niryuu/sni-each-converted huggingface dataset:
the average of the lost record of all rows is 10.75 .
Output: round_eq { avg { all_rows ; lost } ; 10.75 }
the average of the blank ends record of all rows is 6.9 .
Output: round_eq { avg { all_rows ; blank ends } ; 6.9 }
for the bandwidth included records of all rows , all of them fuzzily match to unlimited mb .
Output: all_eq { all_rows ; bandwidth included ; unlimited mb }
select the row whose runs record of all rows is 2nd maximum . the player record of this row is dene hills .
Output: eq { hop { nth_argmax { all_rows ; runs ; 2 } ; player } ; dene hills }
select the rows whose population ( 2008 ) record is less than 100000 . among these rows , select the rows whose created record is equal to 2000 . there is only one such row in the table . the county record of this unqiue row is river gee .
Output: and { only { filter_eq { filter_less { all_rows ; population ( 2008 ) ; 100000 } ; created ; 2000 } } ; eq { hop { filter_eq { filter_less { all_rows ; population ( 2008 ) ; 100000 } ; created ; 2000 } ; county } ; river gee } }
select the rows whose mens singles record fuzzily matches to alan budi kusuma . take the year record of this row . select the rows whose mens singles record fuzzily matches to hermawan susanto . take the year record of this row . the second record is 1 year larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; mens singles ; alan budi kusuma } ; year } ; hop { filter_eq { all_rows ; mens singles ; hermawan susanto } ; year } } ; -1 year }
select the rows whose opponent record fuzzily matches to darren cahill . there is only one such row in the table . the championship record of this unqiue row is gstaad , switzerland .
Output: and { only { filter_eq { all_rows ; opponent ; darren cahill } } ; eq { hop { filter_eq { all_rows ; opponent ; darren cahill } ; championship } ; gstaad , switzerland } }
select the rows whose result record fuzzily matches to loss . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; loss } } ; 2 }
select the rows whose main date record fuzzily matches to may . there is only one such row in the table . the round record of this unqiue row is final .
Output: and { only { filter_eq { all_rows ; main date ; may } } ; eq { hop { filter_eq { all_rows ; main date ; may } ; round } ; final } }
select the row whose date record of all rows is maximum . the opponent record of this row is osvaldo castuera .
Output: eq { hop { argmax { all_rows ; date } ; opponent } ; osvaldo castuera }
select the rows whose relationship record fuzzily matches to wife . there is only one such row in the table . the name record of this unqiue row is eva braun .
Output: and { only { filter_eq { all_rows ; relationship ; wife } } ; eq { hop { filter_eq { all_rows ; relationship ; wife } ; name } ; eva braun } }
select the rows whose score record fuzzily matches to ot . select the row whose january record of these rows is 3rd minimum . the opponent record of this row is st louis blues .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; score ; ot } ; january ; 3 } ; opponent } ; st louis blues }
the sum of the population record of all rows is 17660 .
Output: round_eq { sum { all_rows ; population } ; 17660 }
the average of the episodes record of all rows is 10.73 .
Output: round_eq { avg { all_rows ; episodes } ; 10.73 }
the 3rd minimum round record of all rows is 3 . the circuit record of the row with 3rd minimum round record is wakefield park .
Output: and { eq { nth_min { all_rows ; round ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; round ; 3 } ; circuit } ; wakefield park } }
select the row whose year record of all rows is 2nd minimum . the nomination record of this row is jam hsiao .
Output: eq { hop { nth_argmin { all_rows ; year ; 2 } ; nomination } ; jam hsiao }
select the rows whose syndicate record fuzzily matches to america 3 foundation . among these rows , select the rows whose yacht record fuzzily matches to kanza . there is only one such row in the table . the sail record of this unqiue row is usa - 28 .
Output: and { only { filter_eq { filter_eq { all_rows ; syndicate ; america 3 foundation } ; yacht ; kanza } } ; eq { hop { filter_eq { filter_eq { all_rows ; syndicate ; america 3 foundation } ; yacht ; kanza } ; sail } ; usa - 28 } }
select the rows whose team record fuzzily matches to bourgoin . take the tries for record of this row . select the rows whose team record fuzzily matches to london irish . take the tries for record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; bourgoin } ; tries for } ; hop { filter_eq { all_rows ; team ; london irish } ; tries for } }
select the rows whose laps record is equal to 1 . there is only one such row in the table . the year record of this unqiue row is 1964 . the finish record of this unqiue row is 30 .
Output: and { only { filter_eq { all_rows ; laps ; 1 } } ; and { eq { hop { filter_eq { all_rows ; laps ; 1 } ; year } ; 1964 } ; eq { hop { filter_eq { all_rows ; laps ; 1 } ; finish } ; 30 } } }
select the row whose frequency mhz record of all rows is 3rd maximum . the erp kw record of this row is 3 .
Output: eq { hop { nth_argmax { all_rows ; frequency mhz ; 3 } ; erp kw } ; 3 }
the 1st minimum rank record of all rows is 1 . the player record of the row with 1st minimum rank record is bob charles .
