niryuu/sni-each-converted huggingface dataset:
select the rows whose date record is greater than december 16 . the maximum high points record of these rows is zach randolph ( 34 ) .
Output: eq { max { filter_greater { all_rows ; date ; december 16 } ; high points } ; zach randolph ( 34 ) }
select the rows whose school / club team record fuzzily matches to maynard evans hs . there is only one such row in the table . the player record of this unqiue row is darryl dawkins .
Output: and { only { filter_eq { all_rows ; school / club team ; maynard evans hs } } ; eq { hop { filter_eq { all_rows ; school / club team ; maynard evans hs } ; player } ; darryl dawkins } }
for the engine records of all rows , all of them fuzzily match to motori moderni tipo 615 - 90 1.5 v6 t .
Output: all_eq { all_rows ; engine ; motori moderni tipo 615 - 90 1.5 v6 t }
the sum of the money record of all rows is 4769400 .
Output: round_eq { sum { all_rows ; money } ; 4769400 }
select the rows whose team record fuzzily matches to trail blazers . select the row whose date record of these rows is 2nd minimum . the score record of this row is w 99 - 95 ( ot ) .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; team ; trail blazers } ; date ; 2 } ; score } ; w 99 - 95 ( ot ) }
select the rows whose home team score record is greater than 14 . among these rows , select the rows whose crowd record is greater than 9999 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; home team score ; 14 } ; crowd ; 9999 } } ; 2 }
select the row whose attendance record of all rows is 1st maximum . the visitor record of this row is anaheim .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; visitor } ; anaheim }
select the row whose score record of all rows is maximum . the score record of this row is 3 - 2 .
Output: eq { hop { argmax { all_rows ; score } ; score } ; 3 - 2 }
select the rows whose venue record fuzzily matches to melbourne cricket ground . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; venue ; melbourne cricket ground } } ; 2 }
select the rows whose affiliation record fuzzily matches to pbs . there is only one such row in the table . the call sign record of this unqiue row is kawe .
Output: and { only { filter_eq { all_rows ; affiliation ; pbs } } ; eq { hop { filter_eq { all_rows ; affiliation ; pbs } ; call sign } ; kawe } }
select the rows whose incumbent record fuzzily matches to nathan deal . take the elected record of this row . select the rows whose incumbent record fuzzily matches to charlie norwood . take the elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; nathan deal } ; elected } ; hop { filter_eq { all_rows ; incumbent ; charlie norwood } ; elected } }
select the rows whose outcome record fuzzily matches to runner-up . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; outcome ; runner-up } } ; 5 }
select the rows whose position record fuzzily matches to 7th . there is only one such row in the table . the competition record of this unqiue row is 58th national sports festival of japan .
Output: and { only { filter_eq { all_rows ; position ; 7th } } ; eq { hop { filter_eq { all_rows ; position ; 7th } ; competition } ; 58th national sports festival of japan } }
select the rows whose location record fuzzily matches to canada . for the round records of these rows , most of them are equal to 1 .
Output: most_eq { filter_eq { all_rows ; location ; canada } ; round ; 1 }
select the row whose market value ( usd million ) record of all rows is maximum . the name record of this row is microsoft .
Output: eq { hop { argmax { all_rows ; market value ( usd million ) } ; name } ; microsoft }
select the rows whose nationality record fuzzily matches to united states . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nationality ; united states } } ; 2 }
select the rows whose apps record is equal to 12 . there is only one such row in the table . the season record of this unqiue row is 2004 - 2005 .
Output: and { only { filter_eq { all_rows ; apps ; 12 } } ; eq { hop { filter_eq { all_rows ; apps ; 12 } ; season } ; 2004 - 2005 } }
select the rows whose date record fuzzily matches to december . there is only one such row in the table . the region record of this unqiue row is japan .
Output: and { only { filter_eq { all_rows ; date ; december } } ; eq { hop { filter_eq { all_rows ; date ; december } ; region } ; japan } }
select the rows whose season record fuzzily matches to 2005 - 06 . take the wins record of this row . select the rows whose season record fuzzily matches to 2006 - 07 . take the wins record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2005 - 06 } ; wins } ; hop { filter_eq { all_rows ; season ; 2006 - 07 } ; wins } }
select the row whose date record of all rows is minimum . the performer 4 record of this row is ryan stiles .
