niryuu/sni-each-converted huggingface dataset:
select the rows whose men 's singles record fuzzily matches to andrew smith . 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 ; men 's singles ; andrew smith } } ; eq { hop { filter_eq { all_rows ; men 's singles ; andrew smith } ; year } ; 2006 } }
select the row whose attendance record of all rows is maximum . the opponent record of this row is detroit lions .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; detroit lions }
select the row whose rainfall by volume ( km 3 / year ) record of all rows is maximum . the region record of this row is huetar atlántico .
Output: eq { hop { argmax { all_rows ; rainfall by volume ( km 3 / year ) } ; region } ; huetar atlántico }
select the row whose date record of all rows is 5th minimum . the team record of this row is houston .
Output: eq { hop { nth_argmin { all_rows ; date ; 5 } ; team } ; houston }
select the rows whose men 's singles record fuzzily matches to kevin cordón . the minimum year record of these rows is 2009 .
Output: eq { min { filter_eq { all_rows ; men 's singles ; kevin cordón } ; year } ; 2009 }
select the row whose laps record of all rows is 1st maximum . the year record of this row is 2008 .
Output: eq { hop { nth_argmax { all_rows ; laps ; 1 } ; year } ; 2008 }
select the rows whose finish record fuzzily matches to avignon . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; finish ; avignon } } ; 3 }
select the rows whose engine record fuzzily matches to 2.7 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; engine ; 2.7 } } ; 3 }
the 9th minimum week record of all rows is 9 . the attendance record of the row with 9th minimum week record is 46978 .
Output: and { eq { nth_min { all_rows ; week ; 9 } ; 9 } ; eq { hop { nth_argmin { all_rows ; week ; 9 } ; attendance } ; 46978 } }
for the owner records of all rows , most of them fuzzily match to canadian broadcasting corporation .
Output: most_eq { all_rows ; owner ; canadian broadcasting corporation }
select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is victoria park .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; victoria park }
for the top - 10 records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; top - 10 ; 0 }
select the row whose blocks per game record of all rows is maximum . the month record of this row is december 2007 .
Output: eq { hop { argmax { all_rows ; blocks per game } ; month } ; december 2007 }
for the city of license records of all rows , most of them fuzzily match to colorado .
Output: most_eq { all_rows ; city of license ; colorado }
select the rows whose status record fuzzily matches to millennium trophy match . there is only one such row in the table . the opposing teams record of this unqiue row is ireland .
Output: and { only { filter_eq { all_rows ; status ; millennium trophy match } } ; eq { hop { filter_eq { all_rows ; status ; millennium trophy match } ; opposing teams } ; ireland } }
the average of the population record of all rows is 60970 .
Output: round_eq { avg { all_rows ; population } ; 60970 }
select the row whose peak record of all rows is maximum . the english title record of this row is heart of greed .
Output: eq { hop { argmax { all_rows ; peak } ; english title } ; heart of greed }
select the rows whose top - 5 record is greater than 0 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; top - 5 ; 0 } } ; 2 }
for the total fat records of all rows , most of them are equal to 100 .
Output: most_eq { all_rows ; total fat ; 100 }
select the row whose commenced operations record of all rows is 2nd minimum . the airline record of this row is nouvelair .
Output: eq { hop { nth_argmin { all_rows ; commenced operations ; 2 } ; airline } ; nouvelair }
select the rows whose series record fuzzily matches to gp2 series . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; series ; gp2 series } } ; 2 }
the average of the crowd record of all rows is 16667 .
Output: round_eq { avg { all_rows ; crowd } ; 16667 }
the average of the population ( 2011 ) record of all rows is 706.73 .
Output: round_eq { avg { all_rows ; population ( 2011 ) } ; 706.73 }
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 .
Output: greater { hop { filter_eq { all_rows ; game ; 55 } ; score } ; hop { filter_eq { all_rows ; game ; 56 } ; score } }
the 2nd maximum weeks record of all rows is 16 weeks .
Output: eq { nth_max { all_rows ; weeks ; 2 } ; 16 weeks }
select the rows whose season record fuzzily matches to 9 . take the viewers ( in millions ) record of this row . select the rows whose season record fuzzily matches to 4 . take the viewers ( in millions ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; season ; 9 } ; viewers ( in millions ) } ; hop { filter_eq { all_rows ; season ; 4 } ; viewers ( in millions ) } }
select the rows whose rider record fuzzily matches to stan woods . take the time record of this row . select the rows whose rider record fuzzily matches to tom loughridge . take the time record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; rider ; stan woods } ; time } ; hop { filter_eq { all_rows ; rider ; tom loughridge } ; time } }
select the rows whose incumbent record fuzzily matches to frank t bow . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to louis stokes . take the first elected record of this row . the second record is 18 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; incumbent ; frank t bow } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; louis stokes } ; first elected } } ; -18 years }
select the rows whose venue record fuzzily matches to dairy farmers stadium . there is only one such row in the table . the opponent record of this unqiue row is north queensland cowboys .