Output: and { eq { nth_min { all_rows ; rank ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; rank ; 1 } ; player } ; bob charles } }
select the row whose points for record of all rows is maximum . the club record of this row is betws rfc .
Output: eq { hop { argmax { all_rows ; points for } ; club } ; betws rfc }
select the rows whose notes record fuzzily matches to fa . there is only one such row in the table . the country record of this unqiue row is new zealand .
Output: and { only { filter_eq { all_rows ; notes ; fa } } ; eq { hop { filter_eq { all_rows ; notes ; fa } ; country } ; new zealand } }
select the rows whose partner record fuzzily matches to simon aspelin . the number of such rows is 11 .
Output: eq { count { filter_eq { all_rows ; partner ; simon aspelin } } ; 11 }
select the rows whose venue record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; venue } } ; 5 }
select the rows whose 2nd leg record fuzzily matches to 4 . there is only one such row in the table . the team 1 record of this unqiue row is rabotnički . the team 2 record of this unqiue row is ilinden .
Output: and { only { filter_eq { all_rows ; 2nd leg ; 4 } } ; and { eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 1 } ; rabotnički } ; eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 2 } ; ilinden } } }
select the rows whose score record fuzzily matches to w . for the high points records of these rows , most of them fuzzily match to tim duncan .
Output: most_eq { filter_eq { all_rows ; score ; w } ; high points ; tim duncan }
select the rows whose opponent record fuzzily matches to new york giants . the sum of the attendance record of these rows is 144706 .
Output: round_eq { sum { filter_eq { all_rows ; opponent ; new york giants } ; attendance } ; 144706 }
for the to par records of all rows , most of them fuzzily match to + .
Output: most_eq { all_rows ; to par ; + }
select the rows whose year record is greater than 2005 . there is only one such row in the table . the version record of this unqiue row is live version ( recorded in 2006 ) .
Output: and { only { filter_greater { all_rows ; year ; 2005 } } ; eq { hop { filter_greater { all_rows ; year ; 2005 } ; version } ; live version ( recorded in 2006 ) } }
select the rows whose player record fuzzily matches to linden rowat ( g ) . take the round record of this row . select the rows whose player record fuzzily matches to dwight king ( c ) . take the round record of this row . the first record is 1 larger than the second record . the round record of the first row is 5 . the round record of the second row is 4 .
Output: and { eq { diff { hop { filter_eq { all_rows ; player ; linden rowat ( g ) } ; round } ; hop { filter_eq { all_rows ; player ; dwight king ( c ) } ; round } } ; 1 } ; and { eq { hop { filter_eq { all_rows ; player ; linden rowat ( g ) } ; round } ; 5 } ; eq { hop { filter_eq { all_rows ; player ; dwight king ( c ) } ; round } ; 4 } } }
select the rows whose name record fuzzily matches to gary neville . take the total record of this row . select the rows whose name record fuzzily matches to alex stepney . take the total record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; gary neville } ; total } ; hop { filter_eq { all_rows ; name ; alex stepney } ; total } }
select the rows whose goalkeeper record fuzzily matches to igor etxebarrieta . take the matches record of this row . select the rows whose goalkeeper record fuzzily matches to daniel giménez . take the matches record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; goalkeeper ; igor etxebarrieta } ; matches } ; hop { filter_eq { all_rows ; goalkeeper ; daniel giménez } ; matches } }
select the rows whose date record fuzzily matches to 14 july 1979 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; date ; 14 july 1979 } } ; 5 }
select the rows whose opponent record fuzzily matches to san francisco 49ers . the maximum attendance record of these rows is 65551 . the date record of the row with superlative attendance record is november 11 , 1979 .
Output: and { eq { max { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } ; 65551 } ; eq { hop { argmax { filter_eq { all_rows ; opponent ; san francisco 49ers } ; attendance } ; date } ; november 11 , 1979 } }
select the rows whose no in season record fuzzily matches to 1 / 2 . take the us viewers ( million ) record of this row . select the rows whose no in season record fuzzily matches to 16 . take the us viewers ( million ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; no in season ; 1 / 2 } ; us viewers ( million ) } ; hop { filter_eq { all_rows ; no in season ; 16 } ; us viewers ( million ) } }
select the rows whose tournament record fuzzily matches to world championships . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; tournament ; world championships } } ; 2 }
select the rows whose tournament record fuzzily matches to us open . take the events record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the events record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tournament ; us open } ; events } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; events } }
select the rows whose format record fuzzily matches to oldies . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; format ; oldies } } ; 4 }
select the rows whose college record fuzzily matches to weber state . there is only one such row in the table . the player record of this unqiue row is lee white .
Output: and { only { filter_eq { all_rows ; college ; weber state } } ; eq { hop { filter_eq { all_rows ; college ; weber state } ; player } ; lee white } }
select the rows whose no of barangays record is equal to 11 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; no of barangays ; 11 } } ; 2 }
select the rows whose screens record is greater than 100 . there is only one such row in the table . the territory record of this unqiue row is turkey .
