row is 1998 .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'score_7': 7, '280_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1998_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'score_7': 'score', '280_8': '280', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1998_10': '1998'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'score_7': [0], '280_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1998_10': [3]}
['year', 'tournament', 'round 1', 'round 2', 'round 3', 'round 4', 'score', 'to par', 'place', 'money ( ¥ )']
[['1998', 'casio world open', '69', '74', '71', '70', '284', '4', 't15', '1602000'], ['2002', 'dunlop phoenix tournament', '71', '68', '69', '67', '275', '9', '8', '6100000'], ['2004', 'dunlop phoenix tournament', '65', '67', '65', '67', '264', '16', '1', '40000000'], ['2005', 'dunlop phoenix tournament', '65', '67', '68', '72', '272', '8', '1', '40000000'], ['2006', 'dunlop phoenix tournament', '67', '65', '72', '67', '271', '9', '2', '20000000']]
2007 - 08 rugby - bundesliga
https://en.wikipedia.org/wiki/2007%E2%80%9308_Rugby-Bundesliga
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20877272-5.html.csv
aggregation
the average number of games lost by all clubs in the 2007 - 08 rugby - bundesliga competition is 7 .
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '7', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'lost'], 'result': '7', 'ind': 0, 'tostr': 'avg { all_rows ; lost }'}, '7'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; lost } ; 7 } = true', 'tointer': 'the average of the lost record of all rows is 7 .'}
round_eq { avg { all_rows ; lost } ; 7 } = true
the average of the lost record of all rows is 7 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'lost_4': 4, '7_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'lost_4': 'lost', '7_5': '7'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'lost_4': [0], '7_5': [1]}
['', 'club', 'played', 'won', 'drawn', 'lost', 'points for', 'points against', 'difference', 'points']
[['1', 'rk 03 berlin', '16', '14', '0', '2', '714', '158', '556', '44'], ['2', 'tsv victoria linden', '16', '12', '0', '4', '527', '232', '295', '40'], ['3', 'fc st pauli rugby', '16', '11', '0', '5', '554', '300', '254', '38'], ['4', 'dsv 78 / 08 ricklingen', '16', '10', '0', '6', '504', '265', '239', '36'], ['5', 'sc germania list', '16', '8', '0', '8', '313', '337', '- 24', '32'], ['6', 'sv odin hannover', '16', '7', '1', '8', '280', '306', '- 26', '29'], ['7', 'usv potsdam', '16', '6', '1', '9', '350', '503', '- 153', '27'], ['8', 'hamburger rc', '16', '2', '0', '14', '137', '556', '- 419', '20']]
south wales derby
https://en.wikipedia.org/wiki/South_Wales_derby
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15473253-4.html.csv
superlative
south wales derby had the most swanson wins in the league competition .
{'scope': 'all', 'col_superlative': '5', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'swansea win'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; swansea win }'}, 'competition'], 'result': 'league', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; swansea win } ; competition }'}, 'league'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; swansea win } ; competition } ; league } = true', 'tointer': 'select the row whose swansea win record of all rows is maximum . the competition record of this row is league .'}
eq { hop { argmax { all_rows ; swansea win } ; competition } ; league } = true
select the row whose swansea win record of all rows is maximum . the competition record of this row is league .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'swansea win_5': 5, 'competition_6': 6, 'league_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'swansea win_5': 'swansea win', 'competition_6': 'competition', 'league_7': 'league'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'swansea win_5': [0], 'competition_6': [1], 'league_7': [2]}
['competition', 'total matches', 'cardiff win', 'draw', 'swansea win']
[['league', '55', '19', '16', '20'], ['fa cup', '2', '0', '0', '2'], ['league cup', '5', '2', '0', '3'], ['associate members cup', '4', '1', '1', '2'], ['welsh cup / faw premier cup', '36', '21', '8', '7'], ['southern league', '4', '1', '2', '1'], ['total', '106', '44', '27', '35']]
1998 open championship
https://en.wikipedia.org/wiki/1998_Open_Championship
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18049082-4.html.csv
aggregation
in the '98 open championship , the top golfers from the united states had an average first round score of 66 .
{'scope': 'subset', 'col': '4', 'type': 'average', 'result': '66', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'united states'}}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record fuzzily matches to united states .'}, 'score'], 'result': '66', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; country ; united states } ; score }'}, '66'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 66 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 66 .'}
round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 66 } = true
select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 66 .
3
3
{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'country_5': 5, 'united states_6': 6, 'score_7': 7, '66_8': 8}
{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'country_5': 'country', 'united states_6': 'united states', 'score_7': 'score', '66_8': '66'}
{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'united states_6': [0], 'score_7': [1], '66_8': [2]}
['place', 'player', 'country', 'score', 'to par']
[['t1', 'john huston', 'united states', '65', '- 5'], ['t1', 'tiger woods', 'united states', '65', '- 5'], ['t3', 'fred couples', 'united states', '66', '- 4'], ['t3', 'nick price', 'zimbabwe', '66', '- 4'], ['t3', 'loren roberts', 'united states', '66', '- 4'], ['t6', 'robert allenby', 'australia', '67', '- 3'], ['t6', 'brad faxon', 'united states', '67', '- 3'], ['t6', 'fredrik jacobson', 'sweden', '67', '- 3'], ['t6', 'davis love iii', 'united states', '67', '- 3'], ['t6', 'vijay singh', 'fiji', '67', '- 3']]
indiana high school athletics conferences : ohio river valley - western indiana
https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Ohio_River_Valley_%E2%80%93_Western_Indiana
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18974097-9.html.csv
superlative
in the western indiana ohio river valley high school athletics conference , the school in 28 greene county with the highest enrollment is eastern greene in bloomfield .
{'scope': 'subset', 'col_superlative': '4', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': {'col': '6', 'criterion': 'equal', 'value': '28 greene'}}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'county', '28 greene'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; county ; 28 greene }', 'tointer': 'select the rows whose county record fuzzily matches to 28 greene .'}, 'enrollment'], 'result': None, 'ind': 1, 'tostr': 'argmax { filter_eq { all_rows ; county ; 28 greene } ; enrollment }'}, 'location'], 'result': 'bloomfield', 'ind': 2, 'tostr': 'hop { argmax { filter_eq { all_rows ; county ; 28 greene } ; enrollment } ; location }'}, 'bloomfield'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; county ; 28 greene } ; enrollment } ; location } ; bloomfield } = true', 'tointer': 'select the rows whose county record fuzzily matches to 28 greene . select the row whose enrollment record of these rows is maximum . the location record of this row is bloomfield .'}
eq { hop { argmax { filter_eq { all_rows ; county ; 28 greene } ; enrollment } ; location } ; bloomfield } = true
select the rows whose county record fuzzily matches to 28 greene . select the row whose enrollment record of these rows is maximum . the location record of this row is bloomfield .
4
4
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmax_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'county_6': 6, '28 greene_7': 7, 'enrollment_8': 8, 'location_9': 9, 'bloomfield_10': 10}
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmax_1': 'argmax', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'county_6': 'county', '28 greene_7': '28 greene', 'enrollment_8': 'enrollment', 'location_9': 'location', 'bloomfield_10': 'bloomfield'}
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmax_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'county_6': [0], '28 greene_7': [0], 'enrollment_8': [1], 'location_9': [2], 'bloomfield_10': [3]}
['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'county']
[['bloomfield', 'bloomfield', 'cardinals', '337', 'aa', '28 greene'], ['clay city', 'clay city', 'eels', '272', 'a', '11 clay'], ['eastern greene', 'bloomfield', 'thunderbirds', '406', 'aa', '28 greene'], ['linton stockton', 'linton', 'miners', '344', 'aa', '28 greene'], ['north central farmersburg', 'farmersburg', 'thunderbirds', '336', 'aa', '77 sullivan'], ['north daviess', 'elnora', 'cougars', '306', 'a', '14 daviess'], ['shakamak', 'jasonville', 'lakers', '258', 'a', '28 greene'], ['union dugger', 'dugger', 'bulldogs', '117', 'a', '77 sullivan'], ['white river valley', 'switz city', 'wolverines', '253', 'a', '28 greene']]
wang fengchun
https://en.wikipedia.org/wiki/Wang_Fengchun
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16871409-1.html.csv
comparative
xu xiaoming served as the skip before wang fengchun did .
{'row_1': '2', 'row_2': '3', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'skip', 'xu xiaoming'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose skip record fuzzily matches to xu xiaoming .', 'tostr': 'filter_eq { all_rows ; skip ; xu xiaoming }'}, 'season'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; skip ; xu xiaoming } ; season }', 'tointer': 'select the rows whose skip record fuzzily matches to xu xiaoming . take the season record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'skip', 'wang fengchun'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose skip record fuzzily matches to wang fengchun .', 'tostr': 'filter_eq { all_rows ; skip ; wang fengchun }'}, 'season'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; skip ; wang fengchun } ; season }', 'tointer': 'select the rows whose skip record fuzzily matches to wang fengchun . take the season record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; skip ; xu xiaoming } ; season } ; hop { filter_eq { all_rows ; skip ; wang fengchun } ; season } } = true', 'tointer': 'select the rows whose skip record fuzzily matches to xu xiaoming . take the season record of this row . select the rows whose skip record fuzzily matches to wang fengchun . take the season record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; skip ; xu xiaoming } ; season } ; hop { filter_eq { all_rows ; skip ; wang fengchun } ; season } } = true
select the rows whose skip record fuzzily matches to xu xiaoming . take the season record of this row . select the rows whose skip record fuzzily matches to wang fengchun . take the season 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, 'skip_7': 7, 'xu xiaoming_8': 8, 'season_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'skip_11': 11, 'wang fengchun_12': 12, 'season_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', 'skip_7': 'skip', 'xu xiaoming_8': 'xu xiaoming', 'season_9': 'season', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'skip_11': 'skip', 'wang fengchun_12': 'wang fengchun', 'season_13': 'season'}
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'skip_7': [0], 'xu xiaoming_8': [0], 'season_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'skip_11': [1], 'wang fengchun_12': [1], 'season_13': [3]}
['season', 'skip', 'third', 'second', 'lead', 'alternate', 'events']
[['2002 - 03', 'xu xiaoming', 'wang fengchun', 'yu zhu', 'liu rui', 'ma yongjun', '2002 pcc'], ['2005 - 06', 'xu xiaoming', 'li hongchen', 'wang fengchun', 'liu rui', 'ma yongjun', '2005 pcc'], ['2007 - 08', 'wang fengchun', 'liu rui', 'xu xiaoming', 'zang jialiang', 'li dongyan', '2008 wmcc'], ['2008 - 09', 'wang fengchun', 'liu rui', 'xu xiaoming', 'zang jialiang', "chen lu'an", '2009 wmcc'], ['2009 - 10', 'liu rui', 'wang fengchun ( skip )', 'xu xiaoming', 'zang jialiang', 'li hongchen', '2010 olympic games']]
list of mountains of the british isles by relative height
https://en.wikipedia.org/wiki/List_of_mountains_of_the_British_Isles_by_relative_height
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1456056-1.html.csv
superlative
scotland has the most mountains over 4000 ft out of the countries in the british isles , with a total of 2 .