Output: eq { hop { argmin { all_rows ; date } ; performer 4 } ; ryan stiles }
the sum of the canadian chapters record of all rows is 48 .
Output: round_eq { sum { all_rows ; canadian chapters } ; 48 }
select the rows whose 2011 1h record is greater than 20 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; 2011 1h ; 20 } } ; 2 }
select the rows whose school / club team record fuzzily matches to st john 's . there is only one such row in the table . the player record of this unqiue row is mark jackson .
Output: and { only { filter_eq { all_rows ; school / club team ; st john 's } } ; eq { hop { filter_eq { all_rows ; school / club team ; st john 's } ; player } ; mark jackson } }
select the row whose pick record of all rows is minimum . the pick record of this row is 5 .
Output: eq { hop { argmin { all_rows ; pick } ; pick } ; 5 }
the average of the sales ( billion ) record of all rows is 155.6 .
Output: round_eq { avg { all_rows ; sales ( billion ) } ; 155.6 }
select the rows whose to par record fuzzily matches to +1 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; to par ; +1 } } ; 7 }
select the row whose lost record of all rows is 2nd maximum . the club record of this row is fleur de lys rfc .
Output: eq { hop { nth_argmax { all_rows ; lost ; 2 } ; club } ; fleur de lys rfc }
select the rows whose home team score record is less than 10.0 . among these rows , select the rows whose crowd record is greater than 16000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_less { all_rows ; home team score ; 10.0 } ; crowd ; 16000 } } ; 2 }
the average of the prize record of all rows is 3137419 .
Output: round_eq { avg { all_rows ; prize } ; 3137419 }
the average of the top - 5 record of all rows is .5 .
Output: round_eq { avg { all_rows ; top - 5 } ; .5 }
the average of the attendance record of all rows is 21273 .
Output: round_eq { avg { all_rows ; attendance } ; 21273 }
select the rows whose player record fuzzily matches to jordon flodell . take the round record of this row . select the rows whose player record fuzzily matches to todd fedoruk . take the round record of this row . the second record is 1 larger than the first record . the round record of the first row is 6 . the round record of the second row is 7 .
Output: and { eq { diff { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; player ; jordon flodell } ; round } ; 6 } ; eq { hop { filter_eq { all_rows ; player ; todd fedoruk } ; round } ; 7 } } }
select the row whose rank record of all rows is 2nd minimum . the athlete record of this row is marshevet hooker .
Output: eq { hop { nth_argmin { all_rows ; rank ; 2 } ; athlete } ; marshevet hooker }
select the rows whose transfer window record fuzzily matches to summer . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; transfer window ; summer } } ; 9 }
the average of the attendance record of all rows is 16119 .
Output: round_eq { avg { all_rows ; attendance } ; 16119 }
select the rows whose nation record fuzzily matches to mexico . take the total record of this row . select the rows whose nation record fuzzily matches to canada . take the total record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; mexico } ; total } ; hop { filter_eq { all_rows ; nation ; canada } ; total } }
select the rows whose date record fuzzily matches to florida . there is only one such row in the table .
Output: only { filter_eq { all_rows ; date ; florida } }
select the rows whose winner record fuzzily matches to klavdija bojarskikh . take the winter olympics record of this row . select the rows whose winner record fuzzily matches to toini gustafsson . take the winter olympics record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; winner ; klavdija bojarskikh } ; winter olympics } ; hop { filter_eq { all_rows ; winner ; toini gustafsson } ; winter olympics } }
select the rows whose chassis record fuzzily matches to alfa romeo . 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 ; chassis ; alfa romeo } } ; eq { hop { filter_eq { all_rows ; chassis ; alfa romeo } ; year } ; 1983 } }
select the rows whose proposed record fuzzily matches to 1989 . among these rows , select the rows whose proposed record fuzzily matches to 1989 . there is only one such row in the table . the county record of this unqiue row is new london .
