niryuu/sni-each-converted huggingface dataset:
select the rows whose november record fuzzily matches to 27 . take the points record of this row . select the rows whose november record fuzzily matches to 29 . take the points record of this row . the second record is 2 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; november ; 27 } ; points } ; hop { filter_eq { all_rows ; november ; 29 } ; points } } ; -2 }
select the rows whose nation record fuzzily matches to lebanon . take the total record of this row . select the rows whose nation record fuzzily matches to french community of belgium . take the total record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; lebanon } ; total } ; hop { filter_eq { all_rows ; nation ; french community of belgium } ; total } }
select the rows whose team 1 record fuzzily matches to as saint - étienne ( d1 ) . take the score record of this row . select the rows whose team 1 record fuzzily matches to girondins de bordeaux ( d1 ) . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team 1 ; as saint - étienne ( d1 ) } ; score } ; hop { filter_eq { all_rows ; team 1 ; girondins de bordeaux ( d1 ) } ; score } }
select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high assists record fuzzily matches to g payton . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high assists ; g payton } } ; 3 }
select the rows whose country record does not match to united states . there is only one such row in the table . the player record of this unqiue row is bob charles .
Output: and { only { filter_not_eq { all_rows ; country ; united states } } ; eq { hop { filter_not_eq { all_rows ; country ; united states } ; player } ; bob charles } }
select the row whose crowd record of all rows is maximum . the home team record of this row is collingwood . the away team record of this row is north melbourne .
Output: and { eq { hop { argmax { all_rows ; crowd } ; home team } ; collingwood } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; north melbourne } }
select the row whose est record of all rows is minimum . the name record of this row is yamagata international documentary film festival .
Output: eq { hop { argmin { all_rows ; est } ; name } ; yamagata international documentary film festival }
select the row whose crowd record of all rows is 4th maximum . the home team record of this row is essendon .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 4 } ; home team } ; essendon }
select the rows whose date record fuzzily matches to 2007 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; date ; 2007 } }
the average of the population ( 2005 ) record of all rows is 801.29 .
Output: round_eq { avg { all_rows ; population ( 2005 ) } ; 801.29 }
the sum of the against record of all rows is 53 .
Output: round_eq { sum { all_rows ; against } ; 53 }
the sum of the points record of all rows is 626 .
Output: round_eq { sum { all_rows ; points } ; 626 }
select the rows whose stage ( winner ) record fuzzily matches to mark cavendish . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; stage ( winner ) ; mark cavendish } } ; 3 }
the average of the times contested record of all rows is 2.17 .
Output: round_eq { avg { all_rows ; times contested } ; 2.17 }
select the rows whose venue record fuzzily matches to johannesburg . for the runs records of these rows , most of them are greater than 140 .
Output: most_greater { filter_eq { all_rows ; venue ; johannesburg } ; runs ; 140 }
select the rows whose date record fuzzily matches to 6 march 1976 . select the row whose tie no record of these rows is minimum . the home team record of this row is sunderland . the away team record of this row is crystal palace .
Output: and { eq { hop { argmin { filter_eq { all_rows ; date ; 6 march 1976 } ; tie no } ; home team } ; sunderland } ; eq { hop { argmin { filter_eq { all_rows ; date ; 6 march 1976 } ; tie no } ; away team } ; crystal palace } }
for the first elected records of all rows , most of them are greater than 1960 .
Output: most_greater { all_rows ; first elected ; 1960 }
select the rows whose location record fuzzily matches to boston garden . the sum of the score record of these rows is 843 .
Output: round_eq { sum { filter_eq { all_rows ; location ; boston garden } ; score } ; 843 }
select the rows whose expected year of completion record is equal to 2006 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; expected year of completion ; 2006 } } ; 3 }
the average of the crowd record of all rows is 18,333.33 .
Output: round_eq { avg { all_rows ; crowd } ; 18,333.33 }
select the rows whose frequency record fuzzily matches to 91.1 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; frequency ; 91.1 } } ; 2 }
select the rows whose date record fuzzily matches to october 26 , 1976 . take the score record of this row . select the rows whose date record fuzzily matches to october 23 , 1976 . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; october 26 , 1976 } ; score } ; hop { filter_eq { all_rows ; date ; october 23 , 1976 } ; score } }
select the rows whose name of ground record fuzzily matches to chester road north ground . take the worcs la matches record of this row . select the rows whose name of ground record fuzzily matches to racecourse ground . take the worcs la matches record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name of ground ; chester road north ground } ; worcs la matches } ; hop { filter_eq { all_rows ; name of ground ; racecourse ground } ; worcs la matches } }
select the rows whose peak record fuzzily matches to pietrosul rodnei . take the elevation ( m ) record of this row . select the rows whose peak record fuzzily matches to moldoveanu peak . take the elevation ( m ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; peak ; pietrosul rodnei } ; elevation ( m ) } ; hop { filter_eq { all_rows ; peak ; moldoveanu peak } ; elevation ( m ) } }
for the gender records of all rows , most of them fuzzily match to coed .