{'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', '4000ft +'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; 4000ft + }'}, 'country'], 'result': 'scotland', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; 4000ft + } ; country }'}, 'scotland'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; 4000ft + } ; country } ; scotland } = true', 'tointer': 'select the row whose 4000ft + record of all rows is maximum . the country record of this row is scotland .'}
eq { hop { argmax { all_rows ; 4000ft + } ; country } ; scotland } = true
select the row whose 4000ft + record of all rows is maximum . the country record of this row is scotland .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, '4000ft +_5': 5, 'country_6': 6, 'scotland_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', '4000ft +_5': '4000ft +', 'country_6': 'country', 'scotland_7': 'scotland'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], '4000ft +_5': [0], 'country_6': [1], 'scotland_7': [2]}
['country', 'total', '4000ft +', '3500 - 4000ft', '3000 - 3500ft', '2500 - 3000ft', '2000 - 2500ft']
[['scotland', '82', '2', '21', '31', '21', '7'], ['ireland', '24', '0', '0', '4', '8', '12'], ['wales', '7', '0', '1', '2', '4', '0'], ['england', '4', '0', '0', '3', '1', '0'], ['northern ireland', '1', '0', '0', '0', '1', '0'], ['isle of man', '1', '0', '0', '0', '0', '1']]
jean - marc gounon
https://en.wikipedia.org/wiki/Jean-Marc_Gounon
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1234917-2.html.csv
aggregation
jean - marc gounon successfully completed 2021 total laps in his career .
{'scope': 'all', 'col': '5', 'type': 'sum', 'result': '2021', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'laps'], 'result': '2021', 'ind': 0, 'tostr': 'sum { all_rows ; laps }'}, '2021'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; laps } ; 2021 } = true', 'tointer': 'the sum of the laps record of all rows is 2021 .'}
round_eq { sum { all_rows ; laps } ; 2021 } = true
the sum of the laps record of all rows is 2021 .
2
2
{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'laps_4': 4, '2021_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'laps_4': 'laps', '2021_5': '2021'}
{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'laps_4': [0], '2021_5': [1]}
['year', 'team', 'co - drivers', 'class', 'laps', 'pos', 'class pos']
[['1995', 'venturi automobiles', 'paul belmondo arnaud trévisiol', 'gt1', '193', 'nc', 'nc'], ['1996', 'ennea srl igol', 'éric bernard paul belmondo', 'gt1', '40', 'dnf', 'dnf'], ['1997', 'gulf team davidoff gtc racing', 'pierre - henri raphanel anders olofsson', 'gt1', '360', '2nd', '1st'], ['1998', 'amg - mercedes', 'ricardo zonta christophe bouchut', 'gt1', '31', 'dnf', 'dnf'], ['2000', 'thomas bscher promotion david price racing', 'thomas bscher geoff lees', 'lmp900', '180', 'dnf', 'dnf'], ['2003', 'courage compétition', 'jonathan cochet stéphan grégoire', 'lmp900', '360', '7th', '5th'], ['2004', 'courage compétition', 'alexander frei sam hancock', 'lmp2', '127', 'dnf', 'dnf'], ['2005', 'audi playstation team oreca', 'franck montagny stéphane ortelli', 'lmp1', '362', '4th', '4th'], ['2006', 'courage compétition', 'shinji nakano haruki kurosawa', 'lmp1', '35', 'dnf', 'dnf'], ['2007', 'courage compétition', 'guillaume moreau stefan johansson', 'lmp1', '175', 'dnf', 'dnf'], ['2008', 'epsilon euskadi', 'shinji nakano stefan johansson', 'lmp2', '158', 'dnf', 'dnf']]
flin flon bombers
https://en.wikipedia.org/wiki/Flin_Flon_Bombers
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1216375-2.html.csv
comparative
chuck arnason scored more goals than reggie leach had scored .
{'row_1': '4', 'row_2': '3', 'col': '4', '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', 'winner', 'chuck arnason'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winner record fuzzily matches to chuck arnason .', 'tostr': 'filter_eq { all_rows ; winner ; chuck arnason }'}, 'goals'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winner ; chuck arnason } ; goals }', 'tointer': 'select the rows whose winner record fuzzily matches to chuck arnason . take the goals record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winner', 'reggie leach'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose winner record fuzzily matches to reggie leach .', 'tostr': 'filter_eq { all_rows ; winner ; reggie leach }'}, 'goals'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; winner ; reggie leach } ; goals }', 'tointer': 'select the rows whose winner record fuzzily matches to reggie leach . take the goals record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; winner ; chuck arnason } ; goals } ; hop { filter_eq { all_rows ; winner ; reggie leach } ; goals } } = true', 'tointer': 'select the rows whose winner record fuzzily matches to chuck arnason . take the goals record of this row . select the rows whose winner record fuzzily matches to reggie leach . take the goals record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; winner ; chuck arnason } ; goals } ; hop { filter_eq { all_rows ; winner ; reggie leach } ; goals } } = true
select the rows whose winner record fuzzily matches to chuck arnason . take the goals record of this row . select the rows whose winner record fuzzily matches to reggie leach . take the goals record of this row . the first record is greater than the second record .
5
5
{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winner_7': 7, 'chuck arnason_8': 8, 'goals_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'winner_11': 11, 'reggie leach_12': 12, 'goals_13': 13}
{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winner_7': 'winner', 'chuck arnason_8': 'chuck arnason', 'goals_9': 'goals', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'winner_11': 'winner', 'reggie leach_12': 'reggie leach', 'goals_13': 'goals'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'winner_7': [0], 'chuck arnason_8': [0], 'goals_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'winner_11': [1], 'reggie leach_12': [1], 'goals_13': [3]}
['season', 'league', 'winner', 'goals', 'assists', 'points']
[['1967 - 68', 'wcjhl', 'bobby clarke', '51', '117', '168'], ['1968 - 69', 'wcjhl', 'bobby clarke', '51', '86', '137'], ['1969 - 70', 'wchl', 'reggie leach', '65', '46', '111'], ['1970 - 71', 'wchl', 'chuck arnason', '79', '84', '163'], ['2007 - 08', 'sjhl', 'reid macleod', '47', '42', '89']]
2011 pacific games
https://en.wikipedia.org/wiki/2011_Pacific_Games
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16777236-1.html.csv
ordinal
the third highest number of silver medals won at the 2011 pacific games was the holder of rank 2 .
{'row': '2', 'col': '3', 'order': '3', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None}
{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_max', 'args': ['all_rows', 'silver', '3'], 'result': '42', 'ind': 0, 'tostr': 'nth_max { all_rows ; silver ; 3 }', 'tointer': 'the 3rd maximum silver record of all rows is 42 .'}, '42'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_max { all_rows ; silver ; 3 } ; 42 }', 'tointer': 'the 3rd maximum silver record of all rows is 42 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'silver', '3'], 'result': None, 'ind': 2, 'tostr': 'nth_argmax { all_rows ; silver ; 3 }'}, 'rank'], 'result': '2', 'ind': 3, 'tostr': 'hop { nth_argmax { all_rows ; silver ; 3 } ; rank }'}, '2'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmax { all_rows ; silver ; 3 } ; rank } ; 2 }', 'tointer': 'the rank record of the row with 3rd maximum silver record is 2 .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_max { all_rows ; silver ; 3 } ; 42 } ; eq { hop { nth_argmax { all_rows ; silver ; 3 } ; rank } ; 2 } } = true', 'tointer': 'the 3rd maximum silver record of all rows is 42 . the rank record of the row with 3rd maximum silver record is 2 .'}
and { eq { nth_max { all_rows ; silver ; 3 } ; 42 } ; eq { hop { nth_argmax { all_rows ; silver ; 3 } ; rank } ; 2 } } = true
the 3rd maximum silver record of all rows is 42 . the rank record of the row with 3rd maximum silver record is 2 .
6
6
{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_max_0': 0, 'all_rows_7': 7, 'silver_8': 8, '3_9': 9, '42_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmax_2': 2, 'all_rows_11': 11, 'silver_12': 12, '3_13': 13, 'rank_14': 14, '2_15': 15}
{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_max_0': 'nth_max', 'all_rows_7': 'all_rows', 'silver_8': 'silver', '3_9': '3', '42_10': '42', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmax_2': 'nth_argmax', 'all_rows_11': 'all_rows', 'silver_12': 'silver', '3_13': '3', 'rank_14': 'rank', '2_15': '2'}
{'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_max_0': [1], 'all_rows_7': [0], 'silver_8': [0], '3_9': [0], '42_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmax_2': [3], 'all_rows_11': [2], 'silver_12': [2], '3_13': [2], 'rank_14': [3], '2_15': [4]}
['rank', 'gold', 'silver', 'bronze', 'total']
[['1', '120', '107', '61', '288'], ['2', '60', '42', '42', '144'], ['3', '48', '25', '48', '121'], ['4', '33', '44', '53', '130'], ['5', '22', '17', '34', '73'], ['6', '8', '10', '10', '28'], ['7', '4', '6', '10', '20'], ['8', '3', '0', '0', '3'], ['9', '2', '6', '4', '12'], ['10', '2', '3', '7', '12'], ['11', '1', '8', '8', '17'], ['12', '1', '6', '6', '13'], ['13', '1', '0', '0', '1'], ['14', '0', '6', '5', '11'], ['15', '0', '5', '17', '22'], ['16', '0', '3', '3', '6'], ['17', '0', '2', '1', '3'], ['18', '0', '1', '3', '4'], ['19', '0', '0', '0', '0'], ['total', '305', '291', '312', '908']]
list of england national rugby union team results 2000 - 09
https://en.wikipedia.org/wiki/List_of_England_national_rugby_union_team_results_2000%E2%80%9309
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18178551-1.html.csv
aggregation
the average against for the 2000-09 england national rugby union team was 14.6 .
{'scope': 'all', 'col': '2', 'type': 'average', 'result': '14.6', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'against'], 'result': '14.6', 'ind': 0, 'tostr': 'avg { all_rows ; against }'}, '14.6'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; against } ; 14.6 } = true', 'tointer': 'the average of the against record of all rows is 14.6 .'}
round_eq { avg { all_rows ; against } ; 14.6 } = true
the average of the against record of all rows is 14.6 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'against_4': 4, '14.6_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'against_4': 'against', '14.6_5': '14.6'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'against_4': [0], '14.6_5': [1]}
['opposing teams', 'against', 'date', 'venue', 'status']
[['ireland', '18', '05 / 02 / 2000', 'twickenham , london', 'six nations'], ['france', '9', '19 / 02 / 2000', 'stade de france , saint - denis', 'six nations'], ['wales', '12', '04 / 03 / 2000', 'twickenham , london', 'six nations'], ['italy', '12', '18 / 03 / 2000', 'stadio flaminio , rome', 'six nations'], ['scotland', '19', '02 / 04 / 2000', 'murrayfield , edinburgh', 'six nations'], ['south africa', '18', '17 / 06 / 2000', 'loftus versfeld , pretoria', 'first test'], ['south africa', '22', '24 / 06 / 2000', 'vodacom park , bloemfontein', 'second test'], ['australia', '19', '18 / 11 / 2000', 'twickenham , london', 'test match'], ['argentina', '0', '25 / 11 / 2000', 'twickenham , london', 'test match'], ['south africa', '17', '02 / 12 / 2000', 'twickenham , london', 'test match']]
ken flach
https://en.wikipedia.org/wiki/Ken_Flach
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2362486-1.html.csv
count
there were three occasions where the surface was hard .