Output: and { only { filter_eq { filter_eq { all_rows ; proposed ; 1989 } ; proposed ; 1989 } } ; eq { hop { filter_eq { filter_eq { all_rows ; proposed ; 1989 } ; proposed ; 1989 } ; county } ; new london } }
select the rows whose championship record fuzzily matches to memphis , us . there is only one such row in the table .
Output: only { filter_eq { all_rows ; championship ; memphis , us } }
select the rows whose year record fuzzily matches to 1986 . take the position record of this row . select the rows whose year record fuzzily matches to 1987 . take the position record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; year ; 1986 } ; position } ; hop { filter_eq { all_rows ; year ; 1987 } ; position } }
select the rows whose county record is arbitrary . the number of such rows is 7 .
Output: eq { count { filter_all { all_rows ; county } } ; 7 }
select the rows whose soap opera record fuzzily matches to as the world turns . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; soap opera ; as the world turns } } ; 3 }
the sum of the total bits record of all rows is 320 .
Output: round_eq { sum { all_rows ; total bits } ; 320 }
select the row whose rank record of all rows is minimum . the player record of this row is steven gerrard .
Output: eq { hop { argmin { all_rows ; rank } ; player } ; steven gerrard }
select the row whose height ( f ) record of all rows is 2nd minimum . the player record of this row is yuval naimi .
Output: eq { hop { nth_argmin { all_rows ; height ( f ) ; 2 } ; player } ; yuval naimi }
select the rows whose school record fuzzily matches to hawaii . there is only one such row in the table . the player record of this unqiue row is darrick branch .
Output: and { only { filter_eq { all_rows ; school ; hawaii } } ; eq { hop { filter_eq { all_rows ; school ; hawaii } ; player } ; darrick branch } }
the 4th minimum week record of all rows is 4 . the opponent record of the row with 4th minimum week record is chicago bears .
Output: and { eq { nth_min { all_rows ; week ; 4 } ; 4 } ; eq { hop { nth_argmin { all_rows ; week ; 4 } ; opponent } ; chicago bears } }
the average of the effective exhaust velocity ( m / s ) record of all rows is 33869 .
Output: round_eq { avg { all_rows ; effective exhaust velocity ( m / s ) } ; 33869 }
select the row whose isolation record of all rows is maximum . the mountain peak record of this row is mount rainier .
Output: eq { hop { argmax { all_rows ; isolation } ; mountain peak } ; mount rainier }
select the rows whose crowd record is greater than 30000 . there is only one such row in the table . the home team record of this unqiue row is melbourne .
Output: and { only { filter_greater { all_rows ; crowd ; 30000 } } ; eq { hop { filter_greater { all_rows ; crowd ; 30000 } ; home team } ; melbourne } }
the average of the bodyweight record of all rows is 100.55 .
Output: round_eq { avg { all_rows ; bodyweight } ; 100.55 }
select the rows whose team record fuzzily matches to new york . take the score record of this row . select the rows whose team record fuzzily matches to detroit . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; new york } ; score } ; hop { filter_eq { all_rows ; team ; detroit } ; score } }
select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is david graham .
Output: and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; david graham } }
select the rows whose model number record fuzzily matches to 800 . the average of the frequency record of these rows is 800 .
Output: round_eq { avg { filter_eq { all_rows ; model number ; 800 } ; frequency } ; 800 }
the average of the draws record of all rows is 9.38 .
Output: round_eq { avg { all_rows ; draws } ; 9.38 }
select the rows whose date record fuzzily matches to september 8 , 1986 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 21 , 1986 . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; september 8 , 1986 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 21 , 1986 } ; attendance } }
select the rows whose race record fuzzily matches to italian grand prix . take the date record of this row . select the rows whose race record fuzzily matches to european grand prix . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; race ; italian grand prix } ; date } ; hop { filter_eq { all_rows ; race ; european grand prix } ; date } }
select the rows whose west lancashire record is equal to 4 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; west lancashire ; 4 } } ; 2 }
select the rows whose meas num record is arbitrary . the number of such rows is 11 .
Output: eq { count { filter_all { all_rows ; meas num } } ; 11 }
for the original air date records of all rows , all of them fuzzily match to 2013 .