Output: most_eq { all_rows ; gender ; coed }
select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose from record fuzzily matches to idaho state . there is only one such row in the table . the jersey number ( s ) record of this unqiue row is 41 .
Output: and { only { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } } ; eq { hop { filter_eq { filter_eq { all_rows ; nationality ; united states } ; from ; idaho state } ; jersey number ( s ) } ; 41 } }
select the row whose duration record of all rows is 2nd maximum . the interregnum began record of this row is 20 october 1740 death of charles vi .
Output: eq { hop { nth_argmax { all_rows ; duration ; 2 } ; interregnum began } ; 20 october 1740 death of charles vi }
the average of the games played record of all rows is 65.2 .
Output: round_eq { avg { all_rows ; games played } ; 65.2 }
select the rows whose artist record fuzzily matches to guide . take the points record of this row . select the rows whose artist record fuzzily matches to balsam boys . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; artist ; guide } ; points } ; hop { filter_eq { all_rows ; artist ; balsam boys } ; points } }
select the rows whose player record fuzzily matches to adam voges . take the runs record of this row . select the rows whose player record fuzzily matches to callum ferguson . take the runs record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; adam voges } ; runs } ; hop { filter_eq { all_rows ; player ; callum ferguson } ; runs } }
for the points 1 records of all rows , most of them are less than 40 .
Output: most_less { all_rows ; points 1 ; 40 }
the 1st minimum year completed record of all rows is 1955 .
Output: eq { nth_min { all_rows ; year completed ; 1 } ; 1955 }
select the rows whose name record fuzzily matches to graham rahal . take the points record of this row . select the rows whose name record fuzzily matches to dan clarke . take the points record of this row . the first record is less than the second record . the points record of the first row is 25 . the points record of the second row is 27 .
Output: and { less { hop { filter_eq { all_rows ; name ; graham rahal } ; points } ; hop { filter_eq { all_rows ; name ; dan clarke } ; points } } ; and { eq { hop { filter_eq { all_rows ; name ; graham rahal } ; points } ; 25 } ; eq { hop { filter_eq { all_rows ; name ; dan clarke } ; points } ; 27 } } }
for the nominee ( s ) records of all rows , most of them fuzzily match to john wells .
Output: most_eq { all_rows ; nominee ( s ) ; john wells }
select the rows whose to par record fuzzily matches to - 9 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; to par ; - 9 } } ; 4 }
select the rows whose team record fuzzily matches to east germany . take the lost record of this row . select the rows whose team record fuzzily matches to poland . take the lost record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; east germany } ; lost } ; hop { filter_eq { all_rows ; team ; poland } ; lost } }
select the rows whose constructor record fuzzily matches to ferrari . select the row whose time / retired record of these rows is minimum . the driver record of this row is rubens barrichello .
Output: eq { hop { argmin { filter_eq { all_rows ; constructor ; ferrari } ; time / retired } ; driver } ; rubens barrichello }
select the rows whose scored record is greater than 30 . there is only one such row in the table . the team record of this unqiue row is tembetary .
Output: and { only { filter_greater { all_rows ; scored ; 30 } } ; eq { hop { filter_greater { all_rows ; scored ; 30 } ; team } ; tembetary } }
select the row whose floors record of all rows is maximum . the building record of this row is fenwick tower ( residential ) .
Output: eq { hop { argmax { all_rows ; floors } ; building } ; fenwick tower ( residential ) }
select the rows whose republican ticket record fuzzily matches to frank . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; republican ticket ; frank } } ; 2 }
select the row whose number record of all rows is 2nd maximum . the language record of this row is yiddish .
Output: eq { hop { nth_argmax { all_rows ; number ; 2 } ; language } ; yiddish }
select the rows whose type record fuzzily matches to nursery . there is only one such row in the table . the name record of this unqiue row is heath lane .
Output: and { only { filter_eq { all_rows ; type ; nursery } } ; eq { hop { filter_eq { all_rows ; type ; nursery } ; name } ; heath lane } }
select the row whose crowd record of all rows is maximum . the home team record of this row is south dragons .
Output: eq { hop { argmax { all_rows ; crowd } ; home team } ; south dragons }
the average of the points record of all rows is 21.2 .
Output: round_eq { avg { all_rows ; points } ; 21.2 }
the 2nd minimum rank record of all rows is 2nd . the population record of the row with 2nd minimum rank record is 36039 .
Output: and { eq { nth_min { all_rows ; rank ; 2 } ; 2nd } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; population } ; 36039 } }
select the rows whose date record fuzzily matches to may 2008 . for the original artist records of these rows , all of them fuzzily match to beatles .
Output: all_eq { filter_eq { all_rows ; date ; may 2008 } ; original artist ; beatles }
select the rows whose top - 5 record is equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; top - 5 ; 0 } } ; 2 }
select the rows whose time record is greater than 48 . there is only one such row in the table . the athlete record of this unqiue row is naiel santiago d'almeida .
