niryuu/sni-each-converted huggingface dataset:
select the rows whose opponent record fuzzily matches to alexey ignashov . take the round record of this row . select the rows whose opponent record fuzzily matches to lee hasdell . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; alexey ignashov } ; round } ; hop { filter_eq { all_rows ; opponent ; lee hasdell } ; round } }
select the rows whose incumbent record fuzzily matches to david trimble . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to thomas p moore . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; david trimble } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; thomas p moore } ; first elected } }
the average of the percentage record of all rows is 50 % .
Output: round_eq { avg { all_rows ; percentage } ; 50 % }
the sum of the issue price record of all rows is 395.1 .
Output: round_eq { sum { all_rows ; issue price } ; 395.1 }
select the row whose black knights points record of all rows is 4th minimum . the date record of this row is oct 20 .
Output: eq { hop { nth_argmin { all_rows ; black knights points ; 4 } ; date } ; oct 20 }
select the rows whose frequency record is less than 1000 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; frequency ; 1000 } } ; 3 }
select the rows whose country record fuzzily matches to south africa . there is only one such row in the table . the player record of this unqiue row is wayne westner .
Output: and { only { filter_eq { all_rows ; country ; south africa } } ; eq { hop { filter_eq { all_rows ; country ; south africa } ; player } ; wayne westner } }
select the rows whose country record fuzzily matches to ch . there is only one such row in the table . the building record of this unqiue row is victoria hall .
Output: and { only { filter_eq { all_rows ; country ; ch } } ; eq { hop { filter_eq { all_rows ; country ; ch } ; building } ; victoria hall } }
select the row whose attendance record of all rows is 3rd maximum . the date record of this row is october 1 , 1989 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; october 1 , 1989 }
select the rows whose cause record fuzzily matches to gas explosion . the number of such rows is 16 .
Output: eq { count { filter_eq { all_rows ; cause ; gas explosion } } ; 16 }
select the rows whose wickets record is greater than 700 . there is only one such row in the table . the name record of this unqiue row is shane warne .
Output: and { only { filter_greater { all_rows ; wickets ; 700 } } ; eq { hop { filter_greater { all_rows ; wickets ; 700 } ; name } ; shane warne } }
select the rows whose flagship station record fuzzily matches to wrko . there is only one such row in the table . the color commentator ( s ) record of this unqiue row is cedric maxwell .
Output: and { only { filter_eq { all_rows ; flagship station ; wrko } } ; eq { hop { filter_eq { all_rows ; flagship station ; wrko } ; color commentator ( s ) } ; cedric maxwell } }
select the rows whose listed record fuzzily matches to 1997 . for the built records of these rows , most of them fuzzily match to 1894 .
Output: most_eq { filter_eq { all_rows ; listed ; 1997 } ; built ; 1894 }
select the rows whose country record fuzzily matches to netherlands . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; country ; netherlands } } ; 2 }
select the rows whose film record fuzzily matches to bullets over broadway . among these rows , select the rows whose result record fuzzily matches to nominated . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; film ; bullets over broadway } ; result ; nominated } } ; 2 }
select the row whose total record of all rows is maximum . the athlete record of this row is anastasia davydova & anastasiya yermakova .
Output: eq { hop { argmax { all_rows ; total } ; athlete } ; anastasia davydova & anastasiya yermakova }
for the points records of all rows , most of them are greater than or equal to 9 .
Output: most_greater_eq { all_rows ; points ; 9 }
select the rows whose gold record is equal to 6 . there is only one such row in the table . the nation record of this unqiue row is united states .
Output: and { only { filter_eq { all_rows ; gold ; 6 } } ; eq { hop { filter_eq { all_rows ; gold ; 6 } ; nation } ; united states } }
select the rows whose surface record fuzzily matches to carpet ( i ) . there is only one such row in the table . the tournament record of this unqiue row is wolfsburg , germany .
Output: and { only { filter_eq { all_rows ; surface ; carpet ( i ) } } ; eq { hop { filter_eq { all_rows ; surface ; carpet ( i ) } ; tournament } ; wolfsburg , germany } }
select the rows whose gold record is equal to 3 . there is only one such row in the table . the nation record of this unqiue row is china ( chn ) .
Output: and { only { filter_eq { all_rows ; gold ; 3 } } ; eq { hop { filter_eq { all_rows ; gold ; 3 } ; nation } ; china ( chn ) } }
select the rows whose leading scorer record fuzzily matches to lebron james . among these rows , select the rows whose attendance record is less than 15000 . there is only one such row in the table . the date record of this unqiue row is february 20 .
Output: and { only { filter_less { filter_eq { all_rows ; leading scorer ; lebron james } ; attendance ; 15000 } } ; eq { hop { filter_less { filter_eq { all_rows ; leading scorer ; lebron james } ; attendance ; 15000 } ; date } ; february 20 } }
select the rows whose goals record is equal to 33 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; goals ; 33 } } ; 2 }
select the rows whose date record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; date } } ; 6 }
select the rows whose opponent record fuzzily matches to lee hasdell . take the round record of this row . select the rows whose opponent record fuzzily matches to zabir elbiev . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; lee hasdell } ; round } ; hop { filter_eq { all_rows ; opponent ; zabir elbiev } ; round } }
select the row whose age record of all rows is 1st maximum . the contestant record of this row is cindy guerrero reynoso .