Output: all_eq { all_rows ; original air date ; 2013 }
select the rows whose status record fuzzily matches to display only . there is only one such row in the table . the number record of this unqiue row is 1 .
Output: and { only { filter_eq { all_rows ; status ; display only } } ; eq { hop { filter_eq { all_rows ; status ; display only } ; number } ; 1 } }
select the rows whose afc cup record is not equal to 0 . there is only one such row in the table . the member association record of this unqiue row is uzbekistan .
Output: and { only { filter_not_eq { all_rows ; afc cup ; 0 } } ; eq { hop { filter_not_eq { all_rows ; afc cup ; 0 } ; member association } ; uzbekistan } }
select the rows whose 1st ship delivery date record fuzzily matches to 1942 . for the location ( city , state ) records of these rows , most of them fuzzily match to california .
Output: most_eq { filter_eq { all_rows ; 1st ship delivery date ; 1942 } ; location ( city , state ) ; california }
select the rows whose nation record fuzzily matches to great britain ( gbr ) . take the silver record of this row . select the rows whose nation record fuzzily matches to hungary ( hun ) . take the silver record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; great britain ( gbr ) } ; silver } ; hop { filter_eq { all_rows ; nation ; hungary ( hun ) } ; silver } }
select the rows whose home record fuzzily matches to ottawa . among these rows , select the rows whose decision record fuzzily matches to hasek . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; home ; ottawa } ; decision ; hasek } } ; 4 }
select the row whose gold record of all rows is maximum . the nation record of this row is united states ( usa ) .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; united states ( usa ) }
select the row whose to par record of all rows is minimum . the player record of this row is bill rogers .
Output: eq { hop { argmin { all_rows ; to par } ; player } ; bill rogers }
select the row whose earnings record of all rows is maximum . the player record of this row is lee trevino .
Output: eq { hop { argmax { all_rows ; earnings } ; player } ; lee trevino }
select the row whose total offense record of all rows is maximum . the player record of this row is devin gardner .
Output: eq { hop { argmax { all_rows ; total offense } ; player } ; devin gardner }
select the row whose margin of victory record of all rows is maximum . the champion record of this row is jiyai shin .
Output: eq { hop { argmax { all_rows ; margin of victory } ; champion } ; jiyai shin }
select the rows whose venue record fuzzily matches to brunswick street oval . there is only one such row in the table . the home team record of this unqiue row is fitzroy .
Output: and { only { filter_eq { all_rows ; venue ; brunswick street oval } } ; eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy } }
select the rows whose party record fuzzily matches to democratic . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; party ; democratic } } ; 3 }
select the row whose react record of all rows is 5th minimum . the name record of this row is christopher williams .
Output: eq { hop { nth_argmin { all_rows ; react ; 5 } ; name } ; christopher williams }
select the row whose win % record of all rows is maximum . the played record of this row is 16 . the wins record of this row is 9 .
Output: and { eq { hop { argmax { all_rows ; win % } ; played } ; 16 } ; eq { hop { argmax { all_rows ; win % } ; wins } ; 9 } }
select the rows whose finish record is equal to 10 . there is only one such row in the table . the year record of this unqiue row is 1972 .
Output: and { only { filter_eq { all_rows ; finish ; 10 } } ; eq { hop { filter_eq { all_rows ; finish ; 10 } ; year } ; 1972 } }
the maximum results 2004 record of all rows is 28.90 % . the party record of the row with superlative results 2004 record is ps .
Output: and { eq { max { all_rows ; results 2004 } ; 28.90 % } ; eq { hop { argmax { all_rows ; results 2004 } ; party } ; ps } }
select the rows whose club record fuzzily matches to biu chun rangers . take the position in 2012 - 13 record of this row . select the rows whose club record fuzzily matches to citizen . take the position in 2012 - 13 record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; club ; biu chun rangers } ; position in 2012 - 13 } ; hop { filter_eq { all_rows ; club ; citizen } ; position in 2012 - 13 } }
select the rows whose attendance record is greater than 10000 . for the date records of these rows , most of them fuzzily match to 21 - 11 - 1973 .
