niryuu/sni-each-converted huggingface dataset:
select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 3 }
select the rows whose crowd record is less than 10000 . there is only one such row in the table . the home team record of this unqiue row is st kilda .
Output: and { only { filter_less { all_rows ; crowd ; 10000 } } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; home team } ; st kilda } }
select the rows whose location record fuzzily matches to bangkok . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; bangkok } } ; 2 }
select the rows whose age record is less than 24 . there is only one such row in the table . the player record of this unqiue row is don budge .
Output: and { only { filter_less { all_rows ; age ; 24 } } ; eq { hop { filter_less { all_rows ; age ; 24 } ; player } ; don budge } }
select the rows whose partnering record fuzzily matches to simon stadler . take the date record of this row . select the rows whose partnering record fuzzily matches to paolo lorenzi . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; partnering ; simon stadler } ; date } ; hop { filter_eq { all_rows ; partnering ; paolo lorenzi } ; date } }
the average of the matches record of all rows is 51 .
Output: round_eq { avg { all_rows ; matches } ; 51 }
select the rows whose air date record fuzzily matches to july 2008 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; air date ; july 2008 } } ; 2 }
select the row whose population ( 2002 ) record of all rows is maximum . the urban settlement record of this row is novi sad .
Output: eq { hop { argmax { all_rows ; population ( 2002 ) } ; urban settlement } ; novi sad }
select the rows whose position record fuzzily matches to rb . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; rb } } ; 2 }
select the rows whose manner of departure record fuzzily matches to mutual consent . there is only one such row in the table . the outgoing manager record of this unqiue row is lee sinnott .
Output: and { only { filter_eq { all_rows ; manner of departure ; mutual consent } } ; eq { hop { filter_eq { all_rows ; manner of departure ; mutual consent } ; outgoing manager } ; lee sinnott } }
select the rows whose high rebounds record fuzzily matches to a horford . select the row whose location attendance record of these rows is maximum . the date record of this row is december 8 .
Output: eq { hop { argmax { filter_eq { all_rows ; high rebounds ; a horford } ; location attendance } ; date } ; december 8 }
select the row whose location / attendance record of all rows is 1st maximum . the opponent record of this row is new york .
Output: eq { hop { nth_argmax { all_rows ; location / attendance ; 1 } ; opponent } ; new york }
the average of the result record of all rows is 10.15 th .
Output: round_eq { avg { all_rows ; result } ; 10.15 th }
select the row whose attendance record of all rows is maximum . the week record of this row is 2 .
Output: eq { hop { argmax { all_rows ; attendance } ; week } ; 2 }
select the rows whose total ( kg ) record is greater than or equal to 330 . among these rows , select the rows whose snatch record is less than 150 . there is only one such row in the table . the name record of this unqiue row is juan quiterio ( dom ) .
Output: and { only { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } } ; eq { hop { filter_less { filter_greater_eq { all_rows ; total ( kg ) ; 330 } ; snatch ; 150 } ; name } ; juan quiterio ( dom ) } }
for the winner records of all rows , most of them fuzzily match to new york giants .
Output: most_eq { all_rows ; winner ; new york giants }
for the event records of all rows , most of them fuzzily match to freestyle .
Output: most_eq { all_rows ; event ; freestyle }
the average of the crowd record of all rows is 13916 .
Output: round_eq { avg { all_rows ; crowd } ; 13916 }
select the rows whose manner of departure record fuzzily matches to end of contract . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; manner of departure ; end of contract } } ; 2 }
select the row whose attendance record of all rows is maximum . the opponent record of this row is cleveland browns .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; cleveland browns }
select the rows whose institution record fuzzily matches to university of leicester . take the established record of this row . select the rows whose institution record fuzzily matches to university of east anglia . take the established record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; institution ; university of leicester } ; established } ; hop { filter_eq { all_rows ; institution ; university of east anglia } ; established } }
for the league records of all rows , most of them fuzzily match to segunda división b .
Output: most_eq { all_rows ; league ; segunda división b }
select the rows whose first audition date record fuzzily matches to september . there is only one such row in the table .
Output: only { filter_eq { all_rows ; first audition date ; september } }
for the to par records of all rows , most of them are greater than 0 .
Output: most_greater { all_rows ; to par ; 0 }
for the high points records of all rows , most of them fuzzily match to pierce .
Output: most_eq { all_rows ; high points ; pierce }
select the rows whose venue record fuzzily matches to stuttgart , west germany . take the year record of this row . select the rows whose venue record fuzzily matches to seoul , south korea . take the year record of this row . the second record is 2 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; venue ; stuttgart , west germany } ; year } ; hop { filter_eq { all_rows ; venue ; seoul , south korea } ; year } } ; -2 years }
select the rows whose relationship with predecessor record fuzzily matches to and . there is only one such row in the table . the name record of this unqiue row is maximilian .