{'scope': 'all', 'criterion': 'equal', 'value': 'hard', 'result': '3', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'hard'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to hard .', 'tostr': 'filter_eq { all_rows ; surface ; hard }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; hard } }', 'tointer': 'select the rows whose surface record fuzzily matches to hard . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; hard } } ; 3 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to hard . the number of such rows is 3 .'}
eq { count { filter_eq { all_rows ; surface ; hard } } ; 3 } = true
select the rows whose surface record fuzzily matches to hard . 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, 'surface_5': 5, 'hard_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', 'surface_5': 'surface', 'hard_6': 'hard', '3_7': '3'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'hard_6': [0], '3_7': [2]}
['outcome', 'year', 'championship', 'surface', 'partner', 'opponents in the final', 'score in the final']
[['winner', '1985', 'us open', 'hard', 'robert seguso', 'henri leconte yannick noah', '6 - 7 ( 5 ) , 7 - 6 ( 1 ) , 7 - 6 ( 6 ) , 6 - 0'], ['winner', '1987', 'wimbledon', 'grass', 'robert seguso', 'sergio casal emilio sánchez vicario', '3 - 6 , 6 - 7 ( 6 ) , 7 - 6 ( 3 ) , 6 - 1 , 6 - 4'], ['runner - up', '1987', 'us open', 'hard', 'robert seguso', 'stefan edberg anders järryd', '6 - 7 ( 1 ) , 2 - 6 , 6 - 4 , 7 - 5 , 6 - 7 ( 2 )'], ['winner', '1988', 'wimbledon ( 2 )', 'grass', 'robert seguso', 'john fitzgerald anders järryd', '6 - 4 , 2 - 6 , 6 - 4 , 7 - 6 ( 3 )'], ['runner - up', '1989', 'us open', 'hard', 'robert seguso', 'john mcenroe mark woodforde', '4 - 6 , 6 - 4 , 3 - 6 , 3 - 6']]
1987 indianapolis colts season
https://en.wikipedia.org/wiki/1987_Indianapolis_Colts_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14871429-1.html.csv
majority
indianapolis colts won most games in the month of october during the 1987 season .
{'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'w', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'october'}}
{'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'october'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; october }', 'tointer': 'select the rows whose date record fuzzily matches to october .'}, 'result', 'w'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to w .', 'tostr': 'most_eq { filter_eq { all_rows ; date ; october } ; result ; w } = true'}
most_eq { filter_eq { all_rows ; date ; october } ; result ; w } = true
select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to w .
2
2
{'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'october_5': 5, 'result_6': 6, 'w_7': 7}
{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'october_5': 'october', 'result_6': 'result', 'w_7': 'w'}
{'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'october_5': [0], 'result_6': [1], 'w_7': [1]}
['week', 'date', 'opponent', 'result', 'record', 'game site']
[['1', 'september 13 , 1987', 'cincinnati bengals', 'l 21 - 23', '0 - 1', 'hoosier dome'], ['2', 'september 20 , 1987', 'miami dolphins', 'l 10 - 23', '0 - 2', 'hoosier dome'], ['3', 'september 27 , 1987', 'st louis cardinals', 'canceled', '0 - 2', 'busch memorial stadium'], ['4', 'october 4 , 1987', 'buffalo bills', 'w 47 - 6', '1 - 2', 'rich stadium'], ['5', 'october 11 , 1987', 'new york jets', 'w 6 - 0', '2 - 2', 'hoosier dome'], ['6', 'october 18 , 1987', 'pittsburgh steelers', 'l 7 - 21', '2 - 3', 'three rivers stadium'], ['7', 'october 25 , 1987', 'new england patriots', 'w 30 - 16', '3 - 3', 'hoosier dome'], ['8', 'november 1 , 1987', 'new york jets', 'w 19 - 14', '4 - 3', 'the meadowlands'], ['9', 'november 8 , 1987', 'san diego chargers', 'l 13 - 16', '4 - 4', 'hoosier dome'], ['10', 'november 15 , 1987', 'miami dolphins', 'w 40 - 20', '5 - 4', 'joe robbie stadium'], ['11', 'november 22 , 1987', 'new england patriots', 'l 0 - 24', '5 - 5', 'sullivan stadium'], ['12', 'november 29 , 1987', 'houston oilers', 'w 51 - 27', '6 - 5', 'hoosier dome'], ['13', 'december 6 , 1987', 'cleveland browns', 'w 9 - 7', '7 - 5', 'cleveland stadium'], ['14', 'december 13 , 1987', 'buffalo bills', 'l 3 - 27', '7 - 6', 'hoosier dome'], ['15', 'december 20 , 1987', 'san diego chargers', 'w 20 - 7', '8 - 6', 'jack murphy stadium'], ['16', 'december 27 , 1987', 'tampa bay buccaneers', 'w 24 - 6', '9 - 6', 'hoosier dome']]
list of tvb series ( 2006 )
https://en.wikipedia.org/wiki/List_of_TVB_series_%282006%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10942714-1.html.csv
superlative
la femme desperado was the 2006 tvb series that drew the highest amount of hk viewers .
{'scope': 'all', 'col_superlative': '8', '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', 'hk viewers'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; hk viewers }'}, 'english title'], 'result': 'la femme desperado', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; hk viewers } ; english title }'}, 'la femme desperado'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; hk viewers } ; english title } ; la femme desperado } = true', 'tointer': 'select the row whose hk viewers record of all rows is maximum . the english title record of this row is la femme desperado .'}
eq { hop { argmax { all_rows ; hk viewers } ; english title } ; la femme desperado } = true
select the row whose hk viewers record of all rows is maximum . the english title record of this row is la femme desperado .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'hk viewers_5': 5, 'english title_6': 6, 'la femme desperado_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'hk viewers_5': 'hk viewers', 'english title_6': 'english title', 'la femme desperado_7': 'la femme desperado'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'hk viewers_5': [0], 'english title_6': [1], 'la femme desperado_7': [2]}
['rank', 'english title', 'chinese title', 'average', 'peak', 'premiere', 'finale', 'hk viewers']
[['1', 'la femme desperado', '女人唔易做', '33', '41', '31', '34', '2.14 million'], ['2', 'forensic heroes', '法證先鋒', '33', '43', '28', '37', '2.11 million'], ['3', 'the saviour of the soul', '神鵰俠侶', '32', '40', '32', '35', '2.07 million'], ['4', 'love guaranteed', '愛情全保', '32', '36', '30', '34', '2.07 million'], ['5', 'bar bender', '潮爆大狀', '32', '38', '31', '34', '2.06 million'], ['6', 'the dance of passion', '火舞黃沙', '32', '38', '34', '35', '2.05 million'], ['7', "maiden 's vow", '鳳凰四重奏', '32', '37', '32', '29', '2.05 million'], ['8', 'to grow with love', '肥田囍事', '32', '35', '32', '32', '2.04 million'], ['9', 'men in pain', '男人之苦', '32', '39', '28', '33', '2.03 million'], ['10', 'under the canopy of love', '天幕下的戀人', '31', '37', '28', '33', '2.02 million']]
alain ngalani
https://en.wikipedia.org/wiki/Alain_Ngalani
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11673825-1.html.csv
comparative
ramazan ramazanov went more rounds than eduarda maiorino against alain ngalani .
{'row_1': '2', 'row_2': '5', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'ramazan ramazanov'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to ramazan ramazanov .', 'tostr': 'filter_eq { all_rows ; opponent ; ramazan ramazanov }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; ramazan ramazanov } ; round }', 'tointer': 'select the rows whose opponent record fuzzily matches to ramazan ramazanov . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'eduardo maiorino'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to eduardo maiorino .', 'tostr': 'filter_eq { all_rows ; opponent ; eduardo maiorino }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; eduardo maiorino } ; round }', 'tointer': 'select the rows whose opponent record fuzzily matches to eduardo maiorino . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; opponent ; ramazan ramazanov } ; round } ; hop { filter_eq { all_rows ; opponent ; eduardo maiorino } ; round } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to ramazan ramazanov . take the round record of this row . select the rows whose opponent record fuzzily matches to eduardo maiorino . take the round record of this row . the first record is greater than the second record .'}
greater { hop { filter_eq { all_rows ; opponent ; ramazan ramazanov } ; round } ; hop { filter_eq { all_rows ; opponent ; eduardo maiorino } ; round } } = true
select the rows whose opponent record fuzzily matches to ramazan ramazanov . take the round record of this row . select the rows whose opponent record fuzzily matches to eduardo maiorino . take the round 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, 'opponent_7': 7, 'ramazan ramazanov_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'eduardo maiorino_12': 12, 'round_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', 'opponent_7': 'opponent', 'ramazan ramazanov_8': 'ramazan ramazanov', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'eduardo maiorino_12': 'eduardo maiorino', 'round_13': 'round'}
{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'ramazan ramazanov_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'eduardo maiorino_12': [1], 'round_13': [3]}
['result', 'opponent', 'method', 'event', 'round', 'location']
[['no contest', 'då ¾ evad poturak', 'no contest ( punch after knockdown )', 'elite kickboxing', '1', 'las vegas , nevada , usa'], ['no contest', 'ramazan ramazanov', 'no contest ( ring invasion )', 'planet battle vi', '2', 'wan chai , hong kong'], ['win', 'carter williams', 'tko ( low kicks )', 'planet battle v', '2', 'hong kong'], ['win', 'bob sapp', 'decision', 'planet battle iv', '3', 'wan chai , hong kong'], ['win', 'eduardo maiorino', 'ko ( punches )', 'planet battle iii', '1', 'hong kong'], ['loss', 'brian douwes', 'ko ( knee )', 'planet battle ii', '3', 'hong kong'], ['win', 'michael mcdonald', 'decision', 'planet battle i', '3', 'hong kong']]
china 's famous teas
https://en.wikipedia.org/wiki/China%27s_Famous_Teas
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2224781-1.html.csv
aggregation
all china 's famous teas have an average occurrence of 10 .
{'scope': 'all', 'col': '7', 'type': 'average', 'result': '10', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'occurrences'], 'result': '10', 'ind': 0, 'tostr': 'avg { all_rows ; occurrences }'}, '10'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; occurrences } ; 10 } = true', 'tointer': 'the average of the occurrences record of all rows is 10 .'}
round_eq { avg { all_rows ; occurrences } ; 10 } = true
the average of the occurrences record of all rows is 10 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'occurrences_4': 4, '10_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'occurrences_4': 'occurrences', '10_5': '10'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'occurrences_4': [0], '10_5': [1]}
['', 'translated english name', 'chinese', 'pronunciation', 'place of origin', 'type', 'occurrences']
[['1', 'dragon well', '西湖龙井', 'xi hu long jing', 'hangzhou , zhejiang', 'green tea', '10'], ['2', 'spring snail', '洞庭碧螺春', 'dong ting bi luo chun', 'suzhou , jiangsu', 'green tea', '10'], ['3', 'yellow mountain fur peak', '黄山毛峰', 'huáng shān máo fēng', 'huang shan , anhui', 'green tea', '10'], ['4', 'mount jun silver needle', '君山银针', 'jun shan yin zhen', 'yueyang , hunan', 'yellow tea', '10'], ['5', 'qi men red', '祁门红茶', 'qi men hong cha', 'qimen , anhui', 'black tea', '10'], ['6', 'big red robe', '武夷大紅袍', 'wu yi dà hóng páo', 'wuyi mountains , fujian', 'oolong tea', '10'], ['7', 'melon seed', '六安瓜片', 'liu ān guā piàn', "lu'an , anhui", 'green tea', '10'], ['8', 'iron goddess', '安溪铁观音', 'an xi tiě guān yīn', 'anxi , fujian', 'oolong tea', '10'], ['9', 'houkui tea', '太平猴魁', 'tai ping hou kui', 'huang shan , anhui', 'green tea', '10']]
vesna dolonc
https://en.wikipedia.org/wiki/Vesna_Dolonc
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15639710-7.html.csv
count
vesna dolonc played a total of four tennis tournaments on a clay surface .