Output: most_eq { filter_greater { all_rows ; attendance ; 10000 } ; date ; 21 - 11 - 1973 }
select the rows whose size ( steps ) record is greater than 4 . the average of the size ( cents ) record of these rows is 510 .
Output: round_eq { avg { filter_greater { all_rows ; size ( steps ) ; 4 } ; size ( cents ) } ; 510 }
select the row whose money spent , 2q record of all rows is maximum . the candidate record of this row is barack obama .
Output: eq { hop { argmax { all_rows ; money spent , 2q } ; candidate } ; barack obama }
select the rows whose batsmen record fuzzily matches to iain anderson alan hill . take the runs record of this row . select the rows whose batsmen record fuzzily matches to chris taylor ant botha . take the runs record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; batsmen ; iain anderson alan hill } ; runs } ; hop { filter_eq { all_rows ; batsmen ; chris taylor ant botha } ; runs } }
select the row whose date of successors formal installation record of all rows is 2nd maximum . the successor record of this row is james chesnut , jr ( d ) .
Output: eq { hop { nth_argmax { all_rows ; date of successors formal installation ; 2 } ; successor } ; james chesnut , jr ( d ) }
select the row whose founding date record of all rows is 2nd minimum . the organization record of this row is delta epsilon sigma iota .
Output: eq { hop { nth_argmin { all_rows ; founding date ; 2 } ; organization } ; delta epsilon sigma iota }
select the row whose total finals record of all rows is maximum . the clubs record of this row is primeiro de agosto .
Output: eq { hop { argmax { all_rows ; total finals } ; clubs } ; primeiro de agosto }
select the rows whose lost record is equal to 2 . there is only one such row in the table . the club record of this unqiue row is tumble rfc .
Output: and { only { filter_eq { all_rows ; lost ; 2 } } ; eq { hop { filter_eq { all_rows ; lost ; 2 } ; club } ; tumble rfc } }
select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is bl28 .
Output: eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; bl28 }
select the rows whose county record fuzzily matches to marion . the average of the enrollment record of these rows is 3,633 .
Output: round_eq { avg { filter_eq { all_rows ; county ; marion } ; enrollment } ; 3,633 }
for the directed by records of all rows , all of them fuzzily match to noam pitlik .
Output: all_eq { all_rows ; directed by ; noam pitlik }
select the rows whose runner - up record fuzzily matches to fabrizio baldassari . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; runner - up ; fabrizio baldassari } } ; 1 }
select the rows whose license record fuzzily matches to closed source . the number of such rows is 12 .
Output: eq { count { filter_eq { all_rows ; license ; closed source } } ; 12 }
the average of the crowd record of all rows is 14000 .
Output: round_eq { avg { all_rows ; crowd } ; 14000 }
select the row whose silver record of all rows is maximum . the nation record of this row is england .
Output: eq { hop { argmax { all_rows ; silver } ; nation } ; england }
select the rows whose opponent record fuzzily matches to new york . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; new york } } ; 2 }
for the category records of all rows , all of them fuzzily match to short film 2005 prix uip .
Output: all_eq { all_rows ; category ; short film 2005 prix uip }
select the rows whose college record fuzzily matches to oklahoma . there is only one such row in the table . the name record of this unqiue row is dominique franks .
Output: and { only { filter_eq { all_rows ; college ; oklahoma } } ; eq { hop { filter_eq { all_rows ; college ; oklahoma } ; name } ; dominique franks } }
select the row whose high points record of all rows is maximum . the date record of this row is june 20 .
Output: eq { hop { argmax { all_rows ; high points } ; date } ; june 20 }
select the rows whose nationality record fuzzily matches to bosnia and herzegovina . there is only one such row in the table . the previous team record of this unqiue row is golden state warriors .
Output: and { only { filter_eq { all_rows ; nationality ; bosnia and herzegovina } } ; eq { hop { filter_eq { all_rows ; nationality ; bosnia and herzegovina } ; previous team } ; golden state warriors } }
for the first leg records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; first leg ; 0 }
the average of the floor record of all rows is 60.589 .