Output: eq { hop { nth_argmax { all_rows ; age ; 1 } ; contestant } ; cindy guerrero reynoso }
select the rows whose classification record fuzzily matches to fraternity & sorority . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; classification ; fraternity & sorority } } ; 2 }
the average of the erp w record of all rows is 23 .
Output: round_eq { avg { all_rows ; erp w } ; 23 }
select the rows whose venue record fuzzily matches to cricket . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; venue ; cricket } } ; 3 }
select the rows whose player record fuzzily matches to dwight lewis . take the round record of this row . select the rows whose player record fuzzily matches to doug hogan . take the round record of this row . the first record is less than the second record . the round record of the first row is 10 . the round record of the second row is 16 .
Output: and { less { hop { filter_eq { all_rows ; player ; dwight lewis } ; round } ; hop { filter_eq { all_rows ; player ; doug hogan } ; round } } ; and { eq { hop { filter_eq { all_rows ; player ; dwight lewis } ; round } ; 10 } ; eq { hop { filter_eq { all_rows ; player ; doug hogan } ; round } ; 16 } } }
select the rows whose name record fuzzily matches to metro ag . take the revenue ( mil ) record of this row . select the rows whose name record fuzzily matches to daimler ag . take the revenue ( mil ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; metro ag } ; revenue ( mil ) } ; hop { filter_eq { all_rows ; name ; daimler ag } ; revenue ( mil ) } }
select the row whose date record of all rows is 2nd maximum . the opponent in the final record of this row is milos raonic .
Output: eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent in the final } ; milos raonic }
for the year joined records of all rows , most of them are less than 1978 .
Output: most_less { all_rows ; year joined ; 1978 }
for the score records of all rows , most of them are equal to 1 .
Output: most_eq { all_rows ; score ; 1 }
select the rows whose name record fuzzily matches to pandeli evangjeli ( 2nd time ) . take the term start record of this row . select the rows whose name record fuzzily matches to mehdi bej frashëri ( 1st time ) . take the term start record of this row . the first record is less than the second record . the term start record of the first row is 6 march 1930 . the term start record of the second row is 22 october 1935 .
Output: and { less { hop { filter_eq { all_rows ; name ; pandeli evangjeli ( 2nd time ) } ; term start } ; hop { filter_eq { all_rows ; name ; mehdi bej frashëri ( 1st time ) } ; term start } } ; and { eq { hop { filter_eq { all_rows ; name ; pandeli evangjeli ( 2nd time ) } ; term start } ; 6 march 1930 } ; eq { hop { filter_eq { all_rows ; name ; mehdi bej frashëri ( 1st time ) } ; term start } ; 22 october 1935 } } }
select the rows whose city or town record fuzzily matches to istanbul . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; city or town ; istanbul } } ; 4 }
select the row whose prominence record of all rows is 15th maximum . the mountain peak record of this row is mount fernow .
Output: eq { hop { nth_argmax { all_rows ; prominence ; 15 } ; mountain peak } ; mount fernow }
select the rows whose nominated / won record fuzzily matches to won . the 2nd minimum year record of these rows is 2008 .
Output: eq { nth_min { filter_eq { all_rows ; nominated / won ; won } ; year ; 2 } ; 2008 }
for the screen size ( inch ) records of all rows , most of them are greater than or equal to 7 .
Output: most_greater_eq { all_rows ; screen size ( inch ) ; 7 }
select the rows whose title record fuzzily matches to new girls . take the original airdate record of this row . select the rows whose title record fuzzily matches to freedom . take the original airdate record of this row . the first record is greater than the second record . the original airdate record of the first row is 25 march 1978 . the original airdate record of the second row is 18 march 1978 .
Output: and { greater { hop { filter_eq { all_rows ; title ; new girls } ; original airdate } ; hop { filter_eq { all_rows ; title ; freedom } ; original airdate } } ; and { eq { hop { filter_eq { all_rows ; title ; new girls } ; original airdate } ; 25 march 1978 } ; eq { hop { filter_eq { all_rows ; title ; freedom } ; original airdate } ; 18 march 1978 } } }
select the rows whose player record is arbitrary . the number of such rows is 12 .
Output: eq { count { filter_all { all_rows ; player } } ; 12 }
select the row whose result record of all rows is maximum . the date record of this row is 8 october 2010 .
Output: eq { hop { argmax { all_rows ; result } ; date } ; 8 october 2010 }
select the row whose wins record of all rows is maximum . the club record of this row is warrnambool .
Output: eq { hop { argmax { all_rows ; wins } ; club } ; warrnambool }
the average of the attendance record of all rows is 58746 .
Output: round_eq { avg { all_rows ; attendance } ; 58746 }
select the row whose date record of all rows is 3rd minimum . the venue record of this row is victoria park . the crowd record of this row is 23000 .
Output: and { eq { hop { nth_argmin { all_rows ; date ; 3 } ; venue } ; victoria park } ; eq { hop { nth_argmin { all_rows ; date ; 3 } ; crowd } ; 23000 } }
select the row whose week record of all rows is 9th minimum . the finalist record of this row is francesca schiavone .