{'scope': 'all', 'criterion': 'equal', 'value': 'clay', 'result': '4', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to clay .', 'tostr': 'filter_eq { all_rows ; surface ; clay }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; surface ; clay } }', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; surface ; clay } } ; 4 } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .'}
eq { count { filter_eq { all_rows ; surface ; clay } } ; 4 } = true
select the rows whose surface record fuzzily matches to clay . the number of such rows is 4 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'surface_5': 5, 'clay_6': 6, '4_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'surface_5': 'surface', 'clay_6': 'clay', '4_7': '4'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'surface_5': [0], 'clay_6': [0], '4_7': [2]}
['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score']
[['runner - up', '2 october 2005', 'podgorica , serbia & montenegro', 'clay', 'neda kozić', 'ani mijačika dijana stojics', '6 - 1 3 - 6 4 - 6'], ['runner - up', '11 may 2007', 'monzón , spain', 'hard', 'iryna kuryanovich', 'estrella cabeza - candela maría emilia salerni', '2 - 6 1 - 6'], ['winner', '25 august 2007', 'moscow , russia', 'clay', 'maria kondratieva', 'nina bratchikova sophie lefèvre', '6 - 2 6 - 1'], ['runner - up', '10 november 2007', 'minsk , belarus', 'hard', 'ekaterina ivanova', 'alla kudryavtseva anastasia pavlyuchenkova', '0 - 6 2 - 6'], ['winner', '10 april 2009', 'monzón , spain', 'hard', 'yi chen', 'alberta brianti margalita chakhnashvili', '2 - 6 6 - 4'], ['runner - up', '11 july 2009', 'la coruña , spain', 'hard', 'ksenia milevskaya', 'maría irigoyen florencia molinero', '2 - 6 4 - 6'], ['runner - up', '14 november 2009', 'minsk , belarus', 'hard', 'evgeniya rodina', 'lyudmyla kichenok nadiya kichenok', '3 - 6 6 - 7 ( 7 )'], ['runner - up', '25 september 2010', 'shrewsbury , great britain', 'hard', 'claire feuerstein', 'vitalia diatchenko irena pavlovic', '4 - 6 6 - 4'], ['runner - up', '2 july 2011', 'cuneo , italia', 'clay', 'eva birnerová', 'mandy minella stefanie vögele', '3 - 6 2 - 6'], ['runner - up', '6 february 2012', 'midland , usa', 'hard ( i )', 'stéphanie foretz gacon', 'andrea hlaváčková lucie hradecká', '6 ( 4 ) - 7 2 - 6'], ['winner', '14 may 2012', 'saint - gaudens , france', 'clay', 'irina khromacheva', 'naomi broady julia glushko', '6 - 2 6 - 0']]
89th united states congress
https://en.wikipedia.org/wiki/89th_United_States_Congress
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1847180-3.html.csv
comparative
donald russell got into office before robert p. griffin was able to get into office .
{'row_1': '1', 'row_2': '4', 'col': '5', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'donald s russell ( d )'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose successor record fuzzily matches to donald s russell ( d ) .', 'tostr': 'filter_eq { all_rows ; successor ; donald s russell ( d ) }'}, 'date of successors formal installation'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; successor ; donald s russell ( d ) } ; date of successors formal installation }', 'tointer': 'select the rows whose successor record fuzzily matches to donald s russell ( d ) . take the date of successors formal installation record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'successor', 'robert p griffin ( r )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose successor record fuzzily matches to robert p griffin ( r ) .', 'tostr': 'filter_eq { all_rows ; successor ; robert p griffin ( r ) }'}, 'date of successors formal installation'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; successor ; robert p griffin ( r ) } ; date of successors formal installation }', 'tointer': 'select the rows whose successor record fuzzily matches to robert p griffin ( r ) . take the date of successors formal installation record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; successor ; donald s russell ( d ) } ; date of successors formal installation } ; hop { filter_eq { all_rows ; successor ; robert p griffin ( r ) } ; date of successors formal installation } } = true', 'tointer': 'select the rows whose successor record fuzzily matches to donald s russell ( d ) . take the date of successors formal installation record of this row . select the rows whose successor record fuzzily matches to robert p griffin ( r ) . take the date of successors formal installation record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; successor ; donald s russell ( d ) } ; date of successors formal installation } ; hop { filter_eq { all_rows ; successor ; robert p griffin ( r ) } ; date of successors formal installation } } = true
select the rows whose successor record fuzzily matches to donald s russell ( d ) . take the date of successors formal installation record of this row . select the rows whose successor record fuzzily matches to robert p griffin ( r ) . take the date of successors formal installation 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, 'successor_7': 7, 'donald s russell (d)_8': 8, 'date of successors formal installation_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'successor_11': 11, 'robert p griffin (r)_12': 12, 'date of successors formal installation_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', 'successor_7': 'successor', 'donald s russell (d)_8': 'donald s russell ( d )', 'date of successors formal installation_9': 'date of successors formal installation', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'successor_11': 'successor', 'robert p griffin (r)_12': 'robert p griffin ( r )', 'date of successors formal installation_13': 'date of successors formal installation'}
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'successor_7': [0], 'donald s russell (d)_8': [0], 'date of successors formal installation_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'successor_11': [1], 'robert p griffin (r)_12': [1], 'date of successors formal installation_13': [3]}
['state ( class )', 'vacator', 'reason for change', 'successor', 'date of successors formal installation']
[['south carolina ( 3 )', 'olin d johnston ( d )', 'died april 18 , 1965', 'donald s russell ( d )', 'april 22 , 1965'], ['south carolina ( 3 )', 'donald s russell ( d )', 'successor elected november 8 , 1965', 'ernest hollings ( d )', 'november 9 , 1965'], ['virginia ( 1 )', 'harry f byrd ( d )', 'resigned november 10 , 1965', 'harry f byrd , jr ( d )', 'november 12 , 1965'], ['michigan ( 2 )', 'patrick v mcnamara ( d )', 'died april 30 , 1966', 'robert p griffin ( r )', 'may 11 , 1966'], ['virginia ( 2 )', 'a willis robertson ( d )', 'resigned december 30 , 1966', 'william b spong , jr ( d )', 'december 31 , 1966']]
hadise ( album )
https://en.wikipedia.org/wiki/Hadise_%28album%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16431493-2.html.csv
unique
on hadise the intro is the only track shorter than one minute .
{'scope': 'all', 'row': '1', 'col': '5', 'col_other': '2', 'criterion': 'less_than', 'value': '1:00', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'length', '1:00'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose length record is less than 1:00 .', 'tostr': 'filter_less { all_rows ; length ; 1:00 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; length ; 1:00 } }', 'tointer': 'select the rows whose length record is less than 1:00 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'length', '1:00'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose length record is less than 1:00 .', 'tostr': 'filter_less { all_rows ; length ; 1:00 }'}, 'title'], 'result': 'intro', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; length ; 1:00 } ; title }'}, 'intro'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; length ; 1:00 } ; title } ; intro }', 'tointer': 'the title record of this unqiue row is intro .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; length ; 1:00 } } ; eq { hop { filter_less { all_rows ; length ; 1:00 } ; title } ; intro } } = true', 'tointer': 'select the rows whose length record is less than 1:00 . there is only one such row in the table . the title record of this unqiue row is intro .'}
and { only { filter_less { all_rows ; length ; 1:00 } } ; eq { hop { filter_less { all_rows ; length ; 1:00 } ; title } ; intro } } = true
select the rows whose length record is less than 1:00 . there is only one such row in the table . the title record of this unqiue row is intro .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'length_7': 7, '1:00_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'intro_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'length_7': 'length', '1:00_8': '1:00', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'intro_10': 'intro'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'length_7': [0], '1:00_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'intro_10': [3]}
['track', 'title', 'songwriter ( s )', 'producer ( s )', 'length']
[['1', 'intro', 'hadise açıkgöz', 'yves jongen', '0:52'], ['2', 'deli oğlan', 'sezen aksu', 'hadise açıkgöz , yves jongen', '3:12'], ['3', 'aşkkolik', 'deniz erten', 'özgür buldum', '4:08'], ['4', 'my man and the devil on his shoulder', 'hadise açıkgöz , yves gallard', 'hadise açıkgöz , yves gallard', '4:35'], ['5', 'my body', 'hadise açıkgöz , yves jongen', 'yves jongen', '3:06'], ['6', 'prisoner', 'hadise açıkgöz , stefaan fernande , elio deepcore', 'hadise açıkgöz , stefaan fernande , elio deepcore', '3:52'], ['7', 'a good kiss', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:12'], ['8', 'all together', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:07'], ['9', 'men chase women choose', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:08'], ['10', 'creep', 'hadise açıkgöz , stefaan fernande , stano simor', 'hadise açıkgöz , stefaan fernande , stano simor', '3:44'], ['11', 'good morning baby', 'yves jongen', 'yves jongen', '4:16'], ['12', "do n't ask", 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:00'], ['13', 'intimate', 'hadise açıkgöz , stefaan fernande , luca chiaravall', 'hadise açıkgöz , stefaan fernande , luca chiaravall', '3:37'], ['14', 'busy bee', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:32'], ['15', 'comfort zone', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '4:09'], ['16', 'who am i', 'hadise açıkgöz , yves jongen', 'hadise açıkgöz , yves jongen', '3:14'], ['17', 'a song for my mother', 'hadise açıkgöz , stefaan fernande , luca chiaravall', 'hadise açıkgöz , stefaan fernande , luca chiaravall', '3:32']]
detroit panthers ( pbl )
https://en.wikipedia.org/wiki/Detroit_Panthers_%28PBL%29
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17432028-1.html.csv
count
there were 6 games played by detroit panthers in the month of february .