Output: and { only { filter_greater { all_rows ; time ; 48 } } ; eq { hop { filter_greater { all_rows ; time ; 48 } ; athlete } ; naiel santiago d'almeida } }
select the rows whose player record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; player } } ; 5 }
select the rows whose date record fuzzily matches to november 22 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 29 . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; november 22 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 29 } ; attendance } }
select the rows whose date record fuzzily matches to october . for the result records of these rows , most of them fuzzily match to w .
Output: most_eq { filter_eq { all_rows ; date ; october } ; result ; w }
select the rows whose position record fuzzily matches to lb . select the row whose overall record of these rows is minimum . the name record of this row is cody glenn .
Output: eq { hop { argmin { filter_eq { all_rows ; position ; lb } ; overall } ; name } ; cody glenn }
select the row whose population record of all rows is 3rd maximum . the english name record of this row is suibin county .
Output: eq { hop { nth_argmax { all_rows ; population ; 3 } ; english name } ; suibin county }
select the row whose population record of all rows is 1st maximum . the official name record of this row is chipman .
Output: eq { hop { nth_argmax { all_rows ; population ; 1 } ; official name } ; chipman }
select the row whose length record of all rows is 1st maximum . the title record of this row is my man and the devil on his shoulder .
Output: eq { hop { nth_argmax { all_rows ; length ; 1 } ; title } ; my man and the devil on his shoulder }
for the jockey records of all rows , most of them fuzzily match to g cooksley .
Output: most_eq { all_rows ; jockey ; g cooksley }
select the row whose date record of all rows is 2nd minimum . the opponent in the final record of this row is melinda czink .
Output: eq { hop { nth_argmin { all_rows ; date ; 2 } ; opponent in the final } ; melinda czink }
select the rows whose track record fuzzily matches to mono 45upm - romance adieu ( weltklang remix ) . take the year record of this row . select the rows whose track record fuzzily matches to kinder aus asbest - hey engel ( weltklang remix ) . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; track ; mono 45upm - romance adieu ( weltklang remix ) } ; year } ; hop { filter_eq { all_rows ; track ; kinder aus asbest - hey engel ( weltklang remix ) } ; year } }
select the row whose reason for change record of all rows is 1st minimum . the vacator record of this row is george h christopher ( d ) .
Output: eq { hop { nth_argmin { all_rows ; reason for change ; 1 } ; vacator } ; george h christopher ( d ) }
for the site records of all rows , most of them fuzzily match to ames .
Output: most_eq { all_rows ; site ; ames }
the maximum wins record of all rows is 5 . the year record of the row with superlative wins record is 1991 .
Output: and { eq { max { all_rows ; wins } ; 5 } ; eq { hop { argmax { all_rows ; wins } ; year } ; 1991 } }
for the result records of all rows , most of them fuzzily match to re - elected .
Output: most_eq { all_rows ; result ; re - elected }
select the row whose length ( male ) record of all rows is 3rd maximum . the scientific name record of this row is trioceros jacksonii .
Output: eq { hop { nth_argmax { all_rows ; length ( male ) ; 3 } ; scientific name } ; trioceros jacksonii }
the average of the goals record of all rows is 3.9 .
Output: round_eq { avg { all_rows ; goals } ; 3.9 }
select the rows whose winner record fuzzily matches to ita . there is only one such row in the table .
Output: only { filter_eq { all_rows ; winner ; ita } }
select the rows whose surface record fuzzily matches to clay . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; surface ; clay } } ; 5 }
the 2nd maximum winning score record of all rows is 69 + 65 + 69 + 69 = 272 .
Output: eq { nth_max { all_rows ; winning score ; 2 } ; 69 + 65 + 69 + 69
select the rows whose took office record fuzzily matches to 195 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; took office ; 195 } } ; 2 }
select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is průhonice - prague , czech republic .
Output: and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; průhonice - prague , czech republic } }
for the year records of all rows , most of them are less than 2012 .
Output: most_less { all_rows ; year ; 2012 }
for the lost records of all rows , most of them are equal to 5 .
Output: most_eq { all_rows ; lost ; 5 }
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 .
Output: eq { hop { nth_argmax { filter_greater { all_rows ; population ( 2011 ) ; 1000 } ; change ( % ) ; 1 } ; name } ; la loche }
select the row whose height ( m ) record of all rows is 2nd maximum . the name record of this row is messeturm .
Output: eq { hop { nth_argmax { all_rows ; height ( m ) ; 2 } ; name } ; messeturm }
select the rows whose historical references record fuzzily matches to the wright brothers . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; historical references ; the wright brothers } } ; 2 }
for the date records of all rows , all of them fuzzily match to 10 july 1954 .
Output: all_eq { all_rows ; date ; 10 july 1954 }
select the rows whose state record fuzzily matches to illinois . among these rows , select the rows whose founded record is equal to 1887 . there is only one such row in the table . the school record of this unqiue row is benedictine university .