Output: eq { hop { nth_argmin { all_rows ; week ; 9 } ; finalist } ; francesca schiavone }
select the row whose date record of all rows is 2nd maximum . the location attendance record of this row is verizon center 14485 .
Output: eq { hop { nth_argmax { all_rows ; date ; 2 } ; location attendance } ; verizon center 14485 }
select the rows whose location record fuzzily matches to bridgewater , massachusetts . take the enrollment record of this row . select the rows whose location record fuzzily matches to fitchburg , massachusetts . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; location ; bridgewater , massachusetts } ; enrollment } ; hop { filter_eq { all_rows ; location ; fitchburg , massachusetts } ; enrollment } }
select the rows whose original air date record fuzzily matches to august . there is only one such row in the table . the family / families record of this unqiue row is the hillhouse - docherty family .
Output: and { only { filter_eq { all_rows ; original air date ; august } } ; eq { hop { filter_eq { all_rows ; original air date ; august } ; family / families } ; the hillhouse - docherty family } }
select the rows whose opponent record fuzzily matches to minnesota vikings . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; minnesota vikings } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } }
select the rows whose genre record fuzzily matches to period drama . there is only one such row in the table . the english title ( chinese title ) record of this unqiue row is old time buddy - to catch a thief 難兄難弟之神探李奇 .
Output: and { only { filter_eq { all_rows ; genre ; period drama } } ; eq { hop { filter_eq { all_rows ; genre ; period drama } ; english title ( chinese title ) } ; old time buddy - to catch a thief 難兄難弟之神探李奇 } }
select the row whose starts record of all rows is 2nd maximum . the year record of this row is 1994 .
Output: eq { hop { nth_argmax { all_rows ; starts ; 2 } ; year } ; 1994 }
select the rows whose director record fuzzily matches to siddique . take the year record of this row . select the rows whose director record fuzzily matches to arbaaz khan . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; director ; siddique } ; year } ; hop { filter_eq { all_rows ; director ; arbaaz khan } ; year } }
select the row whose swing to gain record of all rows is 3rd maximum . the constituency record of this row is ayr .
Output: eq { hop { nth_argmax { all_rows ; swing to gain ; 3 } ; constituency } ; ayr }
select the rows whose area record fuzzily matches to azad kashmir . take the % muslim record of this row . select the rows whose area record fuzzily matches to gilgit - baltistan . take the % muslim record of this row . the first record is 1 % larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; area ; azad kashmir } ; % muslim } ; hop { filter_eq { all_rows ; area ; gilgit - baltistan } ; % muslim } } ; 1 % }
select the rows whose rank record is less than or equal to 3 . among these rows , select the rows whose loss record is greater than or equal to 1 . the number of such rows is 2 .
Output: eq { count { filter_greater_eq { filter_less_eq { all_rows ; rank ; 3 } ; loss ; 1 } } ; 2 }
select the rows whose institution record is arbitrary . the number of such rows is 11 .
Output: eq { count { filter_all { all_rows ; institution } } ; 11 }
select the row whose population record of all rows is maximum . the borough record of this row is fleurimont .
Output: eq { hop { argmax { all_rows ; population } ; borough } ; fleurimont }
select the rows whose original air date record fuzzily matches to january . there is only one such row in the table . the title record of this unqiue row is checking out .
Output: and { only { filter_eq { all_rows ; original air date ; january } } ; eq { hop { filter_eq { all_rows ; original air date ; january } ; title } ; checking out } }
select the rows whose name record fuzzily matches to uchana kalan . take the number of electorates ( 2009 ) record of this row . select the rows whose name record fuzzily matches to bawani khera . take the number of electorates ( 2009 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; uchana kalan } ; number of electorates ( 2009 ) } ; hop { filter_eq { all_rows ; name ; bawani khera } ; number of electorates ( 2009 ) } }
select the rows whose enrollment record is greater than 3000 . there is only one such row in the table . the school record of this unqiue row is lake central .
Output: and { only { filter_greater { all_rows ; enrollment ; 3000 } } ; eq { hop { filter_greater { all_rows ; enrollment ; 3000 } ; school } ; lake central } }
select the row whose points record of all rows is maximum . the driver / passenger record of this row is daniãl willemsen / sven verbrugge 1 .
Output: eq { hop { argmax { all_rows ; points } ; driver / passenger } ; daniãl willemsen / sven verbrugge 1 }
select the rows whose pos record fuzzily matches to d . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; pos ; d } } ; 4 }
select the rows whose office record fuzzily matches to representative . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; office ; representative } } ; 5 }
select the rows whose round record fuzzily matches to round of 32 - 1st leg . take the attendance record of this row . select the rows whose round record fuzzily matches to round of 32 - 2nd leg . take the attendance record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; round ; round of 32 - 1st leg } ; attendance } ; hop { filter_eq { all_rows ; round ; round of 32 - 2nd leg } ; attendance } }
select the rows whose surface record fuzzily matches to hard . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; surface ; hard } } ; 3 }
select the rows whose peak record is equal to 1 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; peak ; 1 } } ; 4 }
select the rows whose pavement record fuzzily matches to asphalt . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; pavement ; asphalt } } ; 9 }
select the rows whose chassis record fuzzily matches to ferrari 156 aero . there is only one such row in the table . the year record of this unqiue row is 1964 .