Output: and { only { filter_greater { all_rows ; screens ; 100 } } ; eq { hop { filter_greater { all_rows ; screens ; 100 } ; territory } ; turkey } }
for the tv time records of all rows , most of them fuzzily match to cbs .
Output: most_eq { all_rows ; tv time ; cbs }
select the rows whose best score record fuzzily matches to 30 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; best score ; 30 } } ; 7 }
select the rows whose dance styles record fuzzily matches to latin dances . there is only one such row in the table . the competing dancers record of this unqiue row is jason roditis & tonia kosovich .
Output: and { only { filter_eq { all_rows ; dance styles ; latin dances } } ; eq { hop { filter_eq { all_rows ; dance styles ; latin dances } ; competing dancers } ; jason roditis & tonia kosovich } }
select the rows whose date record fuzzily matches to 16 august 2000 . take the competition record of this row . select the rows whose date record fuzzily matches to 22 august 2007 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is friendly . the competition record of the second row is friendly .
Output: and { eq { hop { filter_eq { all_rows ; date ; 16 august 2000 } ; competition } ; hop { filter_eq { all_rows ; date ; 22 august 2007 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 16 august 2000 } ; competition } ; friendly } ; eq { hop { filter_eq { all_rows ; date ; 22 august 2007 } ; competition } ; friendly } } }
the average of the population ( 2011 ) record of all rows is 2262 .
Output: round_eq { avg { all_rows ; population ( 2011 ) } ; 2262 }
the average of the score record of all rows is 34.8 .
Output: round_eq { avg { all_rows ; score } ; 34.8 }
select the rows whose location attendance record fuzzily matches to pepsi center . for the location attendance records of these rows , most of them are less than 19000 .
Output: most_less { filter_eq { all_rows ; location attendance ; pepsi center } ; location attendance ; 19000 }
the sum of the premierships record of all rows is 30 .
Output: round_eq { sum { all_rows ; premierships } ; 30 }
select the rows whose mens singles record fuzzily matches to dean galt . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; mens singles ; dean galt } } ; 2 }
select the rows whose type record fuzzily matches to tugboat . take the gross tonnage record of this row . select the rows whose type record fuzzily matches to deck cargo barge . take the gross tonnage record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; type ; tugboat } ; gross tonnage } ; hop { filter_eq { all_rows ; type ; deck cargo barge } ; gross tonnage } }
select the row whose crowd record of all rows is 1st maximum . the date record of this row is 12 august 2007 .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; date } ; 12 august 2007 }
select the rows whose date record fuzzily matches to august 18 . take the record record of this row . select the rows whose date record fuzzily matches to august 17 . take the record record of this row . the first record is greater than the second record . the opponent record of the first row is royals . the opponent record of the second row is royals .
Output: and { greater { hop { filter_eq { all_rows ; date ; august 18 } ; record } ; hop { filter_eq { all_rows ; date ; august 17 } ; record } } ; and { eq { hop { filter_eq { all_rows ; date ; august 18 } ; opponent } ; royals } ; eq { hop { filter_eq { all_rows ; date ; august 17 } ; opponent } ; royals } } }
select the row whose enrollment record of all rows is maximum . the institution record of this row is rochester institute of technology .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; rochester institute of technology }
select the row whose gdp per capita ( us ) record of all rows is 2nd minimum . the member countries record of this row is poland .
Output: eq { hop { nth_argmin { all_rows ; gdp per capita ( us ) ; 2 } ; member countries } ; poland }
select the rows whose sport record fuzzily matches to w gymnastics . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; sport ; w gymnastics } } ; 2 }
the average of the division record record of all rows is 3.14 .
Output: round_eq { avg { all_rows ; division record } ; 3.14 }
the sum of the bronze record of all rows is 53 .
Output: round_eq { sum { all_rows ; bronze } ; 53 }
select the row whose wins record of all rows is 2nd maximum . the team record of this row is cerro porteño .
Output: eq { hop { nth_argmax { all_rows ; wins ; 2 } ; team } ; cerro porteño }
select the rows whose away team score record is less than 10.0 . the number of such rows is 2 .
Output: eq { count { filter_less { all_rows ; away team score ; 10.0 } } ; 2 }
select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is portage .
Output: eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; portage }
the average of the crowd record of all rows is 14924 .
Output: round_eq { avg { all_rows ; crowd } ; 14924 }
the average of the viewers record of all rows is 7.42 .
Output: round_eq { avg { all_rows ; viewers } ; 7.42 }
the average of the attendance record of all rows is 48121 .
Output: round_eq { avg { all_rows ; attendance } ; 48121 }
select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is colombia .
Output: eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; colombia }
select the rows whose us viewers ( million ) record is greater than 11.0 . there is only one such row in the table . the no record of this unqiue row is 1 . the title record of this unqiue row is two of a kind .