Output: and { only { filter_eq { all_rows ; venue ; dairy farmers stadium } } ; eq { hop { filter_eq { all_rows ; venue ; dairy farmers stadium } ; opponent } ; north queensland cowboys } }
select the rows whose erp w record fuzzily matches to 10 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; erp w ; 10 } } ; 2 }
select the rows whose result record fuzzily matches to re-elected . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; re-elected } } ; 2 }
the average of the attendance record of all rows is 15720 .
Output: round_eq { avg { all_rows ; attendance } ; 15720 }
select the row whose jersey number ( s ) record of all rows is 2nd maximum . the player record of this row is patrick ewing .
Output: eq { hop { nth_argmax { all_rows ; jersey number ( s ) ; 2 } ; player } ; patrick ewing }
select the rows whose result record fuzzily matches to lost re-election republican gain . there is only one such row in the table . the incumbent record of this unqiue row is tom luken .
Output: and { only { filter_eq { all_rows ; result ; lost re-election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re-election republican gain } ; incumbent } ; tom luken } }
select the row whose date record of all rows is minimum . the city / location record of this row is ontario , california .
Output: eq { hop { argmin { all_rows ; date } ; city / location } ; ontario , california }
select the row whose of seats won record of all rows is maximum . the election record of this row is 1984 .
Output: eq { hop { argmax { all_rows ; of seats won } ; election } ; 1984 }
select the row whose number of seasons in liga mx record of all rows is maximum . the club record of this row is américa .
Output: eq { hop { argmax { all_rows ; number of seasons in liga mx } ; club } ; américa }
select the rows whose gold record is greater than 10 . there is only one such row in the table . the nation record of this unqiue row is united states .
Output: and { only { filter_greater { all_rows ; gold ; 10 } } ; eq { hop { filter_greater { all_rows ; gold ; 10 } ; nation } ; united states } }
select the rows whose race record fuzzily matches to indianapolis 500 . take the date record of this row . select the rows whose race record fuzzily matches to monaco grand prix . take the date record of this row . the first record is 1 day larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; race ; indianapolis 500 } ; date } ; hop { filter_eq { all_rows ; race ; monaco grand prix } ; date } } ; 1 day }
select the row whose date of commissioning record of all rows is maximum . the unit number record of this row is 8 .
Output: eq { hop { argmax { all_rows ; date of commissioning } ; unit number } ; 8 }
the sum of the races record of all rows is 164 .
Output: round_eq { sum { all_rows ; races } ; 164 }
select the rows whose country record fuzzily matches to united states . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 5 }
for the tournament records of all rows , most of them fuzzily match to 25000 .
Output: most_eq { all_rows ; tournament ; 25000 }
select the rows whose method record fuzzily matches to tko . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; method ; tko } } ; 7 }
for the points records of all rows , most of them are less than 10 .
Output: most_less { all_rows ; points ; 10 }
select the rows whose title record fuzzily matches to cry turmoil . take the originalairdate record of this row . select the rows whose title record fuzzily matches to the deadly pyramid . take the originalairdate record of this row . the first record is less than the second record . the originalairdate record of the first row is november 5 , 1994 . the originalairdate record of the second row is november 12 , 1994 .
Output: and { less { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } } ; and { eq { hop { filter_eq { all_rows ; title ; cry turmoil } ; originalairdate } ; november 5 , 1994 } ; eq { hop { filter_eq { all_rows ; title ; the deadly pyramid } ; originalairdate } ; november 12 , 1994 } } }
select the row whose competition record of all rows is 1st minimum . the date record of this row is 29 october 1975 .
Output: eq { hop { nth_argmin { all_rows ; competition ; 1 } ; date } ; 29 october 1975 }
select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . select the rows whose opponent record fuzzily matches to pittsburgh steelers . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } ; hop { filter_eq { all_rows ; opponent ; pittsburgh steelers } ; attendance } }
select the rows whose points record is greater than or equal to 20 . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { all_rows ; points ; 20 } } ; 3 }
select the rows whose opponent record fuzzily matches to buffalo bills . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; buffalo bills } } ; 2 }
select the rows whose country record fuzzily matches to united states . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 4 }
select the row whose attendance record of all rows is maximum . the location record of this row is kohl center madison , wi .
Output: eq { hop { argmax { all_rows ; attendance } ; location } ; kohl center madison , wi }
select the rows whose successor record fuzzily matches to bob livingston ( r ) . take the date successor seated record of this row . select the rows whose successor record fuzzily matches to robert garcía ( d ) . take the date successor seated record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; successor ; bob livingston ( r ) } ; date successor seated } ; hop { filter_eq { all_rows ; successor ; robert garcía ( d ) } ; date successor seated } }
select the rows whose player record fuzzily matches to ( a ) . there is only one such row in the table . the player record of this unqiue row is jim simons ( a ) .
Output: and { only { filter_eq { all_rows ; player ; ( a ) } } ; eq { hop { filter_eq { all_rows ; player ; ( a ) } ; player } ; jim simons ( a ) } }
select the rows whose provider ( national government ) record fuzzily matches to ministry of defense . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; provider ( national government ) ; ministry of defense } } ; 2 }
select the row whose attendance record of all rows is maximum . the date record of this row is nov 23 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; nov 23 }
select the rows whose result record fuzzily matches to 5 - 0 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; result ; 5 - 0 } } ; 3 }
select the rows whose engine record fuzzily matches to judd av . there is only one such row in the table .