Output: and { only { filter_eq { all_rows ; relationship with predecessor ; and } } ; eq { hop { filter_eq { all_rows ; relationship with predecessor ; and } ; name } ; maximilian } }
the average of the wkts record of all rows is 12.91 .
Output: round_eq { avg { all_rows ; wkts } ; 12.91 }
the average of the race 1 ( pts ) record of all rows is 17.8 .
Output: round_eq { avg { all_rows ; race 1 ( pts ) } ; 17.8 }
select the rows whose 1st leg record fuzzily matches to 0-3 . there is only one such row in the table . the team 1 record of this unqiue row is blooming . the team 2 record of this unqiue row is river plate .
Output: and { only { filter_eq { all_rows ; 1st leg ; 0-3 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-3 } ; team 1 } ; blooming } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-3 } ; team 2 } ; river plate } } }
the average of the wins record of all rows is 3.5 .
Output: round_eq { avg { all_rows ; wins } ; 3.5 }
select the rows whose administrative district record fuzzily matches to mullaitivu . take the land area ( km 2 ) record of this row . select the rows whose administrative district record fuzzily matches to mannar . take the land area ( km 2 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; administrative district ; mullaitivu } ; land area ( km 2 ) } ; hop { filter_eq { all_rows ; administrative district ; mannar } ; land area ( km 2 ) } }
for the result records of all rows , most of them fuzzily match to re-elected .
Output: most_eq { all_rows ; result ; re-elected }
select the rows whose score record fuzzily matches to 11:0 . there is only one such row in the table . the competition record of this unqiue row is 1996 asian championships .
Output: and { only { filter_eq { all_rows ; score ; 11:0 } } ; eq { hop { filter_eq { all_rows ; score ; 11:0 } ; competition } ; 1996 asian championships } }
select the rows whose goal difference record is less than 0 . the number of such rows is 5 .
Output: eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 5 }
select the rows whose points record is less than 20 . for the lost records of these rows , most of them are less than 10 .
Output: most_less { filter_less { all_rows ; points ; 20 } ; lost ; 10 }
select the rows whose rank record is equal to 14 . there is only one such row in the table . the fourth place record of this unqiue row is 2 ( 2010 , 2011 ) .
Output: and { only { filter_eq { all_rows ; rank ; 14 } } ; eq { hop { filter_eq { all_rows ; rank ; 14 } ; fourth place } ; 2 ( 2010 , 2011 ) } }
select the rows whose nationality record fuzzily matches to portuguese . there is only one such row in the table . the elector record of this unqiue row is joão pedro julião .
Output: and { only { filter_eq { all_rows ; nationality ; portuguese } } ; eq { hop { filter_eq { all_rows ; nationality ; portuguese } ; elector } ; joão pedro julião } }
select the rows whose player record fuzzily matches to john burch . take the round record of this row . select the rows whose player record fuzzily matches to kendall trainor . take the round record of this row . the second record is 1 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; john burch } ; round } ; hop { filter_eq { all_rows ; player ; kendall trainor } ; round } } ; -1 }
the average of the capacity record of all rows is 22800 .
Output: round_eq { avg { all_rows ; capacity } ; 22800 }
select the row whose attendance record of all rows is maximum . the date record of this row is october 22 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; october 22 }
select the rows whose player record fuzzily matches to bernie fryer . take the years for jazz record of this row . select the rows whose player record fuzzily matches to derek fisher . take the years for jazz record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; bernie fryer } ; years for jazz } ; hop { filter_eq { all_rows ; player ; derek fisher } ; years for jazz } }
the average of the points record of all rows is 11 .
Output: round_eq { avg { all_rows ; points } ; 11 }
select the row whose goals scored record of all rows is 2nd minimum . the team record of this row is once municipal .
Output: eq { hop { nth_argmin { all_rows ; goals scored ; 2 } ; team } ; once municipal }
select the rows whose to par record is equal to -5 . there is only one such row in the table . the player record of this unqiue row is ben hogan .
Output: and { only { filter_eq { all_rows ; to par ; -5 } } ; eq { hop { filter_eq { all_rows ; to par ; -5 } ; player } ; ben hogan } }
select the rows whose location attendance record fuzzily matches to rose garden . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location attendance ; rose garden } } ; 3 }
for the unit records of all rows , most of them fuzzily match to colorado .
Output: most_eq { all_rows ; unit ; colorado }
select the row whose attendance record of all rows is maximum . the date record of this row is 19 may 2007 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; 19 may 2007 }
select the rows whose squadron record fuzzily matches to squadron 552 . among these rows , select the rows whose dates record fuzzily matches to 2006-present . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; squadron ; squadron 552 } ; dates ; 2006-present } } ; 1 }
select the rows whose 1st leg record fuzzily matches to 0 - 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; 1st leg ; 0 - 0 } } ; 2 }
select the rows whose bronze record fuzzily matches to jerzy greszkiewicz . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; bronze ; jerzy greszkiewicz } } ; 2 }
select the rows whose class record fuzzily matches to nuttall . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; class ; nuttall } } ; 2 }
select the row whose race record of all rows is 2nd minimum . the season record of this row is 2008 .