Output: and { only { filter_greater { all_rows ; us viewers ( million ) ; 11.0 } } ; and { eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 11.0 } ; no } ; 1 } ; eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 11.0 } ; title } ; two of a kind } } }
select the row whose us viewers ( million ) record of all rows is minimum . the title record of this row is the lorelais ' first day at yale .
Output: eq { hop { argmin { all_rows ; us viewers ( million ) } ; title } ; the lorelais ' first day at yale }
select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . the number of such rows is 1 .
Output: eq { count { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } } ; 1 }
select the row whose tied record of all rows is maximum . the season record of this row is 1968 - 69 .
Output: eq { hop { argmax { all_rows ; tied } ; season } ; 1968 - 69 }
select the rows whose country of origin record fuzzily matches to united states . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; country of origin ; united states } } ; 2 }
select the rows whose date of birth record is equal to 1968 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date of birth ; 1968 } } ; 2 }
select the row whose market value ( billion ) record of all rows is 1st maximum . the company record of this row is general electric .
Output: eq { hop { nth_argmax { all_rows ; market value ( billion ) ; 1 } ; company } ; general electric }
select the row whose points record of all rows is maximum . the opponent record of this row is vancouver canucks .
Output: eq { hop { argmax { all_rows ; points } ; opponent } ; vancouver canucks }
select the rows whose sunlight hours record is equal to 2043 . there is only one such row in the table . the city / town record of this unqiue row is ourense .
Output: and { only { filter_eq { all_rows ; sunlight hours ; 2043 } } ; eq { hop { filter_eq { all_rows ; sunlight hours ; 2043 } ; city / town } ; ourense } }
select the rows whose college record fuzzily matches to central florida . there is only one such row in the table . the name record of this unqiue row is paul miranda .
Output: and { only { filter_eq { all_rows ; college ; central florida } } ; eq { hop { filter_eq { all_rows ; college ; central florida } ; name } ; paul miranda } }
select the rows whose goals record is equal to 7 . there is only one such row in the table . the scorer record of this unqiue row is lee kil - yong .
Output: and { only { filter_eq { all_rows ; goals ; 7 } } ; eq { hop { filter_eq { all_rows ; goals ; 7 } ; scorer } ; lee kil - yong } }
select the row whose crowd record of all rows is maximum . the home team record of this row is melbourne . the away team record of this row is st kilda .
Output: and { eq { hop { argmax { all_rows ; crowd } ; home team } ; melbourne } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; st kilda } }
select the row whose capacity record of all rows is maximum . the venue record of this row is dinamo , minsk .
Output: eq { hop { argmax { all_rows ; capacity } ; venue } ; dinamo , minsk }
select the row whose points record of all rows is 2nd minimum . the driver record of this row is sandor van es .
Output: eq { hop { nth_argmin { all_rows ; points ; 2 } ; driver } ; sandor van es }
for the team name records of all rows , most of them fuzzily match to kemba racing .
Output: most_eq { all_rows ; team name ; kemba racing }
select the rows whose builder record fuzzily matches to peterson shipbuilders . the number of such rows is 11 .
Output: eq { count { filter_eq { all_rows ; builder ; peterson shipbuilders } } ; 11 }
select the rows whose name record fuzzily matches to margravine johanna of baden - baden . take the became duchess record of this row . select the rows whose name record fuzzily matches to victoria of saxe - coburg and gotha . take the became duchess record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; margravine johanna of baden - baden } ; became duchess } ; hop { filter_eq { all_rows ; name ; victoria of saxe - coburg and gotha } ; became duchess } }
the average of the speed record of all rows is 87.75 .
Output: round_eq { avg { all_rows ; speed } ; 87.75 }
select the row whose year of intro record of all rows is 2nd minimum . the name / designation record of this row is onorati smg .
Output: eq { hop { nth_argmin { all_rows ; year of intro ; 2 } ; name / designation } ; onorati smg }
the average of the goals record of all rows is 82.71 .
Output: round_eq { avg { all_rows ; goals } ; 82.71 }
select the rows whose 2009 record fuzzily matches to sf . there is only one such row in the table . the tournament record of this unqiue row is us open .
Output: and { only { filter_eq { all_rows ; 2009 ; sf } } ; eq { hop { filter_eq { all_rows ; 2009 ; sf } ; tournament } ; us open } }
select the rows whose visual arts record fuzzily matches to sharon hayes . there is only one such row in the table . the year record of this unqiue row is 2013 .
Output: and { only { filter_eq { all_rows ; visual arts ; sharon hayes } } ; eq { hop { filter_eq { all_rows ; visual arts ; sharon hayes } ; year } ; 2013 } }
select the rows whose 9:00 record fuzzily matches to the office . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; 9:00 ; the office } } ; 2 }
select the rows whose score record is equal to 69 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; score ; 69 } } ; 6 }
select the rows whose building record fuzzily matches to richcraft - dow honda site tower i . take the floors record of this row . select the rows whose building record fuzzily matches to claridge plaza iii . take the floors record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; building ; richcraft - dow honda site tower i } ; floors } ; hop { filter_eq { all_rows ; building ; claridge plaza iii } ; floors } }
select the row whose total record of all rows is minimum . the player record of this row is seve ballesteros .