Output: round_eq { avg { all_rows ; floor } ; 60.589 }
select the rows whose home team record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; home team } } ; 6 }
select the rows whose county name record fuzzily matches to warrick . take the population ( 2010 ) ( rank ) record of this row . select the rows whose county name record fuzzily matches to perry . take the population ( 2010 ) ( rank ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; county name ; warrick } ; population ( 2010 ) ( rank ) } ; hop { filter_eq { all_rows ; county name ; perry } ; population ( 2010 ) ( rank ) } }
select the rows whose opponent record fuzzily matches to detroit red wings . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; opponent ; detroit red wings } } ; 3 }
select the rows whose opponents in the final record fuzzily matches to jeremy bates kevin curren . there is only one such row in the table . the date record of this unqiue row is 1990 . the tournament record of this unqiue row is london / queen 's club , england .
Output: and { only { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } } ; and { eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; date } ; 1990 } ; eq { hop { filter_eq { all_rows ; opponents in the final ; jeremy bates kevin curren } ; tournament } ; london / queen 's club , england } } }
select the row whose total record of all rows is maximum . the country record of this row is france .
Output: eq { hop { argmax { all_rows ; total } ; country } ; france }
select the rows whose goals record is less than 55 . there is only one such row in the table . the player record of this unqiue row is eusébio .
Output: and { only { filter_less { all_rows ; goals ; 55 } } ; eq { hop { filter_less { all_rows ; goals ; 55 } ; player } ; eusébio } }
select the rows whose origin record fuzzily matches to us . the average of the chart no record of these rows is 70 .
Output: round_eq { avg { filter_eq { all_rows ; origin ; us } ; chart no } ; 70 }
select the rows whose nationality record fuzzily matches to ireland . among these rows , select the rows whose goals record is greater than 60 . the number of such rows is 5 .
Output: eq { count { filter_greater { filter_eq { all_rows ; nationality ; ireland } ; goals ; 60 } } ; 5 }
select the rows whose shirt sponsor record fuzzily matches to n / a . there is only one such row in the table . the team record of this unqiue row is dubai .
Output: and { only { filter_eq { all_rows ; shirt sponsor ; n / a } } ; eq { hop { filter_eq { all_rows ; shirt sponsor ; n / a } ; team } ; dubai } }
select the rows whose original air date record fuzzily matches to august . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; original air date ; august } } ; 3 }
the average of the caps record of all rows is 10.4 .
Output: round_eq { avg { all_rows ; caps } ; 10.4 }
select the rows whose runs record is less than 3000 . there is only one such row in the table . the record of this unqiue row is chris pringle .
Output: and { only { filter_less { all_rows ; runs ; 3000 } } ; eq { hop { filter_less { all_rows ; runs ; 3000 } ; } ; chris pringle } }
the average of the crowd record of all rows is 18000 .
Output: round_eq { avg { all_rows ; crowd } ; 18000 }
select the rows whose no in series record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; no in series } } ; 5 }
select the rows whose school record fuzzily matches to owen valley . take the enrollment record of this row . select the rows whose school record fuzzily matches to sullivan . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; school ; owen valley } ; enrollment } ; hop { filter_eq { all_rows ; school ; sullivan } ; enrollment } }
for the height records of all rows , most of them fuzzily match to 6 - .
Output: most_eq { all_rows ; height ; 6 - }
select the rows whose settlement record fuzzily matches to gardinovci . take the population ( 2011 ) record of this row . select the rows whose settlement record fuzzily matches to lok . take the population ( 2011 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; settlement ; gardinovci } ; population ( 2011 ) } ; hop { filter_eq { all_rows ; settlement ; lok } ; population ( 2011 ) } }
for the innings records of all rows , most of them are greater than 20 .
Output: most_greater { all_rows ; innings ; 20 }
select the rows whose date record fuzzily matches to june . for the score records of these rows , most of them fuzzily match to 2-0 .
Output: most_eq { filter_eq { all_rows ; date ; june } ; score ; 2-0 }
select the rows whose writer ( s ) record fuzzily matches to ira ungerleider . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; writer ( s ) ; ira ungerleider } } ; 2 }
select the rows whose region record fuzzily matches to africa . among these rows , select the rows whose joined opec record is equal to 1969 . there is only one such row in the table . the country record of this unqiue row is algeria .