Output: and { only { filter_eq { all_rows ; chassis ; ferrari 156 aero } } ; eq { hop { filter_eq { all_rows ; chassis ; ferrari 156 aero } ; year } ; 1964 } }
select the row whose first season record of all rows is maximum . the club record of this row is godoy cruz .
Output: eq { hop { argmax { all_rows ; first season } ; club } ; godoy cruz }
select the row whose margin of victory record of all rows is maximum . the date record of this row is 22 jun 2006 .
Output: eq { hop { argmax { all_rows ; margin of victory } ; date } ; 22 jun 2006 }
select the rows whose director record fuzzily matches to fraser . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; director ; fraser } } ; 2 }
select the row whose weeks on top record of all rows is 2nd maximum . the song record of this row is livin ' la vida loca .
Output: eq { hop { nth_argmax { all_rows ; weeks on top ; 2 } ; song } ; livin ' la vida loca }
select the rows whose competition record fuzzily matches to european cup . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; competition ; european cup } } ; 4 }
select the row whose attendance record of all rows is 1st maximum . the date record of this row is june 25 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; june 25 }
select the rows whose leading scorer record fuzzily matches to lee gregory . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; leading scorer ; lee gregory } } ; 3 }
select the row whose run time record of all rows is 2nd maximum . the episode number record of this row is 311 .
Output: eq { hop { nth_argmax { all_rows ; run time ; 2 } ; episode number } ; 311 }
select the rows whose erp w record is equal to 10 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; erp w ; 10 } } ; 4 }
select the rows whose engine record fuzzily matches to ecotec l61 i4 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; engine ; ecotec l61 i4 } } ; 6 }
for the first issue records of all rows , most of them fuzzily match to august 2008 .
Output: most_eq { all_rows ; first issue ; august 2008 }
select the rows whose year record fuzzily matches to 2005 . take the points record of this row . select the rows whose year record fuzzily matches to 2007 . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 2005 } ; points } ; hop { filter_eq { all_rows ; year ; 2007 } ; points } }
select the rows whose year record fuzzily matches to 1995 . take the points record of this row . select the rows whose year record fuzzily matches to 1996 . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1995 } ; points } ; hop { filter_eq { all_rows ; year ; 1996 } ; points } }
select the row whose quantity made record of all rows is 4th maximum . the class record of this row is c .
Output: eq { hop { nth_argmax { all_rows ; quantity made ; 4 } ; class } ; c }
select the row whose goals record of all rows is maximum . the season record of this row is 2009 - 10 .
Output: eq { hop { argmax { all_rows ; goals } ; season } ; 2009 - 10 }
select the rows whose erp w record is equal to 27 . there is only one such row in the table . the call sign record of this unqiue row is w230aa .
Output: and { only { filter_eq { all_rows ; erp w ; 27 } } ; eq { hop { filter_eq { all_rows ; erp w ; 27 } ; call sign } ; w230aa } }
select the rows whose channel record is equal to 40 . there is only one such row in the table . the territory record of this unqiue row is indonesia .
Output: and { only { filter_eq { all_rows ; channel ; 40 } } ; eq { hop { filter_eq { all_rows ; channel ; 40 } ; territory } ; indonesia } }
select the rows whose result record fuzzily matches to re - elected . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 3 }
select the rows whose party record fuzzily matches to republican . the minimum district record of these rows is texas 8 .
Output: eq { min { filter_eq { all_rows ; party ; republican } ; district } ; texas 8 }
the average of the us viewers ( millions ) record of all rows is 0.944 .
Output: round_eq { avg { all_rows ; us viewers ( millions ) } ; 0.944 }
for the date records of all rows , all of them fuzzily match to 29 july 1978 .
Output: all_eq { all_rows ; date ; 29 july 1978 }
the average of the time record of all rows is 45.16 .
Output: round_eq { avg { all_rows ; time } ; 45.16 }
select the rows whose rec record is less than 50 . the sum of the yards record of these rows is 37 .
Output: round_eq { sum { filter_less { all_rows ; rec ; 50 } ; yards } ; 37 }
select the rows whose college record fuzzily matches to florida state . there is only one such row in the table . the player record of this unqiue row is terry , nat nat terry .
Output: and { only { filter_eq { all_rows ; college ; florida state } } ; eq { hop { filter_eq { all_rows ; college ; florida state } ; player } ; terry , nat nat terry } }
the average of the televote / sms record of all rows is 8.33 .
Output: round_eq { avg { all_rows ; televote / sms } ; 8.33 }
select the row whose start date record of all rows is 4th minimum . the minister record of this row is marc fischbach .
Output: eq { hop { nth_argmin { all_rows ; start date ; 4 } ; minister } ; marc fischbach }
select the rows whose fuel system record fuzzily matches to single carburetor . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; fuel system ; single carburetor } } ; 6 }
select the row whose total record of all rows is maximum . the athlete record of this row is yane marques .