Output: only { filter_eq { all_rows ; engine ; judd av } }
select the row whose time record of all rows is 3rd minimum . the country record of this row is united states .
Output: eq { hop { nth_argmin { all_rows ; time ; 3 } ; country } ; united states }
select the rows whose prize money ( usd ) record is less than 25000 . there is only one such row in the table . the name record of this unqiue row is jan kodeå ¡ .
Output: and { only { filter_less { all_rows ; prize money ( usd ) ; 25000 } } ; eq { hop { filter_less { all_rows ; prize money ( usd ) ; 25000 } ; name } ; jan kodeå ¡ } }
select the rows whose host record fuzzily matches to kettering . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; host ; kettering } } ; 2 }
select the row whose year record of all rows is minimum . the title record of this row is the chipmunk adventure .
Output: eq { hop { argmin { all_rows ; year } ; title } ; the chipmunk adventure }
select the row whose time record of all rows is minimum . the name record of this row is massimiliano rosolino .
Output: eq { hop { argmin { all_rows ; time } ; name } ; massimiliano rosolino }
select the row whose obama % record of all rows is maximum . the county record of this row is essex .
Output: eq { hop { argmax { all_rows ; obama % } ; county } ; essex }
select the rows whose tournament record fuzzily matches to japan . select the row whose date record of these rows is 2nd minimum . the surface record of this row is carpet ( i ) .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; tournament ; japan } ; date ; 2 } ; surface } ; carpet ( i ) }
select the rows whose year record fuzzily matches to 1954 . take the points record of this row . select the rows whose year record fuzzily matches to 1952 . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1954 } ; points } ; hop { filter_eq { all_rows ; year ; 1952 } ; points } }
select the rows whose cores record is equal to 1 . there is only one such row in the table . the model number record of this unqiue row is itanium 2 9010 .
Output: and { only { filter_eq { all_rows ; cores ; 1 } } ; eq { hop { filter_eq { all_rows ; cores ; 1 } ; model number } ; itanium 2 9010 } }
for the mlb draft records of all rows , most of them fuzzily match to 1995 .
Output: most_eq { all_rows ; mlb draft ; 1995 }
select the row whose score record of all rows is 2nd maximum . the date record of this row is march 13 . the opponent record of this row is baltimore bullets .
Output: and { eq { hop { nth_argmax { all_rows ; score ; 2 } ; date } ; march 13 } ; eq { hop { nth_argmax { all_rows ; score ; 2 } ; opponent } ; baltimore bullets } }
select the row whose published record of all rows is 2nd maximum . the title record of this row is hollywood .
Output: eq { hop { nth_argmax { all_rows ; published ; 2 } ; title } ; hollywood }
select the rows whose location attendance record fuzzily matches to great western . there is only one such row in the table . the game record of this unqiue row is 35 .
Output: and { only { filter_eq { all_rows ; location attendance ; great western } } ; eq { hop { filter_eq { all_rows ; location attendance ; great western } ; game } ; 35 } }
select the rows whose name record is arbitrary . the number of such rows is 11 .
Output: eq { count { filter_all { all_rows ; name } } ; 11 }
for the result records of all rows , most of them fuzzily match to won .
Output: most_eq { all_rows ; result ; won }
select the rows whose position record fuzzily matches to centre - left . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; centre - left } } ; 2 }
the average of the number of viewers record of all rows is 28.94 .
Output: round_eq { avg { all_rows ; number of viewers } ; 28.94 }
select the row whose catholic record of all rows is maximum . the region record of this row is southeast asia .
Output: eq { hop { argmax { all_rows ; catholic } ; region } ; southeast asia }
select the rows whose laps record is equal to 80 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; laps ; 80 } } ; 2 }
select the rows whose college record fuzzily matches to ateneo . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; college ; ateneo } } ; 2 }
select the row whose attendance record of all rows is maximum . the game site record of this row is waldstadion .
Output: eq { hop { argmax { all_rows ; attendance } ; game site } ; waldstadion }
select the rows whose try bp record is equal to 0 . there is only one such row in the table . the club record of this unqiue row is bedlinog rfc .
Output: and { only { filter_eq { all_rows ; try bp ; 0 } } ; eq { hop { filter_eq { all_rows ; try bp ; 0 } ; club } ; bedlinog rfc } }
select the row whose seats ( acs ) record of all rows is 1st maximum . the state record of this row is madhya pradesh .
Output: eq { hop { nth_argmax { all_rows ; seats ( acs ) ; 1 } ; state } ; madhya pradesh }
the sum of the national titles record of all rows is 79 .
Output: round_eq { sum { all_rows ; national titles } ; 79 }
select the rows whose team record fuzzily matches to ulrich . select the row whose year record of these rows is maximum . the start record of this row is 33 .