Output: eq { hop { nth_argmin { all_rows ; race ; 2 } ; season } ; 2008 }
the average of the pieces record of all rows is 271.6 .
Output: round_eq { avg { all_rows ; pieces } ; 271.6 }
for the affiliation records of all rows , most of them fuzzily match to private .
Output: most_eq { all_rows ; affiliation ; private }
select the rows whose date record fuzzily matches to december . for the final score records of these rows , most of them are greater than or equal to 20 for both teams .
Output: most_greater_eq { filter_eq { all_rows ; date ; december } ; final score ; 20 for both teams }
select the rows whose home / away record fuzzily matches to home . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; home / away ; home } } ; 8 }
select the rows whose city record fuzzily matches to seongnam . take the population ( 2010 ) record of this row . select the rows whose city record fuzzily matches to namyangju . take the population ( 2010 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; city ; seongnam } ; population ( 2010 ) } ; hop { filter_eq { all_rows ; city ; namyangju } ; population ( 2010 ) } }
the average of the area ( km square ) record of all rows is 198.448 .
Output: round_eq { avg { all_rows ; area ( km square ) } ; 198.448 }
for the enrollment records of all rows , most of them are greater than 1000 .
Output: most_greater { all_rows ; enrollment ; 1000 }
select the rows whose winning driver record fuzzily matches to elio de angelis . take the date record of this row . select the rows whose winning driver record fuzzily matches to niki lauda . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; winning driver ; elio de angelis } ; date } ; hop { filter_eq { all_rows ; winning driver ; niki lauda } ; date } }
select the rows whose game record fuzzily matches to 63 . take the points record of this row . select the rows whose game record fuzzily matches to 57 . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; game ; 63 } ; points } ; hop { filter_eq { all_rows ; game ; 57 } ; points } }
the 1st minimum year record of all rows is 1942 . the opponent in the final record of the row with 1st minimum year record is don budge .
Output: and { eq { nth_min { all_rows ; year ; 1 } ; 1942 } ; eq { hop { nth_argmin { all_rows ; year ; 1 } ; opponent in the final } ; don budge } }
select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is jack fields .
Output: and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; retired } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; result ; retired } ; incumbent } ; jack fields } }
select the rows whose crowd record is greater than 10000 . among these rows , select the rows whose away team score record is greater than or equal to 10 . the number of such rows is 2 .
Output: eq { count { filter_greater_eq { filter_greater { all_rows ; crowd ; 10000 } ; away team score ; 10 } } ; 2 }
select the rows whose position record fuzzily matches to catcher . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; catcher } } ; 2 }
the average of the score record of all rows is 97.3 .
Output: round_eq { avg { all_rows ; score } ; 97.3 }
select the rows whose tag team record fuzzily matches to rosey and the hurricane . take the eliminated record of this row . select the rows whose tag team record fuzzily matches to jindrak and cade . take the eliminated record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; tag team ; rosey and the hurricane } ; eliminated } ; hop { filter_eq { all_rows ; tag team ; jindrak and cade } ; eliminated } }
for the original beechwood bunny tale / source material records of all rows , most of them fuzzily match to teleplay by valérie baranski .
Output: most_eq { all_rows ; original beechwood bunny tale / source material ; teleplay by valérie baranski }
select the rows whose molecule record fuzzily matches to protein . take the percent of mass record of this row . select the rows whose molecule record fuzzily matches to other s inorganic . take the percent of mass record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; molecule ; protein } ; percent of mass } ; hop { filter_eq { all_rows ; molecule ; other s inorganic } ; percent of mass } }
select the rows whose floors record is equal to 32 . there is only one such row in the table . the name record of this unqiue row is at & t building .
Output: and { only { filter_eq { all_rows ; floors ; 32 } } ; eq { hop { filter_eq { all_rows ; floors ; 32 } ; name } ; at & t building } }
select the rows whose date record fuzzily matches to october . the sum of the attendance record of these rows is 245,093 .
Output: round_eq { sum { filter_eq { all_rows ; date ; october } ; attendance } ; 245,093 }
select the rows whose party record fuzzily matches to democratic . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; party ; democratic } } ; 7 }
select the rows whose club record fuzzily matches to malmö ff . take the allsvenskan titles record of this row . select the rows whose club record fuzzily matches to ifk göteborg . take the allsvenskan titles record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; club ; malmö ff } ; allsvenskan titles } ; hop { filter_eq { all_rows ; club ; ifk göteborg } ; allsvenskan titles } }
select the row whose original air date record of all rows is 2nd maximum . the title record of this row is the understudy .
Output: eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; title } ; the understudy }
select the rows whose w / l record fuzzily matches to win . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; w / l ; win } } ; 9 }
the average of the score record of all rows is 1.8 .