Output: eq { hop { argmin { all_rows ; total } ; player } ; seve ballesteros }
select the rows whose political party record fuzzily matches to non - party . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; political party ; non - party } } ; 3 }
the average of the bronze record of all rows is 1.7 .
Output: round_eq { avg { all_rows ; bronze } ; 1.7 }
select the rows whose surface record fuzzily matches to hard . select the row whose date record of these rows is minimum . the opponent in the final record of this row is andre agassi .
Output: eq { hop { argmin { filter_eq { all_rows ; surface ; hard } ; date } ; opponent in the final } ; andre agassi }
the average of the clean electric grid california ( san francisco ) record of all rows is 131.4 g / mile .
Output: round_eq { avg { all_rows ; clean electric grid california ( san francisco ) } ; 131.4 g / mile }
the average of the total record of all rows is 15 .
Output: round_eq { avg { all_rows ; total } ; 15 }
select the rows whose height record is less than 6-0 . there is only one such row in the table . the player record of this unqiue row is myles mack .
Output: and { only { filter_less { all_rows ; height ; 6-0 } } ; eq { hop { filter_less { all_rows ; height ; 6-0 } ; player } ; myles mack } }
select the rows whose date record fuzzily matches to 2002 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; date ; 2002 } } ; 4 }
the average of the grüne record of all rows is 10.58 % .
Output: round_eq { avg { all_rows ; grüne } ; 10.58 % }
select the row whose position record of all rows is minimum . the competition record of this row is universiade .
Output: eq { hop { argmin { all_rows ; position } ; competition } ; universiade }
select the row whose grid record of all rows is maximum . the driver record of this row is zsolt baumgartner .
Output: eq { hop { argmax { all_rows ; grid } ; driver } ; zsolt baumgartner }
select the rows whose player record fuzzily matches to ken higgs . take the wickets record of this row . select the rows whose player record fuzzily matches to stuart turner . take the wickets record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; ken higgs } ; wickets } ; hop { filter_eq { all_rows ; player ; stuart turner } ; wickets } }
for the year records of all rows , most of them are greater than 2010 .
Output: most_greater { all_rows ; year ; 2010 }
the sum of the attendance record of all rows is 771872 .
Output: round_eq { sum { all_rows ; attendance } ; 771872 }
select the rows whose amino acid record fuzzily matches to valine . take the hydropathy index record of this row . select the rows whose amino acid record fuzzily matches to glycine . take the hydropathy index record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; amino acid ; valine } ; hydropathy index } ; hop { filter_eq { all_rows ; amino acid ; glycine } ; hydropathy index } }
select the rows whose label record fuzzily matches to toshiba - emi . there is only one such row in the table . the country record of this unqiue row is japan .
Output: and { only { filter_eq { all_rows ; label ; toshiba - emi } } ; eq { hop { filter_eq { all_rows ; label ; toshiba - emi } ; country } ; japan } }
select the row whose crowd record of all rows is maximum . the venue record of this row is kardinia park .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; kardinia park }
select the rows whose primary industry record fuzzily matches to health care . the average of the market value ( usd million ) record of these rows is 151413.5 .
Output: round_eq { avg { filter_eq { all_rows ; primary industry ; health care } ; market value ( usd million ) } ; 151413.5 }
select the rows whose coach record fuzzily matches to steve mcnamara . among these rows , select the rows whose captain record fuzzily matches to paul deacon . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; coach ; steve mcnamara } ; captain ; paul deacon } } ; 3 }
for the period records of all rows , most of them fuzzily match to 3rd .
Output: most_eq { all_rows ; period ; 3rd }
select the row whose us viewers ( million ) record of all rows is 2nd maximum . the title record of this row is sabotage .
Output: eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; title } ; sabotage }
select the rows whose venue record fuzzily matches to punt road oval . 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 essendon . the date record of this unqiue row is 14 june 1947 .
Output: and { only { filter_eq { all_rows ; venue ; punt road oval } } ; and { eq { hop { filter_eq { all_rows ; venue ; punt road oval } ; home team } ; richmond } ; and { eq { hop { filter_eq { all_rows ; venue ; punt road oval } ; away team } ; essendon } ; eq { hop { filter_eq { all_rows ; venue ; punt road oval } ; date } ; 14 june 1947 } } } }
select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the year record of this unqiue row is 11 october 2004 .
Output: and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; year } ; 11 october 2004 } }
select the row whose aggregate score record of all rows is minimum . the proceed to quarter - final record of this row is montferrand .
Output: eq { hop { argmin { all_rows ; aggregate score } ; proceed to quarter - final } ; montferrand }
select the rows whose overall pick record fuzzily matches to 9 . take the overall pick record of this row . select the rows whose overall pick record fuzzily matches to 18 . take the overall pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; overall pick ; 9 } ; overall pick } ; hop { filter_eq { all_rows ; overall pick ; 18 } ; overall pick } }
the average of the attendance record of all rows is 23350 .