Output: eq { hop { argmax { filter_eq { all_rows ; team ; ulrich } ; year } ; start } ; 33 }
select the row whose first elected record of all rows is minimum . the incumbent record of this row is percy e quin .
Output: eq { hop { argmin { all_rows ; first elected } ; incumbent } ; percy e quin }
select the row whose roll record of all rows is 2nd maximum . the name record of this row is albany school .
Output: eq { hop { nth_argmax { all_rows ; roll ; 2 } ; name } ; albany school }
select the rows whose street address record fuzzily matches to 424 church street . take the floors record of this row . select the rows whose street address record fuzzily matches to 211 union street . take the floors record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; street address ; 424 church street } ; floors } ; hop { filter_eq { all_rows ; street address ; 211 union street } ; floors } }
select the rows whose winning driver record fuzzily matches to raphael matos . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; winning driver ; raphael matos } } ; 1 }
the average of the pick record of all rows is 114 .
Output: round_eq { avg { all_rows ; pick } ; 114 }
for the quantity preserved records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; quantity preserved ; 0 }
the average of the score record of all rows is 3.625 .
Output: round_eq { avg { all_rows ; score } ; 3.625 }
select the row whose ratings record of all rows is maximum . the guest ( s ) record of this row is matt lucas .
Output: eq { hop { argmax { all_rows ; ratings } ; guest ( s ) } ; matt lucas }
select the rows whose station record fuzzily matches to kstp - tv . there is only one such row in the table . the channel record of this unqiue row is 5.1 .
Output: and { only { filter_eq { all_rows ; station ; kstp - tv } } ; eq { hop { filter_eq { all_rows ; station ; kstp - tv } ; channel } ; 5.1 } }
the average of the cuts made record of all rows is 4 .
Output: round_eq { avg { all_rows ; cuts made } ; 4 }
for the director records of all rows , most of them fuzzily match to paul gay .
Output: most_eq { all_rows ; director ; paul gay }
select the rows whose constructor record fuzzily matches to toyota . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; constructor ; toyota } } ; 2 }
select the rows whose name ( year commissioned ) record fuzzily matches to goldfields gas transmission pipeline ( 1996 ) . take the maximum diameter record of this row . select the rows whose name ( year commissioned ) record fuzzily matches to mid west gas pipeline ( 1999 ) . take the maximum diameter record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ( year commissioned ) ; goldfields gas transmission pipeline ( 1996 ) } ; maximum diameter } ; hop { filter_eq { all_rows ; name ( year commissioned ) ; mid west gas pipeline ( 1999 ) } ; maximum diameter } }
the average of the round record of all rows is 2 .
Output: round_eq { avg { all_rows ; round } ; 2 }
select the rows whose location attendance record fuzzily matches to united center . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location attendance ; united center } } ; 3 }
select the rows whose place record fuzzily matches to austria . select the row whose score record of these rows is 1st maximum . the shooter record of this row is uwe potteck ( gdr ) .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; place ; austria } ; score ; 1 } ; shooter } ; uwe potteck ( gdr ) }
for the date records of all rows , all of them fuzzily match to march .
Output: all_eq { all_rows ; date ; march }
the maximum result record of all rows is 4 - 2 . the venue record of the row with superlative result record is estadio monumental , santiago , chile .
Output: and { eq { max { all_rows ; result } ; 4 - 2 } ; eq { hop { argmax { all_rows ; result } ; venue } ; estadio monumental , santiago , chile } }
for the played records of all rows , all of them are equal to 22 .
Output: all_eq { all_rows ; played ; 22 }
for the age records of all rows , most of them are less than 27 .
Output: most_less { all_rows ; age ; 27 }
select the rows whose operating systems record fuzzily matches to linux . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; operating systems ; linux } } ; 3 }
select the row whose length record of all rows is maximum . the version record of this row is music video .
Output: eq { hop { argmax { all_rows ; length } ; version } ; music video }
select the rows whose matches record is equal to 16 . the maximum wins record of these rows is 11 . the year record of the row with superlative wins record is 2011 .
Output: and { eq { max { filter_eq { all_rows ; matches ; 16 } ; wins } ; 11 } ; eq { hop { argmax { filter_eq { all_rows ; matches ; 16 } ; wins } ; year } ; 2011 } }
select the rows whose event record fuzzily matches to ept baden classic . take the prize record of this row . select the rows whose event record fuzzily matches to the european poker championships . take the prize record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; event ; ept baden classic } ; prize } ; hop { filter_eq { all_rows ; event ; the european poker championships } ; prize } }
select the rows whose date of successors formal installation record fuzzily matches to not filled this term . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date of successors formal installation ; not filled this term } } ; 2 }
the average of the number of hurricanes record of all rows is 4.3 .
Output: round_eq { avg { all_rows ; number of hurricanes } ; 4.3 }
the 1st minimum date record of all rows is may 31 .
Output: eq { nth_min { all_rows ; date ; 1 } ; may 31 }
for the batting style records of all rows , most of them fuzzily match to right-handed .
Output: most_eq { all_rows ; batting style ; right-handed }
for the partner records of all rows , most of them fuzzily match to app .