Output: eq { hop { argmax { all_rows ; total } ; athlete } ; yane marques }
for the seaman records of all rows , most of them fuzzily match to petty officer .
Output: most_eq { all_rows ; seaman ; petty officer }
for the team classification records of all rows , most of them fuzzily match to predictor-lotto .
Output: most_eq { all_rows ; team classification ; predictor-lotto }
select the rows whose final record fuzzily matches to did not advance . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; final ; did not advance } } ; 3 }
select the rows whose nationality record fuzzily matches to united states . select the row whose jersey number ( s ) record of these rows is maximum . the player record of this row is frank oleynick .
Output: eq { hop { argmax { filter_eq { all_rows ; nationality ; united states } ; jersey number ( s ) } ; player } ; frank oleynick }
select the rows whose 2nd position record fuzzily matches to lija . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; 2nd position ; lija } } ; 2 }
select the row whose wkts record of all rows is maximum . the player record of this row is brett lee .
Output: eq { hop { argmax { all_rows ; wkts } ; player } ; brett lee }
for the competition records of all rows , most of them fuzzily match to 2002 world cup qualifier .
Output: most_eq { all_rows ; competition ; 2002 world cup qualifier }
select the rows whose venue record fuzzily matches to punt road oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to kardinia park . take the crowd record of this row . the second record is 4000 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; venue ; punt road oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; kardinia park } ; crowd } } ; -4000 }
select the rows whose college record fuzzily matches to charleston southern . there is only one such row in the table . the player record of this unqiue row is shawn gifford .
Output: and { only { filter_eq { all_rows ; college ; charleston southern } } ; eq { hop { filter_eq { all_rows ; college ; charleston southern } ; player } ; shawn gifford } }
the average of the points record of all rows is 325.4 .
Output: round_eq { avg { all_rows ; points } ; 325.4 }
select the rows whose points record is equal to 0 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; points ; 0 } } ; 5 }
select the row whose census ranking record of all rows is 2nd minimum . the official name record of this row is dieppe .
Output: eq { hop { nth_argmin { all_rows ; census ranking ; 2 } ; official name } ; dieppe }
select the row whose elected record of all rows is minimum . the incumbent record of this row is john lewis .
Output: eq { hop { argmin { all_rows ; elected } ; incumbent } ; john lewis }
select the rows whose certification record fuzzily matches to 2x platinum . there is only one such row in the table . the album record of this unqiue row is my love : essential collection .
Output: and { only { filter_eq { all_rows ; certification ; 2x platinum } } ; eq { hop { filter_eq { all_rows ; certification ; 2x platinum } ; album } ; my love : essential collection } }
select the rows whose country record fuzzily matches to finland . there is only one such row in the table . the company record of this unqiue row is stonesoft .
Output: and { only { filter_eq { all_rows ; country ; finland } } ; eq { hop { filter_eq { all_rows ; country ; finland } ; company } ; stonesoft } }
select the rows whose directed by record fuzzily matches to james quinn . there is only one such row in the table . the title record of this unqiue row is indifference .
Output: and { only { filter_eq { all_rows ; directed by ; james quinn } } ; eq { hop { filter_eq { all_rows ; directed by ; james quinn } ; title } ; indifference } }
select the rows whose start source record fuzzily matches to bbc sport . among these rows , select the rows whose loan club record fuzzily matches to dundee united . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; start source ; bbc sport } ; loan club ; dundee united } } ; 2 }
select the row whose gold record of all rows is maximum . the nation record of this row is soviet union .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; soviet union }
the average of the population ( 2011 ) record of all rows is 4251 .
Output: round_eq { avg { all_rows ; population ( 2011 ) } ; 4251 }
select the rows whose date record fuzzily matches to december . for the attendance records of these rows , all of them are less than 75000 .
Output: all_less { filter_eq { all_rows ; date ; december } ; attendance ; 75000 }
select the row whose place record of all rows is minimum . the artist record of this row is liam reilly . the song record of this row is somewhere in europe .
Output: and { eq { hop { argmin { all_rows ; place } ; artist } ; liam reilly } ; eq { hop { argmin { all_rows ; place } ; song } ; somewhere in europe } }
select the row whose date of vacancy record of all rows is minimum . the outgoing manager record of this row is mauricio cienfuegos .
Output: eq { hop { argmin { all_rows ; date of vacancy } ; outgoing manager } ; mauricio cienfuegos }
select the rows whose inhabitants record is equal to 0 . there is only one such row in the table . the name record of this unqiue row is lítla dímun .
Output: and { only { filter_eq { all_rows ; inhabitants ; 0 } } ; eq { hop { filter_eq { all_rows ; inhabitants ; 0 } ; name } ; lítla dímun } }
select the rows whose site record fuzzily matches to memorial stadium minneapolis , mn . for the attendance records of these rows , most of them are greater than 24999 .
Output: most_greater { filter_eq { all_rows ; site ; memorial stadium minneapolis , mn } ; attendance ; 24999 }
select the row whose overall record of all rows is 3rd minimum . the name record of this row is brandon burlsworth .