Output: round_eq { avg { all_rows ; attendance } ; 23350 }
select the rows whose title record is arbitrary . the number of such rows is 8 .
Output: eq { count { filter_all { all_rows ; title } } ; 8 }
select the row whose date record of all rows is 3rd minimum . the tournament record of this row is chennai , india .
Output: eq { hop { nth_argmin { all_rows ; date ; 3 } ; tournament } ; chennai , india }
select the rows whose kansas state vs record fuzzily matches to oklahoma . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; kansas state vs ; oklahoma } } ; 2 }
select the row whose frequency record of all rows is 1st maximum . the model number record of this row is athlon x2 5050e .
Output: eq { hop { nth_argmax { all_rows ; frequency ; 1 } ; model number } ; athlon x2 5050e }
the average of the attendance record of all rows is 45,972 .
Output: round_eq { avg { all_rows ; attendance } ; 45,972 }
select the row whose crowd record of all rows is maximum . the venue record of this row is junction oval .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; junction oval }
select the rows whose year record fuzzily matches to 2003 . take the start record of this row . select the rows whose year record fuzzily matches to 2004 . take the start record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; year ; 2003 } ; start } ; hop { filter_eq { all_rows ; year ; 2004 } ; start } }
select the row whose first place record of all rows is maximum . the region represented record of this row is china .
Output: eq { hop { argmax { all_rows ; first place } ; region represented } ; china }
select the rows whose race winner record fuzzily matches to michele alboreto . there is only one such row in the table . the date record of this unqiue row is 5 june .
Output: and { only { filter_eq { all_rows ; race winner ; michele alboreto } } ; eq { hop { filter_eq { all_rows ; race winner ; michele alboreto } ; date } ; 5 june } }
the average of the score record of all rows is 4.21 .
Output: round_eq { avg { all_rows ; score } ; 4.21 }
select the row whose silver record of all rows is maximum . the nation record of this row is japan .
Output: eq { hop { argmax { all_rows ; silver } ; nation } ; japan }
select the rows whose fuel / trans record fuzzily matches to diesel . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; fuel / trans ; diesel } } ; 5 }
select the rows whose player record fuzzily matches to stefan frei . take the graduated record of this row . select the rows whose player record fuzzily matches to peri maroå ¡ evic . take the graduated record of this row . the first record is less than the second record . the graduated record of the first row is 2010 . the graduated record of the second row is 2011 .
Output: and { less { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } } ; and { eq { hop { filter_eq { all_rows ; player ; stefan frei } ; graduated } ; 2010 } ; eq { hop { filter_eq { all_rows ; player ; peri maroå ¡ evic } ; graduated } ; 2011 } } }
select the rows whose year record is equal to 2008 . among these rows , select the rows whose length record is greater than 5:00 . there is only one such row in the table . the music video record of this unqiue row is umbrella ( feat younha ) ( 우산 ) .
Output: and { only { filter_greater { filter_eq { all_rows ; year ; 2008 } ; length ; 5:00 } } ; eq { hop { filter_greater { filter_eq { all_rows ; year ; 2008 } ; length ; 5:00 } ; music video } ; umbrella ( feat younha ) ( 우산 ) } }
select the rows whose region record fuzzily matches to japan . take the date record of this row . select the rows whose region record fuzzily matches to united kingdom . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; region ; japan } ; date } ; hop { filter_eq { all_rows ; region ; united kingdom } ; date } }
the average of the goals record of all rows is 18.83 .
Output: round_eq { avg { all_rows ; goals } ; 18.83 }
select the rows whose guest record fuzzily matches to garry trudeau . there is only one such row in the table . the episode record of this unqiue row is 806 .
Output: and { only { filter_eq { all_rows ; guest ; garry trudeau } } ; eq { hop { filter_eq { all_rows ; guest ; garry trudeau } ; episode } ; 806 } }
select the rows whose acronym record fuzzily matches to polimas . there is only one such row in the table . the name in english record of this unqiue row is sultan abdul halim mu'adzam shah .
Output: and { only { filter_eq { all_rows ; acronym ; polimas } } ; eq { hop { filter_eq { all_rows ; acronym ; polimas } ; name in english } ; sultan abdul halim mu'adzam shah } }
the average of the attendance record of all rows is 15503 .
Output: round_eq { avg { all_rows ; attendance } ; 15503 }
select the rows whose name record fuzzily matches to historic federal reserve bank . take the floors record of this row . select the rows whose name record fuzzily matches to kansas city power and light building . take the floors record of this row . the first record is less than the second record . the floors record of the first row is 16 . the floors record of the second row is 34 .
Output: and { less { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } } ; and { eq { hop { filter_eq { all_rows ; name ; historic federal reserve bank } ; floors } ; 16 } ; eq { hop { filter_eq { all_rows ; name ; kansas city power and light building } ; floors } ; 34 } } }
select the row whose points record of all rows is maximum . the year record of this row is 1993 .