Output: round_eq { avg { all_rows ; score } ; 1.8 }
select the rows whose original air date record fuzzily matches to may . for the directed by records of these rows , most of them fuzzily match to gary halvorson .
Output: most_eq { filter_eq { all_rows ; original air date ; may } ; directed by ; gary halvorson }
select the rows whose incumbent record fuzzily matches to lane evans . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to phil crane . take the first elected record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; incumbent ; lane evans } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; phil crane } ; first elected } }
select the rows whose innings record is equal to 8 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; innings ; 8 } } ; 3 }
for the earnings records of all rows , most of them are greater than 10000000 .
Output: most_greater { all_rows ; earnings ; 10000000 }
the average of the points record of all rows is 68.75 .
Output: round_eq { avg { all_rows ; points } ; 68.75 }
select the rows whose film record fuzzily matches to furnace four . take the award record of this row . select the rows whose film record fuzzily matches to the hardest part . take the award record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; film ; furnace four } ; award } ; hop { filter_eq { all_rows ; film ; the hardest part } ; award } }
select the rows whose ch 1 record fuzzily matches to 59 ( uhf ) . take the on - air date record of this row . select the rows whose ch 1 record fuzzily matches to 32 ( uhf ) . take the on - air date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; ch 1 ; 59 ( uhf ) } ; on - air date } ; hop { filter_eq { all_rows ; ch 1 ; 32 ( uhf ) } ; on - air date } }
select the rows whose visitor record fuzzily matches to chicago black hawks . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; visitor ; chicago black hawks } } ; 3 }
select the rows whose partner record fuzzily matches to igor sijsling . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; partner ; igor sijsling } } ; 2 }
select the rows whose gold record is equal to 3 . select the row whose silver record of these rows is maximum . the nation record of this row is china .
Output: eq { hop { argmax { filter_eq { all_rows ; gold ; 3 } ; silver } ; nation } ; china }
select the rows whose class record fuzzily matches to d . among these rows , select the rows whose frequency mhz record is equal to 98.7 . there is only one such row in the table . the city of license record of this unqiue row is sparta , georgia .
Output: and { only { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } } ; eq { hop { filter_eq { filter_eq { all_rows ; class ; d } ; frequency mhz ; 98.7 } ; city of license } ; sparta , georgia } }
select the row whose stage record of all rows is 2nd minimum . the year record of this row is 2003 .
Output: eq { hop { nth_argmin { all_rows ; stage ; 2 } ; year } ; 2003 }
select the rows whose frequency record fuzzily matches to fm . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; frequency ; fm } } ; 9 }
select the rows whose episode title record fuzzily matches to remember . take the original air date record of this row . select the rows whose episode title record fuzzily matches to big brotherly love . take the original air date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; episode title ; remember } ; original air date } ; hop { filter_eq { all_rows ; episode title ; big brotherly love } ; original air date } }
select the rows whose successor record fuzzily matches to john parker hale ( r ) . take the date of successors formal installation record of this row . select the rows whose successor record fuzzily matches to william bigler ( d ) . take the date of successors formal installation record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; successor ; john parker hale ( r ) } ; date of successors formal installation } ; hop { filter_eq { all_rows ; successor ; william bigler ( d ) } ; date of successors formal installation } }
the average of the electors record of all rows is 50.85 .
Output: round_eq { avg { all_rows ; electors } ; 50.85 }
select the row whose votes record of all rows is 3rd maximum . the candidate record of this row is pat martin .
Output: eq { hop { nth_argmax { all_rows ; votes ; 3 } ; candidate } ; pat martin }
select the rows whose attendance record is greater than 22288 . there is only one such row in the table . the date record of this unqiue row is march 14 .
Output: and { only { filter_greater { all_rows ; attendance ; 22288 } } ; eq { hop { filter_greater { all_rows ; attendance ; 22288 } ; date } ; march 14 } }
select the row whose finish record of all rows is 2nd minimum . the year record of this row is 1967 .
Output: eq { hop { nth_argmin { all_rows ; finish ; 2 } ; year } ; 1967 }
for the date records of all rows , all of them fuzzily match to february .
Output: all_eq { all_rows ; date ; february }
the sum of the race 1 ( pts ) record of all rows is 128 .
Output: round_eq { sum { all_rows ; race 1 ( pts ) } ; 128 }
select the rows whose isbn us record fuzzily matches to unknown . there is only one such row in the table . the title record of this unqiue row is mission to venus .
Output: and { only { filter_eq { all_rows ; isbn us ; unknown } } ; eq { hop { filter_eq { all_rows ; isbn us ; unknown } ; title } ; mission to venus } }
for the engine records of all rows , most of them fuzzily match to v8 .
Output: most_eq { all_rows ; engine ; v8 }
for the music records of all rows , most of them fuzzily match to sukshinder shinda .
Output: most_eq { all_rows ; music ; sukshinder shinda }
select the row whose asts record of all rows is maximum . the player record of this row is gail goodrich .