Output: eq { hop { nth_argmin { all_rows ; overall ; 3 } ; name } ; brandon burlsworth }
select the rows whose opponent in the final record fuzzily matches to christína papadáki . take the date record of this row . select the rows whose opponent in the final record fuzzily matches to julia vakulenko . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent in the final ; christína papadáki } ; date } ; hop { filter_eq { all_rows ; opponent in the final ; julia vakulenko } ; date } }
for the high rebounds records of all rows , most of them fuzzily match to greg monroe .
Output: most_eq { all_rows ; high rebounds ; greg monroe }
select the row whose year ( ceremony ) record of all rows is 2nd maximum . the film title used in nomination record of this row is lore .
Output: eq { hop { nth_argmax { all_rows ; year ( ceremony ) ; 2 } ; film title used in nomination } ; lore }
select the rows whose race time record is less than 3:00:00 . the number of such rows is 4 .
Output: eq { count { filter_less { all_rows ; race time ; 3:00:00 } } ; 4 }
for the home records of all rows , most of them fuzzily match to philadelphia .
Output: most_eq { all_rows ; home ; philadelphia }
select the row whose attendance record of all rows is maximum . the date record of this row is november 13 , 1994 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; november 13 , 1994 }
select the rows whose date record fuzzily matches to december . for the attendance records of these rows , most of them are greater than 60000 .
Output: most_greater { filter_eq { all_rows ; date ; december } ; attendance ; 60000 }
select the row whose rank record of all rows is minimum . the nation record of this row is cuba .
Output: eq { hop { argmin { all_rows ; rank } ; nation } ; cuba }
select the rows whose venue record fuzzily matches to brisbane cricket ground . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; venue ; brisbane cricket ground } } ; 2 }
select the rows whose warship record fuzzily matches to chacabuco . take the speed ( knots ) record of this row . select the rows whose warship record fuzzily matches to abtao . take the speed ( knots ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; warship ; chacabuco } ; speed ( knots ) } ; hop { filter_eq { all_rows ; warship ; abtao } ; speed ( knots ) } }
the average of the pick record of all rows is 4.5 .
Output: round_eq { avg { all_rows ; pick } ; 4.5 }
select the rows whose score record fuzzily matches to w . there is only one such row in the table . the team record of this unqiue row is new jersey .
Output: and { only { filter_eq { all_rows ; score ; w } } ; eq { hop { filter_eq { all_rows ; score ; w } ; team } ; new jersey } }
for the location records of all rows , most of them fuzzily match to saitama , japan .
Output: most_eq { all_rows ; location ; saitama , japan }
select the row whose haat record of all rows is maximum . the station record of this row is wwpb .
Output: eq { hop { argmax { all_rows ; haat } ; station } ; wwpb }
select the row whose census ranking record of all rows is maximum . the official name record of this row is hampstead .
Output: eq { hop { argmax { all_rows ; census ranking } ; official name } ; hampstead }
select the row whose time record of all rows is maximum . the opponent record of this row is bob schrijber .
Output: eq { hop { argmax { all_rows ; time } ; opponent } ; bob schrijber }
for the time records of all rows , most of them fuzzily match to 2:15 . .
Output: most_eq { all_rows ; time ; 2:15 . }
select the rows whose playoffs 1 record fuzzily matches to champions . there is only one such row in the table . the season record of this unqiue row is 2001 - 02 .
Output: and { only { filter_eq { all_rows ; playoffs 1 ; champions } } ; eq { hop { filter_eq { all_rows ; playoffs 1 ; champions } ; season } ; 2001 - 02 } }
select the rows whose city record fuzzily matches to belgrade . the average of the average attendance record of these rows is 19,807 .
Output: round_eq { avg { filter_eq { all_rows ; city ; belgrade } ; average attendance } ; 19,807 }
the 2nd minimum round record of all rows is 2 . the player record of the row with 2nd minimum round record is joe hernandez .
Output: and { eq { nth_min { all_rows ; round ; 2 } ; 2 } ; eq { hop { nth_argmin { all_rows ; round ; 2 } ; player } ; joe hernandez } }
select the rows whose score record fuzzily matches to l . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; score ; l } } ; 1 }
for the stadium records of all rows , most of them fuzzily match to edison international field .
Output: most_eq { all_rows ; stadium ; edison international field }
select the rows whose children together record fuzzily matches to 1 son . there is only one such row in the table . the name record of this unqiue row is norris church mailer . the deceased spouse record of this unqiue row is norman mailer .
Output: and { only { filter_eq { all_rows ; children together ; 1 son } } ; and { eq { hop { filter_eq { all_rows ; children together ; 1 son } ; name } ; norris church mailer } ; eq { hop { filter_eq { all_rows ; children together ; 1 son } ; deceased spouse } ; norman mailer } } }
for the extra records of all rows , most of them fuzzily match to 4x100 m relay .
Output: most_eq { all_rows ; extra ; 4x100 m relay }
for the replaced by records of all rows , none of them is equal to - .
Output: all_not_eq { all_rows ; replaced by ; - }
select the rows whose opponent record fuzzily matches to cleber luciano . take the time record of this row . select the rows whose opponent record fuzzily matches to john borsos . take the time record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; cleber luciano } ; time } ; hop { filter_eq { all_rows ; opponent ; john borsos } ; time } }
select the row whose ios record of all rows is maximum . the source record of this row is wikimedia .