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'february', 'result': '6', 'col': '1', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'february'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to february .', 'tostr': 'filter_eq { all_rows ; date ; february }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date ; february } }', 'tointer': 'select the rows whose date record fuzzily matches to february . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date ; february } } ; 6 } = true', 'tointer': 'select the rows whose date record fuzzily matches to february . the number of such rows is 6 .'}
eq { count { filter_eq { all_rows ; date ; february } } ; 6 } = true
select the rows whose date record fuzzily matches to february . 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, 'date_5': 5, 'february_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', 'date_5': 'date', 'february_6': 'february', '6_7': '6'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'february_6': [0], '6_7': [2]}
['date', 'opponent', 'home / away', 'score', 'high points', 'high rebounds', 'high assists', 'location / attendance', 'record']
[['january 3', 'mid - michigan destroyers', 'home', '109 - 114', 'brian woodland ( 27 )', 'brian woodland & david myers ( 7 )', 'reginald riley ( 5 )', 'groves high school', '1 - 0'], ['january 4', 'chicago throwbacks', 'home', '110 - 106', 'corey gupton ( 22 )', "stane 's bufford ( 9 )", 'brian woodland & micah kirstein ( 5 )', 'groves high school', '1 - 1'], ['january 10', 'mid - michigan destroyers', 'away', '107 - 102', "stane 's bufford ( 26 )", 'willie mitchell ( 8 )', 'brian woodland ( 7 )', 'bay city western high school', '2 - 1'], ['january 17', 'battle creek knights', 'away', '101 - 109', "stane 's bufford ( 26 )", 'willie mitchell ( 10 )', 'brian woodland ( 4 )', 'kellogg arena', '2 - 2'], ['january 18', 'chicago throwbacks', 'away', '107 - 119', 'james head ( 17 )', 'james head ( 10 )', 'giovanni riley ( 3 )', 'attack athletics', '2 - 3'], ['january 24', 'augusta groove', 'home', '131 - 115', "stane 's bufford ( 27 )", 'willie mitchell ( 10 )', 'giovanni riley ( 9 )', 'groves high school', '2 - 4'], ['january 31', 'battle creek knights', 'home', '119 - 106', 'brian woodland ( 20 )', 'james head ( 9 )', 'brian woodland ( 8 )', 'groves high school', '2 - 5'], ['february 8', 'chicago throwbacks', 'home', '114 - 113', "stane 's bufford ( 28 )", 'chuck bailey ( 8 )', 'randy gill ( 7 )', 'groves high school', '2 - 6'], ['february 13', 'halifax rainmen', 'away', '89 - 100', "stane 's bufford ( 22 )", 'chuck bailey ( 13 )', 'randy gill ( 11 )', 'halifax metro centre', '2 - 7'], ['february 15', 'wilmington sea dawgs', 'away', '95 - 101', "stane 's bufford ( 27 )", 'chuck bailey ( 13 )', 'randy gill ( 3 )', 'schwartz center', '2 - 8'], ['february 20', 'battle creek knights', 'away', '133 - 143', 'randy gill ( 35 )', 'chuck bailey ( 14 )', 'randy gill ( 5 )', 'kellogg arena', '2 - 9'], ['february 22', 'mid - michigan destroyers', 'away', '118 - 103', 'chuck bailey ( 30 )', 'walter waters ( 17 )', 'brian woodland ( 10 )', 'western high school', '3 - 9'], ['february 28', 'battle creek knights', 'home', '108 - 110', 'randy gill ( 35 )', 'chuck bailey ( 15 )', 'randy gill ( 14 )', 'groves high school', '4 - 9'], ['march 6', 'chicago throwbacks', 'away', '123 - 116', 'brian woodland ( 30 )', 'randy gill ( 5 )', 'randy gill ( 3 )', 'attack athletics', '5 - 9'], ['march 8', 'montreal sasquatch', 'home', '115 - 119', "stane 's bufford ( 26 )", 'michael manciel & walter waters ( 13 )', "stane 's bufford ( 6 )", 'groves high school', '6 - 9'], ['march 15', 'augusta groove', 'home', '136 - 132', 'walter waters ( 28 )', 'walter waters ( 15 )', 'brian woodland & randy gill ( 7 )', 'groves high school', '6 - 10'], ['march 21', 'augusta groove', 'away', '119 - 123', 'brian woodland ( 45 )', 'michael manciel ( 12 )', 'oscar sanders ( 5 )', 'richmond academy', '6 - 11'], ['march 22', 'wilmington sea dawgs', 'home', '110 - 91', "stane 's bufford ( 29 )", 'walter waters ( 12 )', 'michael manciel & brian woodland ( 3 )', 'brunswick community college', '6 - 12'], ['march 28', 'wilmington sea dawgs', 'home', '107 - 103', "stane 's bufford ( 31 )", 'walter waters ( 11 )', 'brian woodland ( 6 )', 'groves high school', '6 - 13'], ['march 29', 'wilmington sea dawgs', 'home', '121 - 104', 'brian woodland ( 41 )', 'walter waters ( 16 )', "stane 's bufford ( 9 )", 'groves high school', '6 - 14']]
2010 - 11 new hampshire wildcats men 's basketball team
https://en.wikipedia.org/wiki/2010%E2%80%9311_New_Hampshire_Wildcats_men%27s_basketball_team
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29982187-4.html.csv
aggregation
during this period of the 2010-11 new hampshire wildcats men 's basketball season , the new hampshire wildcats had an average game attendance of about 917 attendees .
{'scope': 'all', 'col': '8', 'type': 'average', 'result': '917', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'location attendance'], 'result': '917', 'ind': 0, 'tostr': 'avg { all_rows ; location attendance }'}, '917'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; location attendance } ; 917 } = true', 'tointer': 'the average of the location attendance record of all rows is 917 .'}
round_eq { avg { all_rows ; location attendance } ; 917 } = true
the average of the location attendance record of all rows is 917 .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'location attendance_4': 4, '917_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'location attendance_4': 'location attendance', '917_5': '917'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'location attendance_4': [0], '917_5': [1]}
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
[['12', 'january 2', 'albany', 'l 59 - 44', 'conley - 15', 'diliegro - 11', 'conley - 3', 'sefcu arena albany , ny ( 1730 )', '5 - 7'], ['13', 'january 4', 'boston university', 'l 61 - 54', 'conley - 14', 'benson - 8', 'conley - 4', 'case gym , boston , ma ( 401 )', '5 - 8'], ['14', 'january 8', 'binghamton', 'l 66 - 61', 'conley - 16', 'benson - 8', 'conley - 3', 'lundholm gym , durham , nh ( 801 )', '5 - 9'], ['15', 'january 11', 'hartford', 'w 57 - 54', 'conley - 33', 'diliegro - 11', 'rhoads - 4', 'lundholm gym , durham , nh ( 498 )', '6 - 9'], ['16', 'january 15', 'stony brook', 'l 64 - 60 2ot', 'conley - 14', 'benson - 22', 'buckley - 3', 'pritchard gymnasium , stony brook , ny ( 1065 )', '6 - 10'], ['17', 'january 20', 'vermont', 'l 61 - 53', 'rhoads - 20', 'conley - 5', 'rhoads - 2', 'lundholm gym , durham , nh ( 538 )', '6 - 11'], ['18', 'january 23', 'umbc', 'w 80 - 60', 'conley - 22', 'benson - 16', 'conley - 7', 'lundholm gym , durham , nh ( 590 )', '7 - 11'], ['19', 'january 25', 'maine', 'l 64 - 50', 'metagrano - 8', 'diliegro - 9', 'rhoads - 3', 'alfond arena , orono , me ( 1268 )', '7 - 12'], ['20', 'january 29', 'boston university', 'w 60 - 48', 'conley - 26', 'benson - 9', 'rhoads - 3', 'lundholm gym , durham , nh ( 1364 )', '8 - 12']]
yugoslavia national football team results
https://en.wikipedia.org/wiki/Yugoslavia_national_football_team_results
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14305653-47.html.csv
count
the yugoslavia national football team played 2 friendly matches in the month of september .
{'scope': 'subset', 'criterion': 'fuzzily_match', 'value': 'september', 'result': '2', 'col': '1', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'september'}}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'september'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; september }', 'tointer': 'select the rows whose date record fuzzily matches to september .'}, 'date', 'september'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to september . among these rows , select the rows whose date record fuzzily matches to september .', 'tostr': 'filter_eq { filter_eq { all_rows ; date ; september } ; date ; september }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; date ; september } ; date ; september } }', 'tointer': 'select the rows whose date record fuzzily matches to september . among these rows , select the rows whose date record fuzzily matches to september . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; date ; september } ; date ; september } } ; 2 } = true', 'tointer': 'select the rows whose date record fuzzily matches to september . among these rows , select the rows whose date record fuzzily matches to september . the number of such rows is 2 .'}
eq { count { filter_eq { filter_eq { all_rows ; date ; september } ; date ; september } } ; 2 } = true
select the rows whose date record fuzzily matches to september . among these rows , select the rows whose date record fuzzily matches to september . 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, 'date_6': 6, 'september_7': 7, 'date_8': 8, 'september_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', 'date_6': 'date', 'september_7': 'september', 'date_8': 'date', 'september_9': 'september', '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], 'date_6': [0], 'september_7': [0], 'date_8': [1], 'september_9': [1], '2_10': [3]}
['date', 'city', 'opponent', 'results', 'type of game']
[['february 26', 'split', 'sweden', '2:1', 'friendly'], ['april 30', 'barcelona , spain', 'spain', '1:2', '1970 wcq'], ['june 4', 'helsinki , finland', 'finland', '5:1', '1970 wcq'], ['september 3', 'belgrade', 'romania', '1:1', 'friendly'], ['september 24', 'belgrade', 'ussr', '1:3', 'friendly'], ['october 19', 'skoplje', 'belgium', '4:0', '1970 wcq']]
2008 - 09 bundesliga
https://en.wikipedia.org/wiki/2008%E2%80%9309_Bundesliga
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17182686-3.html.csv
majority
most of the outgoing managers were sacked during 2008 - 09 bundesliga .
{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'sacked', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'manner of departure', 'sacked'], 'result': True, 'ind': 0, 'tointer': 'for the manner of departure records of all rows , most of them fuzzily match to sacked .', 'tostr': 'most_eq { all_rows ; manner of departure ; sacked } = true'}
most_eq { all_rows ; manner of departure ; sacked } = true
for the manner of departure records of all rows , most of them fuzzily match to sacked .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'manner of departure_3': 3, 'sacked_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'manner of departure_3': 'manner of departure', 'sacked_4': 'sacked'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'manner of departure_3': [0], 'sacked_4': [0]}
['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment', 'position in table']
[['bayer 04 leverkusen', 'michael skibbe', 'sacked', '30 june 2008', 'bruno labbadia', '1 july 2008', 'pre - season'], ['fc bayern munich', 'ottmar hitzfeld', 'end of contract', '30 june 2008', 'jürgen klinsmann', '1 july 2008', 'pre - season'], ['borussia dortmund', 'thomas doll', 'resigned', '30 june 2008', 'jürgen klopp', '1 july 2008', 'pre - season'], ['hamburger sv', 'huub stevens', 'end of contract', '30 june 2008', 'martin jol', '1 july 2008', 'pre - season'], ['fc schalke 04', 'mike büskens & youri mulder', 'stepped down to assistant position', '30 june 2008', 'fred rutten', '1 july 2008', 'pre - season'], ['borussia mönchengladbach', 'jos luhukay', 'sacked', '5 october 2008', 'hans meyer', '18 october 2008', '18th'], ['vfb stuttgart', 'armin veh', 'sacked', '23 november 2008', 'markus babbel', '23 november 2008', '11th'], ['fc schalke 04', 'fred rutten', 'sacked', '26 march 2009', 'mike büskens , youri mulder and oliver reck', '1 april 2009', '8th'], ['fc bayern munich', 'jürgen klinsmann', 'sacked', '27 april 2009', 'jupp heynckes', '27 april 2009', '3rd'], ['arminia bielefeld', 'michael frontzeck', 'sacked', '17 may 2009', 'jörg berger', '19 may 2009', '16th']]
1999 - 2000 toronto raptors season
https://en.wikipedia.org/wiki/1999%E2%80%932000_Toronto_Raptors_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13619135-5.html.csv
count
vince carter scored or tied for the highest number of points nine times .