Output: eq { hop { argmax { all_rows ; asts } ; player } ; gail goodrich }
select the rows whose margin of victory record fuzzily matches to 4 strokes . there is only one such row in the table . the tournament record of this unqiue row is barclays scottish open .
Output: and { only { filter_eq { all_rows ; margin of victory ; 4 strokes } } ; eq { hop { filter_eq { all_rows ; margin of victory ; 4 strokes } ; tournament } ; barclays scottish open } }
select the rows whose date record fuzzily matches to january 29 . take the high points record of this row . select the rows whose date record fuzzily matches to january 1 . take the high points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; january 29 } ; high points } ; hop { filter_eq { all_rows ; date ; january 1 } ; high points } }
select the row whose no record of all rows is 3rd maximum . the player record of this row is ashraf amaya .
Output: eq { hop { nth_argmax { all_rows ; no ; 3 } ; player } ; ashraf amaya }
select the rows whose championship record fuzzily matches to wimbledon . for the outcome records of these rows , most of them fuzzily match to winner .
Output: most_eq { filter_eq { all_rows ; championship ; wimbledon } ; outcome ; winner }
select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } }
select the rows whose opponent in the final record fuzzily matches to mark selby . there is only one such row in the table . the championship record of this unqiue row is uk championship .
Output: and { only { filter_eq { all_rows ; opponent in the final ; mark selby } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; mark selby } ; championship } ; uk championship } }
select the rows whose leading scorer record fuzzily matches to ricky davis . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; leading scorer ; ricky davis } } ; 3 }
select the rows whose season record fuzzily matches to 1978 - 79 . take the goals for record of this row . select the rows whose season record fuzzily matches to 1975 - 76 . take the goals for record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 1978 - 79 } ; goals for } ; hop { filter_eq { all_rows ; season ; 1975 - 76 } ; goals for } }
select the row whose time record of all rows is minimum . the athlete record of this row is jean - baptiste macquet , adrien hardy .
Output: eq { hop { argmin { all_rows ; time } ; athlete } ; jean - baptiste macquet , adrien hardy }
select the row whose saturated fat record of all rows is 4th maximum . the record of this row is vegetable shortening ( hydrogenated ) .
Output: eq { hop { nth_argmax { all_rows ; saturated fat ; 4 } ; } ; vegetable shortening ( hydrogenated ) }
select the rows whose wins record is equal to 2 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; wins ; 2 } } ; 2 }
select the rows whose year record fuzzily matches to 1993 . take the laps record of this row . select the rows whose year record fuzzily matches to 1982 . take the laps record of this row . the first record is greater than the second record . the laps record of the first row is 374 . the laps record of the second row is 92 .
Output: and { greater { hop { filter_eq { all_rows ; year ; 1993 } ; laps } ; hop { filter_eq { all_rows ; year ; 1982 } ; laps } } ; and { eq { hop { filter_eq { all_rows ; year ; 1993 } ; laps } ; 374 } ; eq { hop { filter_eq { all_rows ; year ; 1982 } ; laps } ; 92 } } }
select the row whose points record of all rows is 1st maximum . the club record of this row is pentyrch rfc .
Output: eq { hop { nth_argmax { all_rows ; points ; 1 } ; club } ; pentyrch rfc }
select the row whose height record of all rows is maximum . the name record of this row is rodrigo santana .
Output: eq { hop { argmax { all_rows ; height } ; name } ; rodrigo santana }
select the row whose score record of all rows is 3rd maximum . the home team record of this row is sydney spirit .
Output: eq { hop { nth_argmax { all_rows ; score ; 3 } ; home team } ; sydney spirit }
the 3rd minimum date record of all rows is sep 14 , 1997 .
Output: eq { nth_min { all_rows ; date ; 3 } ; sep 14 , 1997 }
select the rows whose opponent record fuzzily matches to chicago blackhawks . there is only one such row in the table . the game record of this unqiue row is 36 .
Output: and { only { filter_eq { all_rows ; opponent ; chicago blackhawks } } ; eq { hop { filter_eq { all_rows ; opponent ; chicago blackhawks } ; game } ; 36 } }
select the rows whose crowd record is less than 10000 . there is only one such row in the table . the home team record of this unqiue row is essendon . the away team record of this unqiue row is hawthorn .
Output: and { only { filter_less { all_rows ; crowd ; 10000 } } ; and { eq { hop { filter_less { all_rows ; crowd ; 10000 } ; home team } ; essendon } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; away team } ; hawthorn } } }
select the row whose events record of all rows is maximum . the tournament record of this row is the open championship .
Output: eq { hop { argmax { all_rows ; events } ; tournament } ; the open championship }
select the rows whose eliminated by record fuzzily matches to evolution . there is only one such row in the table . the tag team record of this unqiue row is dudley boyz .
Output: and { only { filter_eq { all_rows ; eliminated by ; evolution } } ; eq { hop { filter_eq { all_rows ; eliminated by ; evolution } ; tag team } ; dudley boyz } }
for the played records of all rows , most of them are equal to 22 .