Output: most_eq { all_rows ; partner ; app }
select the rows whose week record is greater than or equal to 13 . the sum of the attendance record of these rows is 95326 .
Output: round_eq { sum { filter_greater_eq { all_rows ; week ; 13 } ; attendance } ; 95326 }
select the rows whose swimsuit record fuzzily matches to 8.432 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; swimsuit ; 8.432 } } ; 2 }
for the status at production records of all rows , all of them fuzzily match to under construction .
Output: all_eq { all_rows ; status at production ; under construction }
select the row whose enrollment record of all rows is maximum . the institution record of this row is queens college .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; queens college }
select the rows whose venue record fuzzily matches to princes park . there is only one such row in the table . the home team record of this unqiue row is carlton . the away team record of this unqiue row is geelong .
Output: and { only { filter_eq { all_rows ; venue ; princes park } } ; and { eq { hop { filter_eq { all_rows ; venue ; princes park } ; home team } ; carlton } ; eq { hop { filter_eq { all_rows ; venue ; princes park } ; away team } ; geelong } } }
for the score records of all rows , most of them fuzzily match to l .
Output: most_eq { all_rows ; score ; l }
select the rows whose incumbent record fuzzily matches to vacant . there is only one such row in the table .
Output: only { filter_eq { all_rows ; incumbent ; vacant } }
select the row whose ratings record of all rows is maximum . the translation of title record of this row is english showdown ! fight for stupid 6 .
Output: eq { hop { argmax { all_rows ; ratings } ; translation of title } ; english showdown ! fight for stupid 6 }
select the rows whose snatch record is greater than 170.0 . there is only one such row in the table . the name record of this unqiue row is joël mackenzie ( cub ) .
Output: and { only { filter_greater { all_rows ; snatch ; 170.0 } } ; eq { hop { filter_greater { all_rows ; snatch ; 170.0 } ; name } ; joël mackenzie ( cub ) } }
select the row whose difference record of all rows is 2nd maximum . the team record of this row is palmeiras .
Output: eq { hop { nth_argmax { all_rows ; difference ; 2 } ; team } ; palmeiras }
select the rows whose place record fuzzily matches to t8 . the sum of the money record of these rows is $ 379,000 .
Output: round_eq { sum { filter_eq { all_rows ; place ; t8 } ; money } ; $ 379,000 }
for the country records of all rows , most of them fuzzily match to tunisia .
Output: most_eq { all_rows ; country ; tunisia }
select the row whose us air date record of all rows is 3rd minimum . the us air date record of this row is july 3 , 1993 .
Output: eq { hop { nth_argmin { all_rows ; us air date ; 3 } ; us air date } ; july 3 , 1993 }
select the row whose points record of all rows is maximum . the series record of this row is toyota racing series .
Output: eq { hop { argmax { all_rows ; points } ; series } ; toyota racing series }
the sum of the laps record of all rows is 3670 .
Output: round_eq { sum { all_rows ; laps } ; 3670 }
select the rows whose area ( km 2 ) record is less than 50 . select the row whose population record of these rows is 1st maximum . the common of record of this row is moncalieri .
Output: eq { hop { nth_argmax { filter_less { all_rows ; area ( km 2 ) ; 50 } ; population ; 1 } ; common of } ; moncalieri }
select the rows whose format record fuzzily matches to digital download . there is only one such row in the table . the record company record of this unqiue row is deutsche grammophon .
Output: and { only { filter_eq { all_rows ; format ; digital download } } ; eq { hop { filter_eq { all_rows ; format ; digital download } ; record company } ; deutsche grammophon } }
for the position records of all rows , most of them fuzzily match to wing .
Output: most_eq { all_rows ; position ; wing }
select the rows whose opponent record fuzzily matches to petr kelner . take the round record of this row . select the rows whose opponent record fuzzily matches to andrej bregar . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; petr kelner } ; round } ; hop { filter_eq { all_rows ; opponent ; andrej bregar } ; round } }
select the rows whose country record fuzzily matches to bosnia and herzegovina . there is only one such row in the table . the film record of this unqiue row is prva plata .
Output: and { only { filter_eq { all_rows ; country ; bosnia and herzegovina } } ; eq { hop { filter_eq { all_rows ; country ; bosnia and herzegovina } ; film } ; prva plata } }
select the rows whose player record fuzzily matches to lee janzen . take the score record of this row . select the rows whose player record fuzzily matches to chris perry . take the score record of this row . the second record is 1 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; lee janzen } ; score } ; hop { filter_eq { all_rows ; player ; chris perry } ; score } } ; -1 }
select the rows whose date record fuzzily matches to november . for the result records of these rows , most of them fuzzily match to l .
Output: most_eq { filter_eq { all_rows ; date ; november } ; result ; l }
select the row whose run time record of all rows is maximum . the episode number record of this row is 313 .
Output: eq { hop { argmax { all_rows ; run time } ; episode number } ; 313 }
for the gender records of all rows , all of them fuzzily match to coed .
Output: all_eq { all_rows ; gender ; coed }
select the rows whose type record fuzzily matches to tribal . there is only one such row in the table . the type record of this unqiue row is tribal .