Output: and { only { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; illinois } ; founded ; 1887 } ; school } ; benedictine university } }
select the rows whose municipality record fuzzily matches to jose panganiban . take the area ( km square ) record of this row . select the rows whose municipality record fuzzily matches to daet ( capital town ) . take the area ( km square ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; municipality ; jose panganiban } ; area ( km square ) } ; hop { filter_eq { all_rows ; municipality ; daet ( capital town ) } ; area ( km square ) } }
the average of the height record of all rows is 1.99 .
Output: round_eq { avg { all_rows ; height } ; 1.99 }
select the rows whose single record fuzzily matches to tell the world . take the other details record of this row . select the rows whose single record fuzzily matches to my love will follow me . take the other details record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; single ; tell the world } ; other details } ; hop { filter_eq { all_rows ; single ; my love will follow me } ; other details } }
select the rows whose date record fuzzily matches to 30 august 1952 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; date ; 30 august 1952 } } ; 6 }
select the rows whose surface record fuzzily matches to hard . among these rows , select the rows whose partner record fuzzily matches to magdalena maleeva . there is only one such row in the table . the date record of this unqiue row is july 16 , 1992 .
Output: and { only { filter_eq { filter_eq { all_rows ; surface ; hard } ; partner ; magdalena maleeva } } ; eq { hop { filter_eq { filter_eq { all_rows ; surface ; hard } ; partner ; magdalena maleeva } ; date } ; july 16 , 1992 } }
select the row whose attendance record of all rows is maximum . the date record of this row is november 1 , 1953 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; november 1 , 1953 }
the average of the points record of all rows is 23.25 .
Output: round_eq { avg { all_rows ; points } ; 23.25 }
the 1st minimum rank record of all rows is 1 . the country record of the row with 1st minimum rank record is dominican republic .
Output: and { eq { nth_min { all_rows ; rank ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; country } ; dominican republic } }
select the row whose mean free path record of all rows is 2nd maximum . the vacuum range record of this row is ultra high vacuum .
Output: eq { hop { nth_argmax { all_rows ; mean free path ; 2 } ; vacuum range } ; ultra high vacuum }
select the row whose time record of all rows is 3rd minimum . the name record of this row is marianne limpert .
Output: eq { hop { nth_argmin { all_rows ; time ; 3 } ; name } ; marianne limpert }
select the rows whose result record fuzzily matches to re - elected . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 5 }
for the ground records of all rows , most of them fuzzily match to humber college north .
Output: most_eq { all_rows ; ground ; humber college north }
for the crowd records of all rows , most of them are greater than 14,000 .
Output: most_greater { all_rows ; crowd ; 14,000 }
the average of the score in the final record of all rows is 6 .
Output: round_eq { avg { all_rows ; score in the final } ; 6 }
the average of the prize ( k ) record of all rows is 219.6 .
Output: round_eq { avg { all_rows ; prize ( k ) } ; 219.6 }
select the rows whose votes record is greater than 100000 . among these rows , select the rows whose seats record is greater than 20 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_greater { all_rows ; votes ; 100000 } ; seats ; 20 } } ; 3 }
for the total units sold records of all rows , most of them are greater than 1500000 .
Output: most_greater { all_rows ; total units sold ; 1500000 }
for the location records of all rows , most of them fuzzily match to ghent .
Output: most_eq { all_rows ; location ; ghent }
the average of the points record of all rows is 80 .
Output: round_eq { avg { all_rows ; points } ; 80 }
select the rows whose player record fuzzily matches to tiger woods . take the score record of this row . select the rows whose player record fuzzily matches to fred couples . take the score record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; tiger woods } ; score } ; hop { filter_eq { all_rows ; player ; fred couples } ; score } }
select the rows whose result record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is cătălin zmărăndescu .
Output: and { only { filter_eq { all_rows ; result ; draw } } ; eq { hop { filter_eq { all_rows ; result ; draw } ; opponent } ; cătălin zmărăndescu } }
select the row whose bronze record of all rows is 3rd maximum . the nation record of this row is soviet union .
Output: eq { hop { nth_argmax { all_rows ; bronze ; 3 } ; nation } ; soviet union }
for the honour records of all rows , all of them fuzzily match to medal of the order of australia .
Output: all_eq { all_rows ; honour ; medal of the order of australia }
select the rows whose directed by record fuzzily matches to roger christiansen . the minimum original air date record of these rows is january 9 , 2010 .
Output: eq { min { filter_eq { all_rows ; directed by ; roger christiansen } ; original air date } ; january 9 , 2010 }
select the row whose draw record of all rows is 1st minimum . the artist record of this row is alma čardžić .
Output: eq { hop { nth_argmin { all_rows ; draw ; 1 } ; artist } ; alma čardžić }
select the rows whose school record fuzzily matches to triton . take the year joined record of this row . select the rows whose school record fuzzily matches to oak hill . take the year joined record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; school ; triton } ; year joined } ; hop { filter_eq { all_rows ; school ; oak hill } ; year joined } }
the average of the attendance record of all rows is 6999 .