Output: most_eq { all_rows ; played ; 22 }
select the rows whose entrant record fuzzily matches to spirit racing . there is only one such row in the table . the year record of this unqiue row is 1983 .
Output: and { only { filter_eq { all_rows ; entrant ; spirit racing } } ; eq { hop { filter_eq { all_rows ; entrant ; spirit racing } ; year } ; 1983 } }
select the rows whose date record fuzzily matches to november 10 . take the leading scorer record of this row . select the rows whose date record fuzzily matches to november 22 . take the leading scorer record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; november 10 } ; leading scorer } ; hop { filter_eq { all_rows ; date ; november 22 } ; leading scorer } }
for the team records of all rows , most of them fuzzily match to holden racing team .
Output: most_eq { all_rows ; team ; holden racing team }
select the row whose percentage record of all rows is maximum . the driver record of this row is juan manuel fangio .
Output: eq { hop { argmax { all_rows ; percentage } ; driver } ; juan manuel fangio }
select the rows whose designer , year record is greater than 2000 . there is only one such row in the table . the name record of this unqiue row is pacific dunes . the state record of this unqiue row is oregon .
Output: and { only { filter_greater { all_rows ; designer , year ; 2000 } } ; and { eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; name } ; pacific dunes } ; eq { hop { filter_greater { all_rows ; designer , year ; 2000 } ; state } ; oregon } } }
the average of the power record of all rows is 8260 .
Output: round_eq { avg { all_rows ; power } ; 8260 }
the sum of the total dismissals record of all rows is 463 .
Output: round_eq { sum { all_rows ; total dismissals } ; 463 }
select the row whose attendance record of all rows is 2nd maximum . the date record of this row is may 22 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; may 22 }
select the rows whose class record fuzzily matches to f - 20 . take the quantity made record of this row . select the rows whose class record fuzzily matches to f - 22 . take the quantity made record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; class ; f - 20 } ; quantity made } ; hop { filter_eq { all_rows ; class ; f - 22 } ; quantity made } }
select the row whose yards record of all rows is 2nd maximum . the season record of this row is 2009 .
Output: eq { hop { nth_argmax { all_rows ; yards ; 2 } ; season } ; 2009 }
the average of the crowd record of all rows is 15760 .
Output: round_eq { avg { all_rows ; crowd } ; 15760 }
for the ihsaa class records of all rows , all of them fuzzily match to 4a .
Output: all_eq { all_rows ; ihsaa class ; 4a }
the 2nd minimum number record of all rows is 18 .
Output: eq { nth_min { all_rows ; number ; 2 } ; 18 }
select the row whose years for rockets record of all rows is minimum . the player record of this row is perry , curtis curtis perry .
Output: eq { hop { argmin { all_rows ; years for rockets } ; player } ; perry , curtis curtis perry }
select the row whose date sent record of all rows is minimum . the constellation record of this row is delphinus .
Output: eq { hop { argmin { all_rows ; date sent } ; constellation } ; delphinus }
select the rows whose nationality record fuzzily matches to canada . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nationality ; canada } } ; 2 }
for the date records of all rows , all of them fuzzily match to 16 july 1949 .
Output: all_eq { all_rows ; date ; 16 july 1949 }
select the rows whose style record fuzzily matches to country - western two - step . there is only one such row in the table . the couple record of this unqiue row is kherington payne mark kanemura .
Output: and { only { filter_eq { all_rows ; style ; country - western two - step } } ; eq { hop { filter_eq { all_rows ; style ; country - western two - step } ; couple } ; kherington payne mark kanemura } }
select the rows whose event record fuzzily matches to wc beijing . the sum of the rank points record of these rows is 23 .
Output: round_eq { sum { filter_eq { all_rows ; event ; wc beijing } ; rank points } ; 23 }
select the rows whose player record fuzzily matches to craig stadler . take the money record of this row . select the rows whose player record fuzzily matches to fred couples . take the money record of this row . the first record is 5863 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; craig stadler } ; money } ; hop { filter_eq { all_rows ; player ; fred couples } ; money } } ; 5863 }
for the date records of all rows , all of them fuzzily match to december .
Output: all_eq { all_rows ; date ; december }
select the rows whose facility record fuzzily matches to coyote ridge corrections center ( crcc ) . take the capacity record of this row . select the rows whose facility record fuzzily matches to monroe correctional complex ( mcc ) . take the capacity record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; facility ; coyote ridge corrections center ( crcc ) } ; capacity } ; hop { filter_eq { all_rows ; facility ; monroe correctional complex ( mcc ) } ; capacity } }
select the row whose tonnage record of all rows is 2nd maximum . the name record of this row is ocean rover .
Output: eq { hop { nth_argmax { all_rows ; tonnage ; 2 } ; name } ; ocean rover }
the sum of the gold record of all rows is 8 .