Output: and { only { filter_eq { all_rows ; type ; tribal } } ; eq { hop { filter_eq { all_rows ; type ; tribal } ; type } ; tribal } }
select the rows whose driver record fuzzily matches to mark webber . take the time / retired record of this row . select the rows whose driver record fuzzily matches to jarno trulli . take the time / retired record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; driver ; mark webber } ; time / retired } ; hop { filter_eq { all_rows ; driver ; jarno trulli } ; time / retired } }
select the row whose area ( km 2 ) record of all rows is maximum . the county record of this row is tana river .
Output: eq { hop { argmax { all_rows ; area ( km 2 ) } ; county } ; tana river }
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 .
Output: eq { hop { nth_argmax { all_rows ; number of episodes ; 2 } ; english title ( chinese title ) } ; burning flame 烈火雄心 }
select the row whose overall record record of all rows is maximum . the texas vs record of this row is texas a & m .
Output: eq { hop { argmax { all_rows ; overall record } ; texas vs } ; texas a & m }
the average of the wickets record of all rows is 33 .
Output: round_eq { avg { all_rows ; wickets } ; 33 }
select the rows whose first elected record is equal to 1944 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; first elected ; 1944 } } ; 2 }
select the rows whose score record fuzzily matches to 2 - 2 . there is only one such row in the table . the date record of this unqiue row is 8 october 2007 . the result record of this unqiue row is draw .
Output: and { only { filter_eq { all_rows ; score ; 2 - 2 } } ; and { eq { hop { filter_eq { all_rows ; score ; 2 - 2 } ; date } ; 8 october 2007 } ; eq { hop { filter_eq { all_rows ; score ; 2 - 2 } ; result } ; draw } } }
for the 1st ship delivery date records of all rows , most of them fuzzily match to may 1943 .
Output: most_eq { all_rows ; 1st ship delivery date ; may 1943 }
select the rows whose original air date record is less than january 1 , 2010 . the average of the us viewers ( million ) record of these rows is 9.74 .
Output: round_eq { avg { filter_less { all_rows ; original air date ; january 1 , 2010 } ; us viewers ( million ) } ; 9.74 }
select the row whose windows record of all rows is 2nd maximum . the source record of this row is international data corporation .
Output: eq { hop { nth_argmax { all_rows ; windows ; 2 } ; source } ; international data corporation }
for the leading scorer records of all rows , most of them fuzzily match to lebron james .
Output: most_eq { all_rows ; leading scorer ; lebron james }
for the time / retired records of all rows , most of them are greater than or equal to 2 .
Output: most_greater_eq { all_rows ; time / retired ; 2 }
for the year records of all rows , all of them are greater than 1970 .
Output: all_greater { all_rows ; year ; 1970 }
select the rows whose us acres episode record fuzzily matches to daydream doctor . take the original airdate record of this row . select the rows whose us acres episode record fuzzily matches to deja vu . take the original airdate record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; us acres episode ; daydream doctor } ; original airdate } ; hop { filter_eq { all_rows ; us acres episode ; deja vu } ; original airdate } }
select the rows whose date record fuzzily matches to 6 september 2006 . take the score record of this row . select the rows whose date record fuzzily matches to 11 october 2006 . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 6 september 2006 } ; score } ; hop { filter_eq { all_rows ; date ; 11 october 2006 } ; score } }
the maximum days held record of all rows is 700 .
Output: eq { max { all_rows ; days held } ; 700 }
for the td 's records of all rows , most of them are greater than or equal to 1 .
Output: most_greater_eq { all_rows ; td 's ; 1 }
select the rows whose score record fuzzily matches to 0 - 0 . there is only one such row in the table . the home team record of this unqiue row is port vale . the away team record of this unqiue row is watford .
Output: and { only { filter_eq { all_rows ; score ; 0 - 0 } } ; and { eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; home team } ; port vale } ; eq { hop { filter_eq { all_rows ; score ; 0 - 0 } ; away team } ; watford } } }
for the 2011 records of all rows , most of them fuzzily match to a .
Output: most_eq { all_rows ; 2011 ; a }
select the rows whose location record fuzzily matches to the vertices of the core . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; the vertices of the core } } ; 2 }
select the row whose total record of all rows is maximum . the nation record of this row is west germany .
Output: eq { hop { argmax { all_rows ; total } ; nation } ; west germany }
the average of the pick record of all rows is 27.8 .
Output: round_eq { avg { all_rows ; pick } ; 27.8 }
select the row whose floors record of all rows is 3rd maximum . the street address record of this row is 1111 main street .
Output: eq { hop { nth_argmax { all_rows ; floors ; 3 } ; street address } ; 1111 main street }
select the row whose score record of all rows is maximum . the home team record of this row is melbourne tigers . the away team record of this row is wollongong hawks .
Output: and { eq { hop { argmax { all_rows ; score } ; home team } ; melbourne tigers } ; eq { hop { argmax { all_rows ; score } ; away team } ; wollongong hawks } }
select the row whose time record of all rows is minimum . the race record of this row is dance design ebf maiden .