Output: round_eq { avg { all_rows ; attendance } ; 6999 }
select the rows whose manager record fuzzily matches to tommy shields . there is only one such row in the table . the finish record of this unqiue row is 4th .
Output: and { only { filter_eq { all_rows ; manager ; tommy shields } } ; eq { hop { filter_eq { all_rows ; manager ; tommy shields } ; finish } ; 4th } }
the sum of the runs record of all rows is 4832 .
Output: round_eq { sum { all_rows ; runs } ; 4832 }
the average of the elevation ( m ) record of all rows is 2494.63 .
Output: round_eq { avg { all_rows ; elevation ( m ) } ; 2494.63 }
the maximum attendance record of all rows is 19567 . the opponent record of the row with superlative attendance record is philadelphia flyers .
Output: and { eq { max { all_rows ; attendance } ; 19567 } ; eq { hop { argmax { all_rows ; attendance } ; opponent } ; philadelphia flyers } }
select the rows whose matches record is equal to 6 . there is only one such row in the table . the player record of this unqiue row is mark turner .
Output: and { only { filter_eq { all_rows ; matches ; 6 } } ; eq { hop { filter_eq { all_rows ; matches ; 6 } ; player } ; mark turner } }
select the rows whose nation record fuzzily matches to serbia . there is only one such row in the table .
Output: only { filter_eq { all_rows ; nation ; serbia } }
select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is vfl park .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; vfl park }
the average of the total position record of all rows is 10.73 .
Output: round_eq { avg { all_rows ; total position } ; 10.73 }
for the party records of all rows , most of them fuzzily match to republican .
Output: most_eq { all_rows ; party ; republican }
the average of the crowd record of all rows is 25185 .
Output: round_eq { avg { all_rows ; crowd } ; 25185 }
for the attendance records of all rows , most of them are greater than 40000 .
Output: most_greater { all_rows ; attendance ; 40000 }
select the rows whose date record fuzzily matches to november . among these rows , select the rows whose game site record fuzzily matches to tampa stadium . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; date ; november } ; game site ; tampa stadium } } ; 3 }
select the rows whose date record fuzzily matches to 20 nov 2005 . take the margin of victory record of this row . select the rows whose date record fuzzily matches to 10 sep 2006 . take the margin of victory record of this row . the first record is 2 strokes larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; date ; 20 nov 2005 } ; margin of victory } ; hop { filter_eq { all_rows ; date ; 10 sep 2006 } ; margin of victory } } ; 2 strokes }
select the row whose population record of all rows is 3rd maximum . the ethnic group record of this row is saho .
Output: eq { hop { nth_argmax { all_rows ; population ; 3 } ; ethnic group } ; saho }
select the rows whose borough or census area record fuzzily matches to fairbanks north star . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; borough or census area ; fairbanks north star } } ; 4 }
select the rows whose easa ( eu ) record fuzzily matches to yes . there is only one such row in the table . the airline record of this unqiue row is premiair .
Output: and { only { filter_eq { all_rows ; easa ( eu ) ; yes } } ; eq { hop { filter_eq { all_rows ; easa ( eu ) ; yes } ; airline } ; premiair } }
select the row whose away record of all rows is maximum . the season record of this row is 2001 - 02 .
Output: eq { hop { argmax { all_rows ; away } ; season } ; 2001 - 02 }
select the rows whose builder record fuzzily matches to g & swr kilmarnock . select the row whose no built record of these rows is 3rd maximum . the class record of this row is 153 .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; builder ; g & swr kilmarnock } ; no built ; 3 } ; class } ; 153 }
for the us viewers ( million ) records of all rows , most of them are greater than 6 .
Output: most_greater { all_rows ; us viewers ( million ) ; 6 }
select the row whose crowd record of all rows is maximum . the venue record of this row is moorabbin oval .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; moorabbin oval }
for the crowd records of all rows , most of them are greater than 17000 .
Output: most_greater { all_rows ; crowd ; 17000 }
select the rows whose introduced record is less than 2000 . among these rows , select the rows whose fleet size record is equal to 2 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_less { all_rows ; introduced ; 2000 } ; fleet size ; 2 } } ; 2 }
the average of the year joined record of all rows is 1977 .
Output: round_eq { avg { all_rows ; year joined } ; 1977 }
select the rows whose championship record fuzzily matches to world snooker championship . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; championship ; world snooker championship } } ; 2 }
select the row whose unemployment rate record of all rows is 2nd maximum . the county record of this row is allegany .
Output: eq { hop { nth_argmax { all_rows ; unemployment rate ; 2 } ; county } ; allegany }
the average of the number of seats record of all rows is 44 .
Output: round_eq { avg { all_rows ; number of seats } ; 44 }
for the founded records of all rows , most of them are less than 1900 .
Output: most_less { all_rows ; founded ; 1900 }
select the rows whose constructor record fuzzily matches to ferrari . the sum of the laps record of these rows is 140 .