Output: round_eq { sum { all_rows ; gold } ; 8 }
select the row whose location attendance record of all rows is 1st maximum . the team record of this row is boston .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; boston }
select the rows whose tv station ( operator ) record fuzzily matches to modernine tv . take the 2005 record of this row . select the rows whose tv station ( operator ) record fuzzily matches to nbt . take the 2005 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tv station ( operator ) ; modernine tv } ; 2005 } ; hop { filter_eq { all_rows ; tv station ( operator ) ; nbt } ; 2005 } }
for the date records of all rows , all of them fuzzily match to 9 august 1952 .
Output: all_eq { all_rows ; date ; 9 august 1952 }
the sum of the points record of all rows is 612 .
Output: round_eq { sum { all_rows ; points } ; 612 }
select the row whose foundation record of all rows is maximum . the organization record of this row is radiotelevisión del principado de asturias ( rtpa ) .
Output: eq { hop { argmax { all_rows ; foundation } ; organization } ; radiotelevisión del principado de asturias ( rtpa ) }
select the rows whose player record fuzzily matches to nick price . take the wins record of this row . select the rows whose player record fuzzily matches to fred couples . take the wins record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; nick price } ; wins } ; hop { filter_eq { all_rows ; player ; fred couples } ; wins } }
select the rows whose tournament record fuzzily matches to world cup . there is only one such row in the table . the year record of this unqiue row is 1998 .
Output: and { only { filter_eq { all_rows ; tournament ; world cup } } ; eq { hop { filter_eq { all_rows ; tournament ; world cup } ; year } ; 1998 } }
select the rows whose official name record fuzzily matches to saint george . take the population record of this row . select the rows whose official name record fuzzily matches to saint andrews . take the population record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; official name ; saint george } ; population } ; hop { filter_eq { all_rows ; official name ; saint andrews } ; population } }
select the row whose start record of all rows is minimum . the year record of this row is 1936 .
Output: eq { hop { argmin { all_rows ; start } ; year } ; 1936 }
the average of the attendance record of all rows is 16013 .
Output: round_eq { avg { all_rows ; attendance } ; 16013 }
the sum of the votes record of all rows is 65680 .
Output: round_eq { sum { all_rows ; votes } ; 65680 }
select the rows whose directed by record fuzzily matches to patrick duffy . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; directed by ; patrick duffy } } ; 8 }
select the row whose bronze record of all rows is 2nd maximum . the nation record of this row is netherlands .
Output: eq { hop { nth_argmax { all_rows ; bronze ; 2 } ; nation } ; netherlands }
select the rows whose opponent record fuzzily matches to jacksonville jaguars . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; jacksonville jaguars } } ; 2 }
select the rows whose runner - up record fuzzily matches to asec abidjan . among these rows , select the rows whose score record is greater than 5 . there is only one such row in the table . the winner record of this unqiue row is us gorée .
Output: and { only { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } } ; eq { hop { filter_greater { filter_eq { all_rows ; runner - up ; asec abidjan } ; score ; 5 } ; winner } ; us gorée } }
select the row whose overall record of all rows is 3rd minimum . the name record of this row is james hamilton .
Output: eq { hop { nth_argmin { all_rows ; overall ; 3 } ; name } ; james hamilton }
select the rows whose hometown record fuzzily matches to tx . there is only one such row in the table . the player record of this unqiue row is kerry wood .
Output: and { only { filter_eq { all_rows ; hometown ; tx } } ; eq { hop { filter_eq { all_rows ; hometown ; tx } ; player } ; kerry wood } }
the average of the distance ( km ) record of all rows is 7.1 .
Output: round_eq { avg { all_rows ; distance ( km ) } ; 7.1 }
for the away captain records of all rows , all of them fuzzily match to prosper utseya .
Output: all_eq { all_rows ; away captain ; prosper utseya }
select the row whose foundation record of all rows is 2nd minimum . the english name record of this row is japan coast guard academy .
Output: eq { hop { nth_argmin { all_rows ; foundation ; 2 } ; english name } ; japan coast guard academy }
the minimum closed record of all rows is 1983 . the station record of the row with superlative closed record is fairmount avenue .
Output: and { eq { min { all_rows ; closed } ; 1983 } ; eq { hop { argmin { all_rows ; closed } ; station } ; fairmount avenue } }
select the rows whose semifinalists record is equal to 1 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; semifinalists ; 1 } } ; 3 }
select the rows whose club record fuzzily matches to real murcia . take the wins record of this row . select the rows whose club record fuzzily matches to jerez cd . take the wins record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; club ; real murcia } ; wins } ; hop { filter_eq { all_rows ; club ; jerez cd } ; wins } }
select the rows whose title record fuzzily matches to king . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; title ; king } } ; 4 }
for the outcome records of all rows , most of them fuzzily match to runner - up .
Output: most_eq { all_rows ; outcome ; runner - up }
select the rows whose primary conference when joining the msfa record fuzzily matches to whac . there is only one such row in the table . the institution record of this unqiue row is trine university .