Output: eq { hop { argmax { all_rows ; points } ; year } ; 1993 }
select the rows whose opponent record fuzzily matches to dallas cowboys . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; dallas cowboys } } ; 2 }
select the rows whose college record fuzzily matches to eckerd . there is only one such row in the table . the player record of this unqiue row is jay washington .
Output: and { only { filter_eq { all_rows ; college ; eckerd } } ; eq { hop { filter_eq { all_rows ; college ; eckerd } ; player } ; jay washington } }
select the row whose against record of all rows is minimum . the opposing teams record of this row is ireland .
Output: eq { hop { argmin { all_rows ; against } ; opposing teams } ; ireland }
select the row whose date record of all rows is minimum . the opponent record of this row is japan .
Output: eq { hop { argmin { all_rows ; date } ; opponent } ; japan }
select the rows whose transfer fee record fuzzily matches to 120k . there is only one such row in the table . the name record of this unqiue row is christou .
Output: and { only { filter_eq { all_rows ; transfer fee ; 120k } } ; eq { hop { filter_eq { all_rows ; transfer fee ; 120k } ; name } ; christou } }
the sum of the races record of all rows is 123 .
Output: round_eq { sum { all_rows ; races } ; 123 }
select the rows whose date record is equal to 1873 . the sum of the no built record of these rows is 14 .
Output: round_eq { sum { filter_eq { all_rows ; date ; 1873 } ; no built } ; 14 }
select the rows whose % change record is greater than 10 % . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; % change ; 10 % } } ; 3 }
select the rows whose origin record fuzzily matches to european union . there is only one such row in the table .
Output: only { filter_eq { all_rows ; origin ; european union } }
select the rows whose content record fuzzily matches to televendita . among these rows , select the rows whose television service record fuzzily matches to la sorgente . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; content ; televendita } ; television service ; la sorgente } } ; 3 }
select the rows whose station record fuzzily matches to tsu . take the distance ( km ) record of this row . select the rows whose station record fuzzily matches to kawage . take the distance ( km ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; station ; tsu } ; distance ( km ) } ; hop { filter_eq { all_rows ; station ; kawage } ; distance ( km ) } }
select the rows whose location attendance record fuzzily matches to united center . the sum of the location attendance record of these rows is 68,742 .
Output: round_eq { sum { filter_eq { all_rows ; location attendance ; united center } ; location attendance } ; 68,742 }
select the rows whose incumbent record fuzzily matches to dick armey . take the opponent record of this row . select the rows whose incumbent record fuzzily matches to jack brooks . take the opponent record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; incumbent ; dick armey } ; opponent } ; hop { filter_eq { all_rows ; incumbent ; jack brooks } ; opponent } }
select the rows whose year record is greater than 2004 . for the position records of these rows , most of them are greater than 50 .
Output: most_greater { filter_greater { all_rows ; year ; 2004 } ; position ; 50 }
select the rows whose royal house record fuzzily matches to jiang . there is only one such row in the table . the state record of this unqiue row is qi .
Output: and { only { filter_eq { all_rows ; royal house ; jiang } } ; eq { hop { filter_eq { all_rows ; royal house ; jiang } ; state } ; qi } }
select the rows whose nationality record fuzzily matches to japan . select the row whose time record of these rows is maximum . the name record of this row is noriko inada .
Output: eq { hop { argmax { filter_eq { all_rows ; nationality ; japan } ; time } ; name } ; noriko inada }
for the lineup records of all rows , most of them fuzzily match to 90 . start .
Output: most_eq { all_rows ; lineup ; 90 . start }
for the crankshaft records of all rows , most of them fuzzily match to 180 degree .
Output: most_eq { all_rows ; crankshaft ; 180 degree }
for the network records of all rows , most of them fuzzily match to abc 's wwos .
Output: most_eq { all_rows ; network ; abc 's wwos }
select the rows whose result record does not match to won . the number of such rows is 2 .
Output: eq { count { filter_not_eq { all_rows ; result ; won } } ; 2 }
for the goals records of all rows , all of them are equal to 0 .
Output: all_eq { all_rows ; goals ; 0 }
the sum of the screens record of all rows is 461 .
Output: round_eq { sum { all_rows ; screens } ; 461 }
select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 66 .
Output: round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 66 }
select the rows whose incumbent record fuzzily matches to thomas shailer weston . take the date record of this row . select the rows whose incumbent record fuzzily matches to isaac wilson . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; thomas shailer weston } ; date } ; hop { filter_eq { all_rows ; incumbent ; isaac wilson } ; date } }
select the rows whose crowd record is less than 17000 . there is only one such row in the table . the venue record of this unqiue row is punt road oval .
Output: and { only { filter_less { all_rows ; crowd ; 17000 } } ; eq { hop { filter_less { all_rows ; crowd ; 17000 } ; venue } ; punt road oval } }
select the rows whose time / retired record fuzzily matches to + 2 laps . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; time / retired ; + 2 laps } } ; 5 }
select the rows whose dominant religion ( 2002 ) record fuzzily matches to catholic christianity . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; dominant religion ( 2002 ) ; catholic christianity } } ; 2 }
select the rows whose venue record fuzzily matches to greece . there is only one such row in the table . the competition record of this unqiue row is world athletics final .