Output: round_eq { sum { filter_eq { all_rows ; constructor ; ferrari } ; laps } ; 140 }
the average of the laps record of all rows is 102.9 .
Output: round_eq { avg { all_rows ; laps } ; 102.9 }
select the row whose attendance record of all rows is 3rd maximum . the date record of this row is october 13 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; october 13 }
for the general classification records of all rows , most of them fuzzily match to alejandro valverde .
Output: most_eq { all_rows ; general classification ; alejandro valverde }
select the rows whose country record fuzzily matches to réunion . among these rows , select the rows whose airport record fuzzily matches to roland garros airport . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; country ; réunion } ; airport ; roland garros airport } } ; 1 }
select the rows whose venue record fuzzily matches to athens , greece . take the date record of this row . select the rows whose venue record fuzzily matches to oslo , norway . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; venue ; athens , greece } ; date } ; hop { filter_eq { all_rows ; venue ; oslo , norway } ; date } }
select the row whose profits ( billion ) record of all rows is 2nd maximum . the company record of this row is royal dutch shell .
Output: eq { hop { nth_argmax { all_rows ; profits ( billion ) ; 2 } ; company } ; royal dutch shell }
select the rows whose team record fuzzily matches to new jersey . the 1st minimum date record of these rows is october 5 .
Output: eq { nth_min { filter_eq { all_rows ; team ; new jersey } ; date ; 1 } ; october 5 }
select the row whose last aired record of all rows is 2nd minimum . the show record of this row is high rollers .
Output: eq { hop { nth_argmin { all_rows ; last aired ; 2 } ; show } ; high rollers }
the sum of the hard discounters record of all rows is 5096 .
Output: round_eq { sum { all_rows ; hard discounters } ; 5096 }
select the rows whose winning score record fuzzily matches to - 7 . there is only one such row in the table . the tournament record of this unqiue row is us women 's open .
Output: and { only { filter_eq { all_rows ; winning score ; - 7 } } ; eq { hop { filter_eq { all_rows ; winning score ; - 7 } ; tournament } ; us women 's open } }
select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_less { all_rows ; away team score ; 10 } ; crowd ; 20000 } } ; 2 }
select the row whose gold record of all rows is maximum . the nation record of this row is netherlands ( ned ) .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; netherlands ( ned ) }
select the rows whose result record fuzzily matches to 4-0 . there is only one such row in the table . the year record of this unqiue row is 2006 .
Output: and { only { filter_eq { all_rows ; result ; 4-0 } } ; eq { hop { filter_eq { all_rows ; result ; 4-0 } ; year } ; 2006 } }
select the rows whose opponent record fuzzily matches to toronto maple leafs . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; opponent ; toronto maple leafs } } ; 3 }
the average of the to par record of all rows is -5.6 .
Output: round_eq { avg { all_rows ; to par } ; -5.6 }
the 1st minimum us release date record of all rows is 27 june 2006 . the title record of the row with 1st minimum us release date record is galaxy angel .
Output: and { eq { nth_min { all_rows ; us release date ; 1 } ; 27 june 2006 } ; eq { hop { nth_argmin { all_rows ; us release date ; 1 } ; title } ; galaxy angel } }
the average of the margin ( pts ) record of all rows is 27.25 .
Output: round_eq { avg { all_rows ; margin ( pts ) } ; 27.25 }
select the row whose caps record of all rows is 3rd maximum . the player record of this row is kyle beckerman .
Output: eq { hop { nth_argmax { all_rows ; caps ; 3 } ; player } ; kyle beckerman }
the average of the weight ( lbs ) record of all rows is 238.05 .
Output: round_eq { avg { all_rows ; weight ( lbs ) } ; 238.05 }
select the rows whose date record fuzzily matches to oct . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; date ; oct } } ; 6 }
select the rows whose year record fuzzily matches to 199 . for the performance records of these rows , most of them fuzzily match to triple jump .
Output: most_eq { filter_eq { all_rows ; year ; 199 } ; performance ; triple jump }
select the rows whose directed by record fuzzily matches to brent woods . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; directed by ; brent woods } } ; 2 }
select the rows whose leading lady record fuzzily matches to alberta vaughn . there is only one such row in the table . the title record of this unqiue row is randy rides alone .
Output: and { only { filter_eq { all_rows ; leading lady ; alberta vaughn } } ; eq { hop { filter_eq { all_rows ; leading lady ; alberta vaughn } ; title } ; randy rides alone } }
select the rows whose date record fuzzily matches to december 1976 . for the result records of these rows , all of them fuzzily match to w .
Output: all_eq { filter_eq { all_rows ; date ; december 1976 } ; result ; w }
select the rows whose city of license record fuzzily matches to ellijay , georgia . take the frequency mhz record of this row . select the rows whose city of license record fuzzily matches to morrow , georgia . take the frequency mhz record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; city of license ; ellijay , georgia } ; frequency mhz } ; hop { filter_eq { all_rows ; city of license ; morrow , georgia } ; frequency mhz } }
the maximum total record of all rows is 8 . the nation record of the row with superlative total record is soviet union ( urs ) . the gold record of the row with superlative total record is 3 . the silver record of the row with superlative total record is 3 .