Output: eq { hop { argmax { all_rows ; ios } ; source } ; wikimedia }
select the rows whose date record fuzzily matches to october . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; date ; october } } ; 4 }
select the row whose pts record of all rows is 3rd maximum . the season record of this row is 2010 - 11 .
Output: eq { hop { nth_argmax { all_rows ; pts ; 3 } ; season } ; 2010 - 11 }
select the row whose years for jazz record of all rows is maximum . the player record of this row is kirk snyder .
Output: eq { hop { argmax { all_rows ; years for jazz } ; player } ; kirk snyder }
select the rows whose date record fuzzily matches to 12 june 1937 . select the row whose crowd record of these rows is maximum . the venue record of this row is punt road oval .
Output: eq { hop { argmax { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd } ; venue } ; punt road oval }
for the socket records of all rows , most of them fuzzily match to lga 775 .
Output: most_eq { all_rows ; socket ; lga 775 }
select the rows whose enrollment record is greater than 2000 . the number of such rows is 5 .
Output: eq { count { filter_greater { all_rows ; enrollment ; 2000 } } ; 5 }
select the rows whose laps record is equal to 96 . the average of the points record of these rows is 20.5 .
Output: round_eq { avg { filter_eq { all_rows ; laps ; 96 } ; points } ; 20.5 }
select the rows whose title record fuzzily matches to gilead . take the usviewers ( million ) record of this row . select the rows whose title record fuzzily matches to fa guan . take the usviewers ( million ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; title ; gilead } ; usviewers ( million ) } ; hop { filter_eq { all_rows ; title ; fa guan } ; usviewers ( million ) } }
select the rows whose name record fuzzily matches to 9 ad . take the power record of this row . select the rows whose name record fuzzily matches to 9 adb . take the power record of this row . the second record is 200 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; 9 ad } ; power } ; hop { filter_eq { all_rows ; name ; 9 adb } ; power } } ; -200 }
select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is james p richards .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; james p richards }
select the row whose first elected record of all rows is 3rd minimum . the incumbent record of this row is william huston natcher .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 3 } ; incumbent } ; william huston natcher }
select the rows whose year record is equal to 1954 . the sum of the points record of these rows is 4 .
Output: round_eq { sum { filter_eq { all_rows ; year ; 1954 } ; points } ; 4 }
select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to alvan adams . there is only one such row in the table . the date record of this unqiue row is april 15 .
Output: and { only { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; alvan adams } } ; eq { hop { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; alvan adams } ; date } ; april 15 } }
select the row whose area ( km square ) record of all rows is maximum . the english name record of this row is central denmark region .
Output: eq { hop { argmax { all_rows ; area ( km square ) } ; english name } ; central denmark region }
select the rows whose position record fuzzily matches to left wing . for the nationality records of these rows , most of them fuzzily match to canada .
Output: most_eq { filter_eq { all_rows ; position ; left wing } ; nationality ; canada }
for the to par records of all rows , most of them are greater than or equal to 9 .
Output: most_greater_eq { all_rows ; to par ; 9 }
select the row whose length record of all rows is maximum . the name record of this row is 6.5 x 68 .
Output: eq { hop { argmax { all_rows ; length } ; name } ; 6.5 x 68 }
select the rows whose winning driver record fuzzily matches to roberval andrade . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; winning driver ; roberval andrade } } ; 4 }
for the show records of all rows , most of them fuzzily match to terminator : the sarah connor chronicles .
Output: most_eq { all_rows ; show ; terminator : the sarah connor chronicles }
for the date records of all rows , all of them fuzzily match to 6 july 1957 .
Output: all_eq { all_rows ; date ; 6 july 1957 }
select the rows whose nba draft record fuzzily matches to pick . there is only one such row in the table . the player record of this unqiue row is marquis teague .
Output: and { only { filter_eq { all_rows ; nba draft ; pick } } ; eq { hop { filter_eq { all_rows ; nba draft ; pick } ; player } ; marquis teague } }
select the rows whose gain record is less than 100 . among these rows , select the rows whose long record is equal to 18 . there is only one such row in the table . the name record of this unqiue row is patton , chase .
Output: and { only { filter_eq { filter_less { all_rows ; gain ; 100 } ; long ; 18 } } ; eq { hop { filter_eq { filter_less { all_rows ; gain ; 100 } ; long ; 18 } ; name } ; patton , chase } }
select the row whose points record of all rows is maximum . the entrant record of this row is force india f1 team .
Output: eq { hop { argmax { all_rows ; points } ; entrant } ; force india f1 team }
select the rows whose result record fuzzily matches to nominated . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; result ; nominated } } ; 5 }
select the row whose total record of all rows is maximum . the 1953 record of this row is robert f wagner , jr .
Output: eq { hop { argmax { all_rows ; total } ; 1953 } ; robert f wagner , jr }
the average of the enrollment record of all rows is 1128.5 .
Output: round_eq { avg { all_rows ; enrollment } ; 1128.5 }
select the rows whose district record is arbitrary . the number of such rows is 16 .