{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'vince carter', 'result': '9', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high points', 'vince carter'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high points record fuzzily matches to vince carter .', 'tostr': 'filter_eq { all_rows ; high points ; vince carter }'}], 'result': '9', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high points ; vince carter } }', 'tointer': 'select the rows whose high points record fuzzily matches to vince carter . the number of such rows is 9 .'}, '9'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high points ; vince carter } } ; 9 } = true', 'tointer': 'select the rows whose high points record fuzzily matches to vince carter . the number of such rows is 9 .'}
eq { count { filter_eq { all_rows ; high points ; vince carter } } ; 9 } = true
select the rows whose high points record fuzzily matches to vince carter . the number of such rows is 9 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high points_5': 5, 'vince carter_6': 6, '9_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high points_5': 'high points', 'vince carter_6': 'vince carter', '9_7': '9'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high points_5': [0], 'vince carter_6': [0], '9_7': [2]}
['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']
[['30', 'january 4', 'portland', 'l 90 - 114 ( ot )', 'tracy mcgrady ( 18 )', 'kevin willis ( 8 )', 'doug christie , tracy mcgrady ( 4 )', 'air canada centre 19800', '17 - 13'], ['31', 'january 6', 'sacramento', 'w 101 - 89 ( ot )', 'charles oakley ( 20 )', 'charles oakley ( 16 )', 'alvin williams ( 10 )', 'air canada centre 19800', '18 - 13'], ['32', 'january 7', 'atlanta', 'l 97 - 105 ( ot )', 'vince carter ( 34 )', 'vince carter , charles oakley , kevin willis ( 7 )', 'doug christie ( 5 )', 'philips arena 14452', '18 - 14'], ['33', 'january 9', 'vancouver', 'l 97 - 107 ( ot )', 'vince carter , antonio davis ( 20 )', 'vince carter ( 10 )', 'vince carter , charles oakley ( 6 )', 'air canada centre 19188', '18 - 15'], ['34', 'january 11', 'washington', 'l 89 - 117 ( ot )', 'vince carter ( 19 )', 'antonio davis ( 15 )', 'muggsy bogues ( 6 )', 'mci center 13610', '18 - 16'], ['35', 'january 12', 'orlando', 'w 108 - 102 ( ot )', 'vince carter ( 30 )', 'antonio davis ( 9 )', 'vince carter ( 9 )', 'air canada centre 17241', '19 - 16'], ['36', 'january 14', 'milwaukee', 'w 115 - 110 ( ot )', 'vince carter ( 47 )', 'charles oakley ( 12 )', 'doug christie ( 8 )', 'air canada centre 19246', '20 - 16'], ['37', 'january 15', 'milwaukee', 'l 97 - 118 ( ot )', 'doug christie ( 31 )', 'kevin willis ( 12 )', 'muggsy bogues , vince carter ( 5 )', 'bradley center 18717', '20 - 17'], ['38', 'january 17', 'charlotte', 'l 94 - 115 ( ot )', 'vince carter ( 24 )', 'michael stewart ( 8 )', 'vince carter ( 6 )', 'charlotte coliseum 20278', '20 - 18'], ['39', 'january 19', 'boston', 'l 90 - 94 ( ot )', 'vince carter ( 20 )', 'charles oakley ( 10 )', 'muggsy bogues , alvin williams ( 5 )', 'fleetcenter 16124', '20 - 19'], ['40', 'january 23', 'seattle', 'w 94 - 77 ( ot )', 'antonio davis , tracy mcgrady ( 17 )', 'kevin willis ( 12 )', 'doug christie ( 6 )', 'air canada centre 19800', '21 - 19'], ['41', 'january 26', 'washington', 'w 120 - 105 ( ot )', 'vince carter ( 26 )', 'kevin willis ( 9 )', 'charles oakley ( 8 )', 'air canada centre 17582', '22 - 19'], ['42', 'january 28', 'miami', 'w 108 - 93 ( ot )', 'vince carter ( 23 )', 'antonio davis ( 12 )', 'charles oakley ( 7 )', 'air canada centre 19800', '23 - 19']]
list of stargate audiobooks
https://en.wikipedia.org/wiki/List_of_Stargate_audiobooks
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16279520-1.html.csv
count
sally malcolm was the writer for 4 of the titles .
{'scope': 'all', 'criterion': 'equal', 'value': 'sally malcolm', 'result': '4', 'col': '5', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'writer', 'sally malcolm'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose writer record fuzzily matches to sally malcolm .', 'tostr': 'filter_eq { all_rows ; writer ; sally malcolm }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; writer ; sally malcolm } }', 'tointer': 'select the rows whose writer record fuzzily matches to sally malcolm . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; writer ; sally malcolm } } ; 4 } = true', 'tointer': 'select the rows whose writer record fuzzily matches to sally malcolm . the number of such rows is 4 .'}
eq { count { filter_eq { all_rows ; writer ; sally malcolm } } ; 4 } = true
select the rows whose writer record fuzzily matches to sally malcolm . the number of such rows is 4 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'writer_5': 5, 'sally malcolm_6': 6, '4_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'writer_5': 'writer', 'sally malcolm_6': 'sally malcolm', '4_7': '4'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'writer_5': [0], 'sally malcolm_6': [0], '4_7': [2]}
['title', 'series', 'release', 'featuring', 'writer', 'director', 'length', 'timeline', 'release date']
[['gift of the gods', 'stargate sg - 1', '1.1', 'michael shanks john schwab', 'sally malcolm', 'sharon gosling', "70 '", 'season 3 before fair game', 'april 1 , 2008'], ['a necessary evil', 'stargate atlantis', '1.2', 'torri higginson timothy watson', 'sharon gosling', 'sharon gosling', "70 '", 'season 3 , bookended by season 4', 'may 2008'], ['shell game', 'stargate sg - 1', '1.3', 'claudia black michael shanks', 'james swallow', 'sharon gosling', "70 '", 'season 10 after the pegasus project', 'june 1 , 2008'], ['perchance to dream', 'stargate atlantis', '1.4', 'paul mcgillion sara douglas', 'sally malcolm', 'sharon gosling', "70 '", 'season 2', 'july 2008'], ['savarna', 'stargate sg - 1', '1.5', 'teryl rothery toby longworth', 'sally malcolm', 'sharon gosling', "70 '", 'season 7 between grace and heroes', 'august 2008'], ['zero point', 'stargate atlantis', '1.6', 'david nykl ursula burton', 'james swallow', 'sharon gosling', "70 '", 'early season 4', 'september 2008'], ['first prime', 'stargate sg - 1', '2.1', 'christopher judge noel clarke', 'james swallow', 'sharon gosling', "70 '", 'season 4', 'may 30 , 2009'], ['impressions', 'stargate atlantis', '2.2', 'kavan smith nicholas briggs', 'scott andrews', 'sharon gosling', "60 '", 'season 4 between lifeline and doppelganger', 'june 30 , 2009'], ['pathogen', 'stargate sg - 1', '2.3', 'teryl rothery christopher judge', 'sharon gosling', 'sharon gosling', "60 '", 'season 7 between fragile balance and heroes', 'july 31 , 2009'], ['the kindness of strangers', 'stargate atlantis', '2.4', 'paul mcgillion neil roberts', 'sharon gosling', 'sharon gosling', "60 '", 'season 2 / 3 before sunday', 'august 31 , 2009'], ['meltdown', 'stargate atlantis', '2.6', 'david nykl aiden j david', 'david a mcintee', 'sharon gosling', "60 '", "season 2 , shortly before coup d'etat", 'october 30 , 2009'], ['half - life', 'stargate sg - 1', '3.1', 'michael shanks claudia black cliff simon', 'james swallow', 'lisa bowerman & jason haigh - ellery', "60 '", "season 9 , between stronghold and arthur 's mantle", 'may 2012'], ['an eye for an eye', 'stargate sg - 1', '3.2', 'michael shanks claudia black cliff simon', 'sally malcolm', 'lisa bowerman & jason haigh - ellery', "60 '", "season 9 , between stronghold and arthur 's mantle", 'may 2012'], ['infiltration', 'stargate sg - 1', '3.3', 'michael shanks claudia black cliff simon', 'steve lyons', 'lisa bowerman & jason haigh - ellery', "60 '", "season 9 , between stronghold and arthur 's mantle", 'may 2012'], ['excision', 'stargate sg - 1', '3.4', 'michael shanks claudia black', 'peter j evans', 'lisa bowerman & jason haigh - ellery', "60 '", 'after stargate season 10', 'december 2012'], ['duplicity', 'stargate sg - 1', '3.5', 'michael shanks claudia black', 'richard dinnick', 'lisa bowerman & jason haigh - ellery', "60 '", 'after stargate season 10', 'december 2012']]
eurobasket 1967
https://en.wikipedia.org/wiki/EuroBasket_1967
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13843829-3.html.csv
majority
everyone in all the positions at eurobasket 1967 participated in seven matches .
{'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': '7', 'subset': None}
{'func': 'all_eq', 'args': ['all_rows', 'matches', '7'], 'result': True, 'ind': 0, 'tointer': 'for the matches records of all rows , all of them are equal to 7 .', 'tostr': 'all_eq { all_rows ; matches ; 7 } = true'}
all_eq { all_rows ; matches ; 7 } = true
for the matches records of all rows , all of them are equal to 7 .
1
1
{'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'matches_3': 3, '7_4': 4}
{'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'matches_3': 'matches', '7_4': '7'}
{'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'matches_3': [0], '7_4': [0]}
['pos', 'matches', 'wins', 'loses', 'results', 'points', 'diff']
[['1', '7', '6', '1', '550:461', '12', '+ 89'], ['2', '7', '6', '1', '554:485', '12', '+ 69'], ['3', '7', '5', '2', '479:449', '10', '+ 30'], ['4', '7', '4', '3', '493:497', '8', '4'], ['5', '7', '4', '3', '523:507', '8', '+ 16'], ['6', '7', '2', '5', '526:579', '4', '53'], ['7', '7', '1', '6', '500:581', '2', '81'], ['8', '7', '0', '7', '454:570', '0', '116']]
campos racing
https://en.wikipedia.org/wiki/Campos_Racing
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10793848-1.html.csv
unique
ben hanley was the only campos racing driver who competed in less than 10 races between 2005 and 2008 .
{'scope': 'all', 'row': '8', 'col': '4', 'col_other': '3', 'criterion': 'less_than', 'value': '10', 'subset': None}
{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'races', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose races record is less than 10 .', 'tostr': 'filter_less { all_rows ; races ; 10 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; races ; 10 } }', 'tointer': 'select the rows whose races record is less than 10 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'races', '10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose races record is less than 10 .', 'tostr': 'filter_less { all_rows ; races ; 10 }'}, 'drivers'], 'result': 'ben hanley', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; races ; 10 } ; drivers }'}, 'ben hanley'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; races ; 10 } ; drivers } ; ben hanley }', 'tointer': 'the drivers record of this unqiue row is ben hanley .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; races ; 10 } } ; eq { hop { filter_less { all_rows ; races ; 10 } ; drivers } ; ben hanley } } = true', 'tointer': 'select the rows whose races record is less than 10 . there is only one such row in the table . the drivers record of this unqiue row is ben hanley .'}
and { only { filter_less { all_rows ; races ; 10 } } ; eq { hop { filter_less { all_rows ; races ; 10 } ; drivers } ; ben hanley } } = true
select the rows whose races record is less than 10 . there is only one such row in the table . the drivers record of this unqiue row is ben hanley .