Output: and { eq { max { all_rows ; total } ; 8 } ; and { eq { hop { argmax { all_rows ; total } ; nation } ; soviet union ( urs ) } ; and { eq { hop { argmax { all_rows ; total } ; gold } ; 3 } ; eq { hop { argmax { all_rows ; total } ; silver } ; 3 } } } }
select the rows whose nationality record fuzzily matches to greece . there is only one such row in the table . the athlete record of this unqiue row is konstadinos douvalidis .
Output: and { only { filter_eq { all_rows ; nationality ; greece } } ; eq { hop { filter_eq { all_rows ; nationality ; greece } ; athlete } ; konstadinos douvalidis } }
select the rows whose date record fuzzily matches to december 30 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 26 . take the attendance record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; date ; december 30 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 26 } ; attendance } }
select the row whose founded record of all rows is minimum . the institution record of this row is garden city community college .
Output: eq { hop { argmin { all_rows ; founded } ; institution } ; garden city community college }
select the row whose crowd record of all rows is 1st maximum . the venue record of this row is princes park .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; princes park }
the average of the population ( 2010 census ) record of all rows is 68,253 .
Output: round_eq { avg { all_rows ; population ( 2010 census ) } ; 68,253 }
select the rows whose average record is greater than 10000 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; average ; 10000 } } ; 3 }
select the row whose wins record of all rows is maximum . the club record of this row is ca osasuna .
Output: eq { hop { argmax { all_rows ; wins } ; club } ; ca osasuna }
select the rows whose points record is equal to 0 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; points ; 0 } } ; 3 }
select the rows whose points record is equal to 30 . there is only one such row in the table . the team record of this unqiue row is portuguesa .
Output: and { only { filter_eq { all_rows ; points ; 30 } } ; eq { hop { filter_eq { all_rows ; points ; 30 } ; team } ; portuguesa } }
select the rows whose equatorial bulge record is greater than 1000 . among these rows , select the rows whose polar diameter record is greater than 100,000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; equatorial bulge ; 1000 } ; polar diameter ; 100,000 } } ; 2 }
select the rows whose entrant record fuzzily matches to team lotus . take the points record of this row . select the rows whose entrant record fuzzily matches to team lotus . take the points record of this row . the first record is equal to the second record .
Output: eq { hop { filter_eq { all_rows ; entrant ; team lotus } ; points } ; hop { filter_eq { all_rows ; entrant ; team lotus } ; points } }
select the rows whose debut in europe record is equal to 1995 . select the row whose games record of these rows is maximum . the player record of this row is raãl .
Output: eq { hop { argmax { filter_eq { all_rows ; debut in europe ; 1995 } ; games } ; player } ; raãl }
select the rows whose no of couples record is equal to 4 . the average of the viewers ( millions ) record of these rows is 4.575 .
Output: round_eq { avg { filter_eq { all_rows ; no of couples ; 4 } ; viewers ( millions ) } ; 4.575 }
the average of the number of powiats record of all rows is 2.5 .
Output: round_eq { avg { all_rows ; number of powiats } ; 2.5 }
for the date records of all rows , most of them fuzzily match to april .
Output: most_eq { all_rows ; date ; april }
select the rows whose race record fuzzily matches to german grand prix . take the date record of this row . select the rows whose race record fuzzily matches to british grand prix . take the date record of this row . the first record is 15 days larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; race ; german grand prix } ; date } ; hop { filter_eq { all_rows ; race ; british grand prix } ; date } } ; 15 days }
select the rows whose code name record fuzzily matches to surfer . take the serial number record of this row . select the rows whose code name record fuzzily matches to shark . take the serial number record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; code name ; surfer } ; serial number } ; hop { filter_eq { all_rows ; code name ; shark } ; serial number } }
select the row whose pick record of all rows is maximum . the player record of this row is james campbell .
Output: eq { hop { argmax { all_rows ; pick } ; player } ; james campbell }
the average of the points record of all rows is 19.5 .
Output: round_eq { avg { all_rows ; points } ; 19.5 }
for the points records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; points ; 0 }
select the rows whose affiliation record fuzzily matches to private christian . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; affiliation ; private christian } } ; 2 }
for the manufacturer records of all rows , most of them fuzzily match to chevrolet .
Output: most_eq { all_rows ; manufacturer ; chevrolet }
select the rows whose categoría record fuzzily matches to rock group . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; categoría ; rock group } } ; 2 }
for the ihsaa class records of all rows , all of them fuzzily match to aaa .
Output: all_eq { all_rows ; ihsaa class ; aaa }
select the row whose production code record of all rows is 2nd maximum . the episode record of this row is 809 .
Output: eq { hop { nth_argmax { all_rows ; production code ; 2 } ; episode } ; 809 }
select the rows whose td 's record is greater than or equal to 3 . the number of such rows is 4 .