Output: and { only { filter_eq { all_rows ; primary conference when joining the msfa ; whac } } ; eq { hop { filter_eq { all_rows ; primary conference when joining the msfa ; whac } ; institution } ; trine university } }
select the rows whose score record is greater than 280 . there is only one such row in the table . the year record of this unqiue row is 1998 .
Output: and { only { filter_greater { all_rows ; score ; 280 } } ; eq { hop { filter_greater { all_rows ; score ; 280 } ; year } ; 1998 } }
for the score records of all rows , all of them fuzzily match to 1 goal .
Output: all_eq { all_rows ; score ; 1 goal }
the average of the attendance record of all rows is 43800 .
Output: round_eq { avg { all_rows ; attendance } ; 43800 }
the average of the candidates record of all rows is 76.075 % .
Output: round_eq { avg { all_rows ; candidates } ; 76.075 % }
select the rows whose language record fuzzily matches to french . among these rows , select the rows whose points record is equal to 14 . there is only one such row in the table . the artist record of this unqiue row is tonia .
Output: and { only { filter_eq { filter_eq { all_rows ; language ; french } ; points ; 14 } } ; eq { hop { filter_eq { filter_eq { all_rows ; language ; french } ; points ; 14 } ; artist } ; tonia } }
select the row whose date record of all rows is 5th minimum . the venue record of this row is guangzhou .
Output: eq { hop { nth_argmin { all_rows ; date ; 5 } ; venue } ; guangzhou }
the average of the round record of all rows is 1.95 .
Output: round_eq { avg { all_rows ; round } ; 1.95 }
for the gross tonnage records of all rows , most of them are greater than 2000 .
Output: most_greater { all_rows ; gross tonnage ; 2000 }
select the row whose score record of all rows is maximum . the player record of this row is jason gillespie .
Output: eq { hop { argmax { all_rows ; score } ; player } ; jason gillespie }
for the distance ( miles ) records of all rows , all of them fuzzily match to 1-3 / 16 .
Output: all_eq { all_rows ; distance ( miles ) ; 1-3 / 16 }
select the rows whose institution record fuzzily matches to mercy college . take the enrollment record of this row . select the rows whose institution record fuzzily matches to dowling college . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; institution ; mercy college } ; enrollment } ; hop { filter_eq { all_rows ; institution ; dowling college } ; enrollment } }
for the date records of all rows , most of them fuzzily match to 26 january 1985 .
Output: most_eq { all_rows ; date ; 26 january 1985 }
select the rows whose date record fuzzily matches to 09 / 30 / 1933 . take the result record of this row . select the rows whose date record fuzzily matches to 11 / 04 / 1933 . take the result record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 09 / 30 / 1933 } ; result } ; hop { filter_eq { all_rows ; date ; 11 / 04 / 1933 } ; result } }
select the row whose score record of all rows is 1st maximum . the ground record of this row is humber college north .
Output: eq { hop { nth_argmax { all_rows ; score ; 1 } ; ground } ; humber college north }
for the directed by records of all rows , all of them fuzzily match to ted wass .
Output: all_eq { all_rows ; directed by ; ted wass }
for the venue records of all rows , most of them fuzzily match to london .
Output: most_eq { all_rows ; venue ; london }
select the rows whose status record fuzzily matches to alive after exiting the cube . there is only one such row in the table . the name record of this unqiue row is kazan .
Output: and { only { filter_eq { all_rows ; status ; alive after exiting the cube } } ; eq { hop { filter_eq { all_rows ; status ; alive after exiting the cube } ; name } ; kazan } }
select the row whose floors record of all rows is maximum . the name record of this row is bank one tower .
Output: eq { hop { argmax { all_rows ; floors } ; name } ; bank one tower }
select the rows whose country record fuzzily matches to sweden . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; country ; sweden } } ; 2 }
select the rows whose chassis record fuzzily matches to lola t370 . there is only one such row in the table . the year record of this unqiue row is 1974 .
Output: and { only { filter_eq { all_rows ; chassis ; lola t370 } } ; eq { hop { filter_eq { all_rows ; chassis ; lola t370 } ; year } ; 1974 } }
for the game site records of all rows , most of them fuzzily match to mile high stadium .
Output: most_eq { all_rows ; game site ; mile high stadium }
select the row whose time record of all rows is 2nd maximum . the wrestler record of this row is chris masters .
Output: eq { hop { nth_argmax { all_rows ; time ; 2 } ; wrestler } ; chris masters }
select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is melbourne .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; melbourne }
the average of the against record of all rows is 11.6 .
Output: round_eq { avg { all_rows ; against } ; 11.6 }
select the rows whose elevated record fuzzily matches to 1117 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; elevated ; 1117 } } ; 3 }
select the rows whose incumbent record fuzzily matches to robert taylor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to mark alexander . take the first elected record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; incumbent ; robert taylor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; mark alexander } ; first elected } }