6
5
{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'races_7': 7, '10_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'drivers_9': 9, 'ben hanley_10': 10}
{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'races_7': 'races', '10_8': '10', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'drivers_9': 'drivers', 'ben hanley_10': 'ben hanley'}
{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'races_7': [0], '10_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'drivers_9': [2], 'ben hanley_10': [3]}
['year', 'team name', 'drivers', 'races', 'wins', 'poles', 'flaps', 'points', 'dc', 'tc']
[['2005', 'campos racing', 'juan cruz álvarez', '23', '0', '0', '0', '4.5', '18th', '12th'], ['2005', 'campos racing', 'sergio hernández', '23', '0', '0', '0', '3', '20th', '12th'], ['2006', 'campos racing', 'adrián vallés', '21', '0', '0', '1', '7', '18th', '12th'], ['2006', 'campos racing', 'félix porteiro', '21', '0', '0', '0', '5', '22nd', '12th'], ['2007', 'campos grand prix', 'vitaly petrov', '21', '1', '0', '0', '21', '13th', '3rd'], ['2007', 'campos grand prix', 'giorgio pantano', '21', '2', '1', '1', '59', '3rd', '3rd'], ['2008', 'barwa international campos team', 'vitaly petrov', '20', '1', '0', '1', '39', '7th', '1st'], ['2008', 'barwa international campos team', 'ben hanley', '6', '0', '0', '0', '1', '24th', '1st'], ['2008', 'barwa international campos team', 'lucas di grassi', '14', '3', '0', '2', '63', '3rd', '1st']]
list of communities in saskatchewan
https://en.wikipedia.org/wiki/List_of_communities_in_Saskatchewan
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-189598-7.html.csv
ordinal
la loche has the highest change ( % ) among those with population ( 2011 ) more than 1000 in the list of communities in saskatchewan .
{'scope': 'subset', 'row': '8', 'col': '4', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': '1000'}}
{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'population ( 2011 )', '1000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; population ( 2011 ) ; 1000 }', 'tointer': 'select the rows whose population ( 2011 ) record is greater than 1000 .'}, 'change ( % )', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmax { filter_greater { all_rows ; population ( 2011 ) ; 1000 } ; change ( % ) ; 1 }'}, 'name'], 'result': 'la loche', 'ind': 2, 'tostr': 'hop { nth_argmax { filter_greater { all_rows ; population ( 2011 ) ; 1000 } ; change ( % ) ; 1 } ; name }'}, 'la loche'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmax { filter_greater { all_rows ; population ( 2011 ) ; 1000 } ; change ( % ) ; 1 } ; name } ; la loche } = true', 'tointer': 'select the rows whose population ( 2011 ) record is greater than 1000 . select the row whose change ( % ) record of these rows is 1st maximum . the name record of this row is la loche .'}
eq { hop { nth_argmax { filter_greater { all_rows ; population ( 2011 ) ; 1000 } ; change ( % ) ; 1 } ; name } ; la loche } = true
select the rows whose population ( 2011 ) record is greater than 1000 . select the row whose change ( % ) record of these rows is 1st maximum . the name record of this row is la loche .
4
4
{'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmax_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'population (2011)_6': 6, '1000_7': 7, 'change (%)_8': 8, '1_9': 9, 'name_10': 10, 'la loche_11': 11}
{'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmax_1': 'nth_argmax', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'population (2011)_6': 'population ( 2011 )', '1000_7': '1000', 'change (%)_8': 'change ( % )', '1_9': '1', 'name_10': 'name', 'la loche_11': 'la loche'}
{'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmax_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'population (2011)_6': [0], '1000_7': [0], 'change (%)_8': [1], '1_9': [1], 'name_10': [2], 'la loche_11': [3]}
['name', 'population ( 2011 )', 'population ( 2006 )', 'change ( % )', 'land area ( km square )', 'population density ( per km square )']
[['air ronge', '1043', '1032', '1.1', '6.00', '173.8'], ['beauval', '756', '806', '- 6.2', '6.71', '112.6'], ['buffalo narrows', '1153', '1081', '6.7', '68.63', '16.8'], ['cumberland house', '772', '810', '- 4.7', '15.69', '49.2'], ['denare beach', '820', '785', '4.5', '5.84', '140.4'], ['green lake', '418', '361', '15.8', '121.92', '3.4'], ['île - à - la - crosse', '1365', '1341', '1.8', '23.84', '57.3'], ['la loche', '2611', '2348', '11.2', '15.59', '167.5'], ['pelican narrows', '790', '599', '31.9', '3.70', '213.3'], ['pinehouse', '978', '1076', '- 9.1', '6.84', '142.9'], ['sandy bay', '1233', '1175', '4.9', '14.85', '83.0']]
list of csi : miami characters
https://en.wikipedia.org/wiki/List_of_CSI%3A_Miami_characters
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11210576-1.html.csv
count
golden parachute is the episode that 6 of the characters first appeared in .
{'scope': 'all', 'criterion': 'equal', 'value': 'golden parachute', 'result': '6', 'col': '4', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'first episode', 'golden parachute'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first episode record fuzzily matches to golden parachute .', 'tostr': 'filter_eq { all_rows ; first episode ; golden parachute }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; first episode ; golden parachute } }', 'tointer': 'select the rows whose first episode record fuzzily matches to golden parachute . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; first episode ; golden parachute } } ; 6 } = true', 'tointer': 'select the rows whose first episode record fuzzily matches to golden parachute . the number of such rows is 6 .'}
eq { count { filter_eq { all_rows ; first episode ; golden parachute } } ; 6 } = true
select the rows whose first episode record fuzzily matches to golden parachute . 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, 'first episode_5': 5, 'golden parachute_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', 'first episode_5': 'first episode', 'golden parachute_6': 'golden parachute', '6_7': '6'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'first episode_5': [0], 'golden parachute_6': [0], '6_7': [2]}
['character', 'position', 'actor', 'first episode', 'final episode', 'episodes credited']
[['horatio caine', 'csi lieutenant', 'david caruso', 'golden parachute', 'habeas corpse', '232'], ['calleigh duquesne', 'csi detective', 'emily procter', 'golden parachute', 'habeas corpse', '232'], ['eric delko', 'csi detective', 'adam rodriguez', 'golden parachute', 'habeas corpse', '221'], ['frank tripp', 'detective sgt', 'rex linn', 'evidence of things unseen', 'habeas corpse', '187'], ['ryan wolfe', 'csi detective', 'jonathan togo', 'under the influence', 'habeas corpse', '182'], ['natalia boa vista', 'csi detective', 'eva larue', 'from the grave', 'habeas corpse', '153'], ['alexx woods', 'medical examiner', 'khandi alexander', 'golden parachute', 'bad seed', '145'], ['walter simmons', 'csi detective', 'omar miller', 'bolt action', 'habeas corpse', '063'], ['yelina salas', 'detective', 'sofia milos', 'simple man', 'seeing red', '060'], ['tim speedle', 'csi detective', 'rory cochrane', 'golden parachute', 'bang , bang , your debt', '050'], ['jesse cardoza', 'csi detective', 'eddie cibrian', 'out of time', 'fallen', '025'], ['tara price', 'medical examiner', 'megalyn echikunwoke', "wo n't get fueled again", 'dissolved', '023'], ['megan donner', 'csi lieutenant', 'kim delaney', 'golden parachute', 'a horrible mind', '010']]
henri leconte
https://en.wikipedia.org/wiki/Henri_Leconte
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1171445-6.html.csv
majority
the majority of tournament outcomes for henri leconte were wins .
{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'winner', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'outcome', 'winner'], 'result': True, 'ind': 0, 'tointer': 'for the outcome records of all rows , most of them fuzzily match to winner .', 'tostr': 'most_eq { all_rows ; outcome ; winner } = true'}
most_eq { all_rows ; outcome ; winner } = true
for the outcome records of all rows , most of them fuzzily match to winner .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'outcome_3': 3, 'winner_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'outcome_3': 'outcome', 'winner_4': 'winner'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'outcome_3': [0], 'winner_4': [0]}
['outcome', 'date', 'tournament', 'surface', 'partner', 'opponents in the final', 'score in the final']
[['winner', '1981', 'bologna , italy', 'carpet', 'sammy giammalva jr', 'tomáš šmíd balázs taróczy', '7 - 6 , 6 - 4'], ['winner', '1982', 'nice , france', 'clay', 'yannick noah', 'paul mcnamee balázs taróczy', '5 - 7 , 6 - 4 , 6 - 3'], ['runner - up', '1982', 'bournemouth , england', 'clay', 'ilie năstase', 'paul mcnamee buster mottram', '6 - 3 , 6 - 7 , 3 - 6'], ['winner', '1982', 'basel , switzerland', 'hard ( i )', 'yannick noah', 'fritz buehning pavel složil', '6 - 2 , 6 - 2'], ['winner', '1982', 'vienna , austria', 'carpet', 'pavel složil', 'mark dickson terry moor', '6 - 1 , 7 - 6'], ['runner - up', '1983', 'monte carlo , monaco', 'clay', 'yannick noah', 'heinz günthardt balázs taróczy', '2 - 6 , 4 - 6'], ['winner', '1983', 'aix - en - provence , france', 'clay', 'gilles moretton', 'ivan camus sergio casal', '2 - 6 , 6 - 1 , 6 - 2'], ['runner - up', '1984', 'philadelphia , us', 'carpet', 'yannick noah', 'peter fleming john mcenroe', '2 - 6 , 3 - 6'], ['winner', '1984', 'french open , paris', 'clay', 'yannick noah', 'pavel složil tomáš šmíd', '6 - 4 , 2 - 6 , 3 - 6 , 6 - 3 , 6 - 2'], ['winner', '1984', 'kitzbühel , austria', 'clay', 'pascal portes', 'colin dowdeswell wojtek fibak', '2 - 6 , 7 - 6 , 7 - 6'], ['winner', '1984', 'stockholm , sweden', 'hard ( i )', 'tomáš šmíd', 'vijay amritraj ilie năstase', '3 - 6 , 7 - 6 , 6 - 4'], ['runner - up', '1985', 'us open , new york', 'hard', 'yannick noah', 'ken flach robert seguso', '7 - 6 , 6 - 7 , 6 - 7 , 0 - 6'], ['winner', '1988', 'nice , france', 'clay', 'guy forget', 'heinz günthardt diego nargiso', '4 - 6 , 6 - 3 , 6 - 4'], ['runner - up', '1988', 'monte carlo , monaco', 'clay', 'ivan lendl', 'sergio casal emilio sánchez', '0 - 6 , 3 - 6'], ['runner - up', '1990', "london / queen 's club , england", 'grass', 'ivan lendl', 'jeremy bates kevin curren', '2 - 6 , 6 - 7'], ['runner - up', '1991', 'indian wells , us', 'hard', 'guy forget', 'jim courier javier sánchez', '6 - 7 , 6 - 3 , 3 - 6'], ['runner - up', '1992', 'toulouse , france', 'hard ( i )', 'guy forget', 'brad pearce byron talbot', '1 - 6 , 6 - 3 , 3 - 6'], ['winner', '1993', 'indian wells , us', 'hard', 'guy forget', 'luke jensen scott melville', '6 - 4 , 7 - 5'], ['runner - up', '1994', 'halle , germany', 'grass', 'gary muller', 'olivier delaître guy forget', '4 - 6 , 7 - 6 , 4 - 6']]
vehicles & animals
https://en.wikipedia.org/wiki/Vehicles_%26_Animals
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1546629-3.html.csv
comparative
the version of the album vehicles & animals from the label astralwerks was released earlier than the version from the label capitol records .