Output: eq { count { filter_greater_eq { all_rows ; td 's ; 3 } } ; 4 }
select the rows whose written by record fuzzily matches to kevin falls . among these rows , select the rows whose directed by record fuzzily matches to alex graves . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; written by ; kevin falls } ; directed by ; alex graves } } ; 2 }
select the row whose laps led record of all rows is maximum . the driver record of this row is sam hornish , jr .
Output: eq { hop { argmax { all_rows ; laps led } ; driver } ; sam hornish , jr }
select the rows whose venue record fuzzily matches to western oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to windy hill . take the crowd record of this row . the first record is 3755 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; venue ; western oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; windy hill } ; crowd } } ; 3755 }
the sum of the hotel rooms record of all rows is 18447 .
Output: round_eq { sum { all_rows ; hotel rooms } ; 18447 }
select the row whose rnd record of all rows is 3rd minimum . the date record of this row is 1 march .
Output: eq { hop { nth_argmin { all_rows ; rnd ; 3 } ; date } ; 1 march }
select the rows whose team record fuzzily matches to vélez sársfield . take the points record of this row . select the rows whose team record fuzzily matches to newell 's old boys . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; vélez sársfield } ; points } ; hop { filter_eq { all_rows ; team ; newell 's old boys } ; points } }
select the rows whose state record fuzzily matches to new york . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; state ; new york } } ; 3 }
select the row whose votes record of all rows is 3rd maximum . the candidate 's name record of this row is michael mackay .
Output: eq { hop { nth_argmax { all_rows ; votes ; 3 } ; candidate 's name } ; michael mackay }
select the row whose withdrawal record of all rows is maximum . the irish name record of this row is flóirín .
Output: eq { hop { argmax { all_rows ; withdrawal } ; irish name } ; flóirín }
select the rows whose status record fuzzily matches to retired republican hold . there is only one such row in the table . the district record of this unqiue row is minnesota3 .
Output: and { only { filter_eq { all_rows ; status ; retired republican hold } } ; eq { hop { filter_eq { all_rows ; status ; retired republican hold } ; district } ; minnesota3 } }
for the crowd records of all rows , most of them are greater than or equal to 20,000 .
Output: most_greater_eq { all_rows ; crowd ; 20,000 }
select the rows whose air date record fuzzily matches to 2008 . the number of such rows is 11 .
Output: eq { count { filter_eq { all_rows ; air date ; 2008 } } ; 11 }
select the rows whose max processors record fuzzily matches to 1 ultrasparc t1 . select the row whose max memory record of these rows is 1st minimum . the model record of this row is t1000 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; max processors ; 1 ultrasparc t1 } ; max memory ; 1 } ; model } ; t1000 }
select the rows whose game site record fuzzily matches to the meadowlands . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; game site ; the meadowlands } } ; 8 }
select the rows whose monday to friday record fuzzily matches to monday to saturday . there is only one such row in the table . the mexico record of this unqiue row is bosnia and herzegovina .
Output: and { only { filter_eq { all_rows ; monday to friday ; monday to saturday } } ; eq { hop { filter_eq { all_rows ; monday to friday ; monday to saturday } ; mexico } ; bosnia and herzegovina } }
select the rows whose points record is less than 20 . among these rows , select the rows whose draw record is equal to 2 . there is only one such row in the table . the team record of this unqiue row is wanda nowa huta .
Output: and { only { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } } ; eq { hop { filter_eq { filter_less { all_rows ; points ; 20 } ; draw ; 2 } ; team } ; wanda nowa huta } }
the sum of the us viewers ( million ) record of all rows is 7.9 .
Output: round_eq { sum { all_rows ; us viewers ( million ) } ; 7.9 }
select the rows whose date record fuzzily matches to october . among these rows , select the rows whose date record fuzzily matches to october . the number of such rows is 5 .
Output: eq { count { filter_eq { filter_eq { all_rows ; date ; october } ; date ; october } } ; 5 }
the average of the 2001 - 02 record of all rows is 2907 .
Output: round_eq { avg { all_rows ; 2001 - 02 } ; 2907 }
for the league / division records of all rows , most of them fuzzily match to first division .
Output: most_eq { all_rows ; league / division ; first division }
select the rows whose high rebounds record fuzzily matches to , . there is only one such row in the table . the date record of this unqiue row is september 12 .
Output: and { only { filter_eq { all_rows ; high rebounds ; , } } ; eq { hop { filter_eq { all_rows ; high rebounds ; , } ; date } ; september 12 } }
the average of the goals per match record of all rows is 1.052 .
Output: round_eq { avg { all_rows ; goals per match } ; 1.052 }
select the row whose total record of all rows is 1st maximum . the nation record of this row is france ( fra ) .
Output: eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; france ( fra ) }
select the rows whose player record fuzzily matches to gillis wilson . take the round record of this row . select the rows whose player record fuzzily matches to lester towns . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; gillis wilson } ; round } ; hop { filter_eq { all_rows ; player ; lester towns } ; round } }