Output: eq { count { filter_all { all_rows ; district } } ; 16 }
select the row whose points record of all rows is maximum . the song record of this row is all kinds of everything .
Output: eq { hop { argmax { all_rows ; points } ; song } ; all kinds of everything }
select the rows whose result record fuzzily matches to t . there is only one such row in the table .
Output: only { filter_eq { all_rows ; result ; t } }
select the rows whose tournament at which lead began record fuzzily matches to australian championships . there is only one such row in the table . the player record of this unqiue row is roy emerson .
Output: and { only { filter_eq { all_rows ; tournament at which lead began ; australian championships } } ; eq { hop { filter_eq { all_rows ; tournament at which lead began ; australian championships } ; player } ; roy emerson } }
select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is d wyatt aiken .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; d wyatt aiken }
the average of the points record of all rows is 51.8 .
Output: round_eq { avg { all_rows ; points } ; 51.8 }
select the rows whose gold record is greater than or equal to 1 . among these rows , select the rows whose silver record is equal to 2 . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_greater_eq { all_rows ; gold ; 1 } ; silver ; 2 } } ; 3 }
for the seats records of all rows , all of them are equal to 2 .
Output: all_eq { all_rows ; seats ; 2 }
select the rows whose position record fuzzily matches to ol . select the row whose pick record of these rows is minimum . the cfl team record of this row is montreal alouettes .
Output: eq { hop { argmin { filter_eq { all_rows ; position ; ol } ; pick } ; cfl team } ; montreal alouettes }
select the rows whose date record fuzzily matches to wednesday . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date ; wednesday } } ; 2 }
select the rows whose 2010 record fuzzily matches to sf . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; 2010 ; sf } } ; 2 }
select the rows whose built record is equal to 1923 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; built ; 1923 } } ; 2 }
select the rows whose driver record fuzzily matches to mike spence . take the laps record of this row . select the rows whose driver record fuzzily matches to jo bonnier . take the laps record of this row . the first record is 1 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; driver ; mike spence } ; laps } ; hop { filter_eq { all_rows ; driver ; jo bonnier } ; laps } } ; 1 }
the average of the votes record of all rows is 76762 .
Output: round_eq { avg { all_rows ; votes } ; 76762 }
select the rows whose opponent record fuzzily matches to carolina hurricanes . the sum of the points record of these rows is 57 .
Output: round_eq { sum { filter_eq { all_rows ; opponent ; carolina hurricanes } ; points } ; 57 }
select the row whose muzzle energy record of all rows is maximum . the cartridge record of this row is .357 magnum .
Output: eq { hop { argmax { all_rows ; muzzle energy } ; cartridge } ; .357 magnum }
select the row whose date successor seated record of all rows is 2nd minimum . the successor record of this row is michael j harrington ( d ) .
Output: eq { hop { nth_argmin { all_rows ; date successor seated ; 2 } ; successor } ; michael j harrington ( d ) }
select the rows whose location record fuzzily matches to rio de janeiro . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; rio de janeiro } } ; 2 }
select the rows whose coach record fuzzily matches to bj coleman . there is only one such row in the table . the episode summary record of this unqiue row is chris is made into a celebrity assistant .
Output: and { only { filter_eq { all_rows ; coach ; bj coleman } } ; eq { hop { filter_eq { all_rows ; coach ; bj coleman } ; episode summary } ; chris is made into a celebrity assistant } }
select the rows whose school record is arbitrary . the number of such rows is 8 .
Output: eq { count { filter_all { all_rows ; school } } ; 8 }
select the rows whose team record fuzzily matches to sírio libanês . take the played record of this row . select the rows whose team record fuzzily matches to jardim américa . take the played record of this row . the first record is less than the second record . the played record of the first row is 12 . the played record of the second row is 13 .
Output: and { less { hop { filter_eq { all_rows ; team ; sírio libanês } ; played } ; hop { filter_eq { all_rows ; team ; jardim américa } ; played } } ; and { eq { hop { filter_eq { all_rows ; team ; sírio libanês } ; played } ; 12 } ; eq { hop { filter_eq { all_rows ; team ; jardim américa } ; played } ; 13 } } }
select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is march 13 .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; march 13 }
the average of the result record of all rows is 15th .
Output: round_eq { avg { all_rows ; result } ; 15th }
select the row whose weeks on top record of all rows is 1st maximum . the song record of this row is music .
Output: eq { hop { nth_argmax { all_rows ; weeks on top ; 1 } ; song } ; music }
the average of the enrollment record of all rows is 738 .
Output: round_eq { avg { all_rows ; enrollment } ; 738 }
the sum of the winnings record of all rows is 2076417 .
Output: round_eq { sum { all_rows ; winnings } ; 2076417 }
select the rows whose type record fuzzily matches to embankment . the maximum height ( m ) record of these rows is 67.0 .
Output: eq { max { filter_eq { all_rows ; type ; embankment } ; height ( m ) } ; 67.0 }
select the rows whose result record fuzzily matches to safe . the sum of the score record of these rows is 54 .
Output: round_eq { sum { filter_eq { all_rows ; result ; safe } ; score } ; 54 }