{'row_1': '4', 'row_2': '5', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}
{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'label', 'astralwerks'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose label record fuzzily matches to astralwerks .', 'tostr': 'filter_eq { all_rows ; label ; astralwerks }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; label ; astralwerks } ; date }', 'tointer': 'select the rows whose label record fuzzily matches to astralwerks . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'label', 'capitol records'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose label record fuzzily matches to capitol records .', 'tostr': 'filter_eq { all_rows ; label ; capitol records }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; label ; capitol records } ; date }', 'tointer': 'select the rows whose label record fuzzily matches to capitol records . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; label ; astralwerks } ; date } ; hop { filter_eq { all_rows ; label ; capitol records } ; date } } = true', 'tointer': 'select the rows whose label record fuzzily matches to astralwerks . take the date record of this row . select the rows whose label record fuzzily matches to capitol records . take the date record of this row . the first record is less than the second record .'}
less { hop { filter_eq { all_rows ; label ; astralwerks } ; date } ; hop { filter_eq { all_rows ; label ; capitol records } ; date } } = true
select the rows whose label record fuzzily matches to astralwerks . take the date record of this row . select the rows whose label record fuzzily matches to capitol records . take the date record of this row . the first record is less than the second record .
5
5
{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'label_7': 7, 'astralwerks_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'label_11': 11, 'capitol records_12': 12, 'date_13': 13}
{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'label_7': 'label', 'astralwerks_8': 'astralwerks', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'label_11': 'label', 'capitol records_12': 'capitol records', 'date_13': 'date'}
{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'label_7': [0], 'astralwerks_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'label_11': [1], 'capitol records_12': [1], 'date_13': [3]}
['country', 'date', 'label', 'format', 'catalog']
[['united kingdom', '7 april 2003', 'parlophone', 'lp', '582 2911'], ['united kingdom', '7 april 2003', 'parlophone', 'cd', '582 2912'], ['united kingdom', '7 april 2003', 'parlophone', 'cd digipak', '584 2112'], ['united states', '18 may 2004', 'astralwerks', 'cd', 'asw 82291'], ['australia', '14 march 2005', 'capitol records', 'cd', '582 3412']]
desperate romantics
https://en.wikipedia.org/wiki/Desperate_Romantics
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23705843-1.html.csv
majority
the majority of these episodes of desperate romantics were directed by paul gay .
{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'paul gay', 'subset': None}
{'func': 'most_str_eq', 'args': ['all_rows', 'director', 'paul gay'], 'result': True, 'ind': 0, 'tointer': 'for the director records of all rows , most of them fuzzily match to paul gay .', 'tostr': 'most_eq { all_rows ; director ; paul gay } = true'}
most_eq { all_rows ; director ; paul gay } = true
for the director records of all rows , most of them fuzzily match to paul gay .
1
1
{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'director_3': 3, 'paul gay_4': 4}
{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'director_3': 'director', 'paul gay_4': 'paul gay'}
{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'director_3': [0], 'paul gay_4': [0]}
['episode', 'director', 'writer', 'original air date', 'ratings ( millions )']
[['episode 1', 'paul gay', 'peter bowker', '21 july 2009', '2.61'], ['episode 2', 'paul gay', 'peter bowker', '28 july 2009', '2.13'], ['episode 3', 'paul gay', 'peter bowker', '4 august 2009', '2.15'], ['episode 4', 'diarmuid lawrence', 'peter bowker', '11 august 2009', '1.92'], ['episode 5', 'diarmuid lawrence', 'peter bowker', '18 august 2009', '1.96']]
usage share of operating systems
https://en.wikipedia.org/wiki/Usage_share_of_operating_systems
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11381701-3.html.csv
aggregation
considering the information from all sources listed in the usage share of operating systems , the average share of blackberry use is around 3.19 % .
{'scope': 'all', 'col': '6', 'type': 'average', 'result': '3.19 %', 'subset': None}
{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'blackberry'], 'result': '3.19 %', 'ind': 0, 'tostr': 'avg { all_rows ; blackberry }'}, '3.19 %'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; blackberry } ; 3.19 % } = true', 'tointer': 'the average of the blackberry record of all rows is 3.19 % .'}
round_eq { avg { all_rows ; blackberry } ; 3.19 % } = true
the average of the blackberry record of all rows is 3.19 % .
2
2
{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'blackberry_4': 4, '3.19%_5': 5}
{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'blackberry_4': 'blackberry', '3.19%_5': '3.19 %'}
{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'blackberry_4': [0], '3.19%_5': [1]}
['source', 'date', 'method', 'ios', 'android', 'blackberry', 'symbian / series 40', 'bada', 'windows', 'other']
[['comscore reports ( us only )', 'may - 13', 'subscribers , us', '39.20 %', '52.40 %', '4.80 %', '0.40 %', 'n / a', '3.00 %', 'n / a'], ['gartner', 'may - 13', 'units sold', '18.2 %', '74.4 %', '3.0 %', '0.6 %', '0.7 %', '2.9 %', '0.3 %'], ['gartner', 'aug - 13', 'units sold', '14.2 %', '79.0 %', '2.7 %', '0.3 %', '0.4 %', '3.3 %', '0.2 %'], ['international data corporation', 'may - 13', 'units shipped', '17.3 %', '75.0 %', '2.9 %', '0.6', 'n / a', '3.2 %', '0.0 %'], ['net market share', 'july - 13', 'browsing', '58.26 %', '25.28 %', '3.23 %', '2.23 %', '0.05 %', '1.15 %', '0.19 %'], ['statcounter global stats', 'july - 13', 'browsing', '24.80 %', '38.34 %', '3.66 %', '20.76 %', '4.64 %', '1.52', '2.66'], ['wikimedia', 'mar - 13', 'browsing', '66.53 %', '25.93 %', '2.02 %', '3.03 %', '0.42 %', '1.85 %', '0.7 %']]
2000 san diego chargers season
https://en.wikipedia.org/wiki/2000_San_Diego_Chargers_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15331726-1.html.csv
count
in the 2000 san diego chargers season the attendance at four games was over 70,000 .
{'scope': 'all', 'criterion': 'greater_than', 'value': '70000', 'result': '4', 'col': '7', 'subset': None}
{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'attendance', '70000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose attendance record is greater than 70000 .', 'tostr': 'filter_greater { all_rows ; attendance ; 70000 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; attendance ; 70000 } }', 'tointer': 'select the rows whose attendance record is greater than 70000 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; attendance ; 70000 } } ; 4 } = true', 'tointer': 'select the rows whose attendance record is greater than 70000 . the number of such rows is 4 .'}
eq { count { filter_greater { all_rows ; attendance ; 70000 } } ; 4 } = true
select the rows whose attendance record is greater than 70000 . the number of such rows is 4 .
3
3
{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '70000_6': 6, '4_7': 7}
{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', '70000_6': '70000', '4_7': '4'}
{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '70000_6': [0], '4_7': [2]}
['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance']
[['1', 'september 3 , 2000', 'oakland raiders', 'l 6 - 9', 'network associates coliseum', '0 - 1', '56373'], ['2', 'september 10 , 2000', 'new orleans saints', 'l 27 - 28', 'qualcomm stadium', '0 - 2', '51300'], ['3', 'september 17 , 2000', 'kansas city chiefs', 'l 10 - 42', 'arrowhead stadium', '0 - 3', '77604'], ['4', 'september 24 , 2000', 'seattle seahawks', 'l 12 - 20', 'qualcomm stadium', '0 - 4', '47233'], ['5', 'october 1 , 2000', 'st louis rams', 'l 31 - 57', 'trans world dome', '0 - 5', '66010'], ['6', 'october 8 , 2000', 'denver broncos', 'l 7 - 21', 'qualcomm stadium', '0 - 6', '56079'], ['7', 'october 15 , 2000', 'buffalo bills', 'l 24 - 27', 'ralph wilson stadium', '0 - 7', '72351'], ['8', 'october 22 , 2000', '-', '-', '-', '-', ''], ['9', 'october 29 , 2000', 'oakland raiders', 'l 13 - 15', 'qualcomm stadium', '0 - 8', '66659'], ['10', 'november 5 , 2000', 'seattle seahawks', 'l 15 - 17', 'husky stadium', '0 - 9', '59884'], ['11', 'november 12 , 2000', 'miami dolphins', 'l 7 - 17', 'qualcomm stadium', '0 - 10', '56896'], ['12', 'november 19 , 2000', 'denver broncos', 'l 37 - 38', 'mile high stadium', '0 - 11', '75218'], ['13', 'november 26 , 2000', 'kansas city chiefs', 'w 17 - 16', 'qualcomm stadium', '1 - 11', '47228'], ['14', 'december 3 , 2000', 'san francisco 49ers', 'l 17 - 45', 'qualcomm stadium', '1 - 12', '57255'], ['15', 'december 10 , 2000', 'baltimore ravens', 'l 3 - 24', 'psinet stadium', '1 - 13', '68805'], ['16', 'december 17 , 2000', 'carolina panthers', 'l 22 - 30', 'ericsson stadium', '1 - 14', '72159'], ['17', 'december 24 , 2000', 'pittsburgh steelers', 'l 21 - 34', 'qualcomm stadium', '1 - 15', '50809']]
1962 denver broncos season
https://en.wikipedia.org/wiki/1962_Denver_Broncos_season
https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17778417-1.html.csv
superlative
the october 14 , 1962 game was the only one where attendance was lower than 10000 fans .
{'scope': 'all', 'col_superlative': '7', 'row_superlative': '6', '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', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; attendance }'}, 'date'], 'result': 'october 14 , 1962', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; attendance } ; date }'}, 'october 14 , 1962'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; attendance } ; date } ; october 14 , 1962 } = true', 'tointer': 'select the row whose attendance record of all rows is minimum . the date record of this row is october 14 , 1962 .'}
eq { hop { argmin { all_rows ; attendance } ; date } ; october 14 , 1962 } = true
select the row whose attendance record of all rows is minimum . the date record of this row is october 14 , 1962 .
3
3
{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, 'october 14 , 1962_7': 7}
{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'date_6': 'date', 'october 14 , 1962_7': 'october 14 , 1962'}
{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], 'october 14 , 1962_7': [2]}
['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance']
[['1', 'september 7 , 1962', 'san diego chargers', 'w 30 - 21', 'university of denver stadium', '1 - 0', '28000'], ['2', 'september 15 , 1962', 'buffalo bills', 'w 23 - 20', 'war memorial stadium', '2 - 0', '30577'], ['3', 'september 21 , 1962', 'boston patriots', 'l 16 - 41', 'boston university field', '2 - 1', '21038'], ['4', 'september 30 , 1962', 'new york titans', 'w 32 - 10', 'polo grounds', '3 - 1', '17213'], ['5', 'october 5 , 1962', 'oakland raiders', 'w 44 - 7', 'bears stadium', '4 - 1', '22452'], ['6', 'october 14 , 1962', 'oakland raiders', 'w 23 - 6', 'frank youell field', '5 - 1', '7000'], ['7', 'october 21 , 1962', 'houston oilers', 'w 20 - 10', 'bears stadium', '6 - 1', '34496'], ['8', 'october 28 , 1962', 'buffalo bills', 'l 35 - 48', 'bears stadium', '6 - 2', '26051'], ['9', 'november 4 , 1962', 'san diego chargers', 'w 23 - 20', 'balboa stadium', '7 - 2', '20827'], ['10', 'november 11 , 1962', 'boston patriots', 'l 29 - 33', 'bears stadium', '7 - 3', '28187'], ['11', 'november 18 , 1962', 'dallas texans', 'l 29 - 33', 'bears stadium', '7 - 4', '23523'], ['12', 'november 22 , 1962', 'new york titans', 'l 45 - 46', 'bears stadium', '7 - 5', '15776'], ['13', 'december 2 , 1962', 'houston oilers', 'l 17 - 34', 'jeppesen stadium', '7 - 6', '30650'], ['14', 'december 9 , 1962', 'dallas texans', 'l 10 - 17', 'cotton bowl', '7 - 7', '19137']]