Output: eq { hop { argmin { all_rows ; time } ; race } ; dance design ebf maiden }
select the rows whose outcome record fuzzily matches to winner . select the row whose date record of these rows is minimum . the tournament record of this row is granby , canada .
Output: eq { hop { argmin { filter_eq { all_rows ; outcome ; winner } ; date } ; tournament } ; granby , canada }
select the rows whose venue record fuzzily matches to melbourne . among these rows , select the rows whose goals record is greater than 0 . there is only one such row in the table . the date record of this unqiue row is 1956 - 11 - 27 .
Output: and { only { filter_greater { filter_eq { all_rows ; venue ; melbourne } ; goals ; 0 } } ; eq { hop { filter_greater { filter_eq { all_rows ; venue ; melbourne } ; goals ; 0 } ; date } ; 1956 - 11 - 27 } }
select the rows whose venue record fuzzily matches to mcg . there is only one such row in the table . the home team record of this unqiue row is richmond . the away team record of this unqiue row is collingwood .
Output: and { only { filter_eq { all_rows ; venue ; mcg } } ; and { eq { hop { filter_eq { all_rows ; venue ; mcg } ; home team } ; richmond } ; eq { hop { filter_eq { all_rows ; venue ; mcg } ; away team } ; collingwood } } }
select the rows whose edition record is equal to 2005 . among these rows , select the rows whose outcome record fuzzily matches to loss . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; edition ; 2005 } ; outcome ; loss } } ; 2 }
the average of the bush % record of all rows is 65-70 .
Output: round_eq { avg { all_rows ; bush % } ; 65-70 }
for the played records of all rows , all of them are equal to 30 .
Output: all_eq { all_rows ; played ; 30 }
the average of the impedance ( ohms ) record of all rows is 33 .
Output: round_eq { avg { all_rows ; impedance ( ohms ) } ; 33 }
for the date of vacancy records of all rows , most of them fuzzily match to march 2011 .
Output: most_eq { all_rows ; date of vacancy ; march 2011 }
select the row whose enrollment record of all rows is maximum . the location record of this row is mercer island .
Output: eq { hop { argmax { all_rows ; enrollment } ; location } ; mercer island }
select the rows whose year record fuzzily matches to sophomore . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; year ; sophomore } } ; 5 }
for the director records of all rows , most of them fuzzily match to rn bradbury .
Output: most_eq { all_rows ; director ; rn bradbury }
select the rows whose high rebounds record fuzzily matches to james booyer . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; high rebounds ; james booyer } } ; 2 }
the average of the total record of all rows is 3 .
Output: round_eq { avg { all_rows ; total } ; 3 }
select the rows whose college record fuzzily matches to arizona state . there is only one such row in the table . the player record of this unqiue row is tony lorick .
Output: and { only { filter_eq { all_rows ; college ; arizona state } } ; eq { hop { filter_eq { all_rows ; college ; arizona state } ; player } ; tony lorick } }
for the 2007 records of all rows , most of them fuzzily match to a .
Output: most_eq { all_rows ; 2007 ; a }
select the rows whose loser record fuzzily matches to iceland . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; loser ; iceland } } ; 1 }
select the rows whose year record is equal to 2000 . there is only one such row in the table . the name record of this unqiue row is anthony thomas .
Output: and { only { filter_eq { all_rows ; year ; 2000 } } ; eq { hop { filter_eq { all_rows ; year ; 2000 } ; name } ; anthony thomas } }
select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is jamie l whitten .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; jamie l whitten }
the average of the home team score record of all rows is 13.47 .
Output: round_eq { avg { all_rows ; home team score } ; 13.47 }
select the rows whose type record fuzzily matches to occupational . there is only one such row in the table . the surname record of this unqiue row is møller .
Output: and { only { filter_eq { all_rows ; type ; occupational } } ; eq { hop { filter_eq { all_rows ; type ; occupational } ; surname } ; møller } }
select the row whose date record of all rows is 1st minimum . the winning driver record of this row is eugenio silvani .
Output: eq { hop { nth_argmin { all_rows ; date ; 1 } ; winning driver } ; eugenio silvani }
select the row whose week record of all rows is 1st minimum . the opponent record of this row is detroit lions .
Output: eq { hop { nth_argmin { all_rows ; week ; 1 } ; opponent } ; detroit lions }
the average of the attendance record of all rows is 54939 .
Output: round_eq { avg { all_rows ; attendance } ; 54939 }
select the rows whose reason for termination record fuzzily matches to not confirmed . there is only one such row in the table . the judge record of this unqiue row is clinton woodbury howard .
Output: and { only { filter_eq { all_rows ; reason for termination ; not confirmed } } ; eq { hop { filter_eq { all_rows ; reason for termination ; not confirmed } ; judge } ; clinton woodbury howard } }
select the rows whose area ( km square ) record is less than 100 . among these rows , select the rows whose population 2000 census record is less than 100000 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_less { all_rows ; area ( km square ) ; 100 } ; population 2000 census ; 100000 } } ; 2 }
select the row whose points record of all rows is 2nd maximum . the year record of this row is 1970 .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; year } ; 1970 }
select the rows whose opponent record fuzzily matches to carolina panthers . take the date record of this row . select the rows whose opponent record fuzzily matches to atlanta falcons . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; carolina panthers } ; date } ; hop { filter_eq { all_rows ; opponent ; atlanta falcons } ; date } }
select the row whose us viewers ( million ) record of all rows is 2nd maximum . the no in season record of this row is 3 .