Output: and { only { filter_eq { all_rows ; venue ; greece } } ; eq { hop { filter_eq { all_rows ; venue ; greece } ; competition } ; world athletics final } }
the maximum margin of victory record of all rows is 11 strokes .
Output: eq { max { all_rows ; margin of victory } ; 11 strokes }
select the rows whose institution record fuzzily matches to boise state university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to california state university , bakersfield . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; institution ; boise state university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; california state university , bakersfield } ; enrollment } }
select the row whose location attendance record of all rows is maximum . the date record of this row is january 11 .
Output: eq { hop { argmax { all_rows ; location attendance } ; date } ; january 11 }
the average of the money record of all rows is 420350 .
Output: round_eq { avg { all_rows ; money } ; 420350 }
the average of the average ratings record of all rows is 11.76 % .
Output: round_eq { avg { all_rows ; average ratings } ; 11.76 % }
select the rows whose name record fuzzily matches to frank borman . take the born record of this row . select the rows whose name record fuzzily matches to jim lovell . take the born record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; frank borman } ; born } ; hop { filter_eq { all_rows ; name ; jim lovell } ; born } }
select the rows whose division record fuzzily matches to southeast . the average of the pts record of these rows is 75.5 .
Output: round_eq { avg { filter_eq { all_rows ; division ; southeast } ; pts } ; 75.5 }
select the rows whose opponent record fuzzily matches to manny morales . take the round record of this row . select the rows whose opponent record fuzzily matches to alberto rodriguez . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; manny morales } ; round } ; hop { filter_eq { all_rows ; opponent ; alberto rodriguez } ; round } }
select the rows whose us mission record fuzzily matches to ranger . the average of the mass ( kg ) record of these rows is 340 .
Output: round_eq { avg { filter_eq { all_rows ; us mission ; ranger } ; mass ( kg ) } ; 340 }
select the rows whose transfer fee record fuzzily matches to youth system . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; transfer fee ; youth system } } ; 2 }
select the rows whose home team record fuzzily matches to fitzroy . take the crowd record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; crowd } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; crowd } }
select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is clarence f lea .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; clarence f lea }
for the final records of all rows , most of them fuzzily match to did not advance .
Output: most_eq { all_rows ; final ; did not advance }
the average of the away team score record of all rows is 8.75 .
Output: round_eq { avg { all_rows ; away team score } ; 8.75 }
the average of the drawn record of all rows is 2.55 .
Output: round_eq { avg { all_rows ; drawn } ; 2.55 }
select the rows whose location / attendance record fuzzily matches to the omni . the maximum score record of these rows is w 124 - 91 .
Output: eq { max { filter_eq { all_rows ; location / attendance ; the omni } ; score } ; w 124 - 91 }
select the rows whose place record is greater than 1 . among these rows , select the rows whose points jury record is greater than 30 . the number of such rows is 4 .
Output: eq { count { filter_greater { filter_greater { all_rows ; place ; 1 } ; points jury ; 30 } } ; 4 }
select the rows whose match points record fuzzily matches to 4-0 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; match points ; 4-0 } } ; 3 }
select the rows whose channel record fuzzily matches to atv world . take the launch date record of this row . select the rows whose channel record fuzzily matches to tvb pearl . take the launch date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; channel ; atv world } ; launch date } ; hop { filter_eq { all_rows ; channel ; tvb pearl } ; launch date } }
for the goodman records of all rows , most of them are greater than 8 .
Output: most_greater { all_rows ; goodman ; 8 }
select the rows whose tyre record fuzzily matches to e . for the constructor records of these rows , most of them fuzzily match to ferrari .
Output: most_eq { filter_eq { all_rows ; tyre ; e } ; constructor ; ferrari }
select the rows whose school / club team record fuzzily matches to university of texas at arlington . the sum of the overall record of these rows is 308 .
Output: round_eq { sum { filter_eq { all_rows ; school / club team ; university of texas at arlington } ; overall } ; 308 }
select the rows whose city record fuzzily matches to mansuriyah . there is only one such row in the table . the club record of this unqiue row is arabi .
Output: and { only { filter_eq { all_rows ; city ; mansuriyah } } ; eq { hop { filter_eq { all_rows ; city ; mansuriyah } ; club } ; arabi } }
select the rows whose pilot record fuzzily matches to and . there is only one such row in the table . the vehicle record of this unqiue row is schempp - hirth nimbus - 4dm .
Output: and { only { filter_eq { all_rows ; pilot ; and } } ; eq { hop { filter_eq { all_rows ; pilot ; and } ; vehicle } ; schempp - hirth nimbus - 4dm } }
select the row whose cultural and educational panel record of all rows is 2nd maximum . the party record of this row is fianna fáil .
Output: eq { hop { nth_argmax { all_rows ; cultural and educational panel ; 2 } ; party } ; fianna fáil }