Output: eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; no in season } ; 3 }
select the rows whose team record fuzzily matches to portland . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; team ; portland } } ; 2 }
select the row whose time record of all rows is 3rd minimum . the rider record of this row is ross johnson .
Output: eq { hop { nth_argmin { all_rows ; time ; 3 } ; rider } ; ross johnson }
select the row whose agg record of all rows is maximum . the team 1 record of this row is pyunik .
Output: eq { hop { argmax { all_rows ; agg } ; team 1 } ; pyunik }
the average of the points record of all rows is 2.7 .
Output: round_eq { avg { all_rows ; points } ; 2.7 }
select the rows whose score record fuzzily matches to l . select the row whose date record of these rows is 3rd minimum . the attendance record of this row is 11741 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; score ; l } ; date ; 3 } ; attendance } ; 11741 }
the average of the apparent magnitude record of all rows is 12.24 .
Output: round_eq { avg { all_rows ; apparent magnitude } ; 12.24 }
select the row whose home team score record of all rows is maximum . the away team score record of this row is 13.14 ( 92 ) .
Output: eq { hop { argmax { all_rows ; home team score } ; away team score } ; 13.14 ( 92 ) }
for the package / option records of all rows , most of them fuzzily match to no ( fta ) .
Output: most_eq { all_rows ; package / option ; no ( fta ) }
select the rows whose first premiere record fuzzily matches to 2009 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; first premiere ; 2009 } } ; 3 }
select the row whose enrollment 08 - 09 record of all rows is 2nd maximum . the school record of this row is heritage .
Output: eq { hop { nth_argmax { all_rows ; enrollment 08 - 09 ; 2 } ; school } ; heritage }
select the rows whose player record fuzzily matches to mitchell duke . take the goals record of this row . select the rows whose player record fuzzily matches to john hutchinson . take the goals record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; mitchell duke } ; goals } ; hop { filter_eq { all_rows ; player ; john hutchinson } ; goals } }
select the rows whose home team score record is greater than 15.0 . among these rows , select the rows whose crowd record is less than 20,000 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_greater { all_rows ; home team score ; 15.0 } ; crowd ; 20,000 } } ; 2 }
select the rows whose partner record fuzzily matches to tatiana poutchek . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; partner ; tatiana poutchek } } ; 2 }
the average of the time record of all rows is 3:29 .
Output: round_eq { avg { all_rows ; time } ; 3:29 }
select the rows whose fuel propulsion record fuzzily matches to diesel - electric hybrid . there is only one such row in the table . the make and model record of this unqiue row is new flyer de35lf .
Output: and { only { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } } ; eq { hop { filter_eq { all_rows ; fuel propulsion ; diesel - electric hybrid } ; make and model } ; new flyer de35lf } }
select the row whose total record of all rows is maximum . the gold record of this row is 3 .
Output: eq { hop { argmax { all_rows ; total } ; gold } ; 3 }
select the rows whose name record fuzzily matches to simon ammann . take the 1st ( m ) record of this row . select the rows whose name record fuzzily matches to thomas morgenstern . take the 1st ( m ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; simon ammann } ; 1st ( m ) } ; hop { filter_eq { all_rows ; name ; thomas morgenstern } ; 1st ( m ) } }
select the rows whose club record fuzzily matches to mold rfc . take the losing bonus record of this row . select the rows whose club record fuzzily matches to ruthin rfc . take the losing bonus record of this row . the second record is 1 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; club ; mold rfc } ; losing bonus } ; hop { filter_eq { all_rows ; club ; ruthin rfc } ; losing bonus } } ; -1 }
select the row whose wins record of all rows is maximum . the club record of this row is ud salamanca .
Output: eq { hop { argmax { all_rows ; wins } ; club } ; ud salamanca }
select the row whose became queen record of all rows is 2nd minimum . the name record of this row is maria teresa of the two sicilies .
Output: eq { hop { nth_argmin { all_rows ; became queen ; 2 } ; name } ; maria teresa of the two sicilies }
select the rows whose college record fuzzily matches to san diego state . there is only one such row in the table . the name record of this unqiue row is freddie keiaho .
Output: and { only { filter_eq { all_rows ; college ; san diego state } } ; eq { hop { filter_eq { all_rows ; college ; san diego state } ; name } ; freddie keiaho } }
select the rows whose team record fuzzily matches to switzerland . take the points record of this row . select the rows whose team record fuzzily matches to germany . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; switzerland } ; points } ; hop { filter_eq { all_rows ; team ; germany } ; points } }
select the rows whose date record fuzzily matches to november . for the attendance records of these rows , most of them are greater than or equal to 10000 .
Output: most_greater_eq { filter_eq { all_rows ; date ; november } ; attendance ; 10000 }