Output: and { only { filter_eq { filter_eq { all_rows ; region ; africa } ; joined opec ; 1969 } } ; eq { hop { filter_eq { filter_eq { all_rows ; region ; africa } ; joined opec ; 1969 } ; country } ; algeria } }
select the row whose car record of all rows is 2nd maximum . the player record of this row is john ritcher .
Output: eq { hop { nth_argmax { all_rows ; car ; 2 } ; player } ; john ritcher }
select the row whose speed ( km / h ) record of all rows is 2nd maximum . the vehicle record of this row is lockheed sr - 71 blackbird .
Output: eq { hop { nth_argmax { all_rows ; speed ( km / h ) ; 2 } ; vehicle } ; lockheed sr - 71 blackbird }
the average of the goals for record of all rows is 38.5 .
Output: round_eq { avg { all_rows ; goals for } ; 38.5 }
select the rows whose seasons at this level record fuzzily matches to 27 seasons . for the settlements records of these rows , most of them fuzzily match to lisbon .
Output: most_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; lisbon }
select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row . select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row . the first record is less than the second record . the finish position record of the first row is 31st . the finish position record of the second row is 33rd .
Output: and { less { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; hop { filter_eq { all_rows ; year ; 2008 } ; finish position } } ; and { eq { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; 31st } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; finish position } ; 33rd } } }
the maximum distance record of all rows is 2500 m .
Output: eq { max { all_rows ; distance } ; 2500 m }
select the rows whose won record is equal to 15 . there is only one such row in the table . the club record of this unqiue row is heidelberger rk .
Output: and { only { filter_eq { all_rows ; won ; 15 } } ; eq { hop { filter_eq { all_rows ; won ; 15 } ; club } ; heidelberger rk } }
select the rows whose episode record is equal to 77 . there is only one such row in the table . the netflix record of this unqiue row is s03e25 .
Output: and { only { filter_eq { all_rows ; episode ; 77 } } ; eq { hop { filter_eq { all_rows ; episode ; 77 } ; netflix } ; s03e25 } }
select the rows whose homeport ( as of july 2013 ) record fuzzily matches to portsmouth . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; homeport ( as of july 2013 ) ; portsmouth } } ; 6 }
the average of the winnings record of all rows is 884656 .
Output: round_eq { avg { all_rows ; winnings } ; 884656 }
select the rows whose nationality record fuzzily matches to jamaica . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nationality ; jamaica } } ; 2 }
select the row whose total record of all rows is 2nd maximum . the nation record of this row is netherlands .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; netherlands }
select the rows whose category record fuzzily matches to best supporting actress . there is only one such row in the table .
Output: only { filter_eq { all_rows ; category ; best supporting actress } }
select the rows whose result record fuzzily matches to win . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; result ; win } } ; 6 }
select the rows whose result record fuzzily matches to 5-0 . there is only one such row in the table . the winner record of this unqiue row is kiveton park .
Output: and { only { filter_eq { all_rows ; result ; 5-0 } } ; eq { hop { filter_eq { all_rows ; result ; 5-0 } ; winner } ; kiveton park } }
select the rows whose result record fuzzily matches to w . among these rows , select the rows whose attendance record is greater than 50000 . there is only one such row in the table . the date record of this unqiue row is 10 / 20 / 1928 .
Output: and { only { filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 50000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 50000 } ; date } ; 10 / 20 / 1928 } }
select the rows whose unit record fuzzily matches to colorado . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; unit ; colorado } } ; 3 }
select the rows whose partner record fuzzily matches to tami whitlinger - jones . select the row whose date record of these rows is minimum . the surface record of this row is carpet .
Output: eq { hop { argmin { filter_eq { all_rows ; partner ; tami whitlinger - jones } ; date } ; surface } ; carpet }
select the rows whose name record fuzzily matches to eclair . take the launched record of this row . select the rows whose name record fuzzily matches to derwent . take the launched record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; eclair } ; launched } ; hop { filter_eq { all_rows ; name ; derwent } ; launched } }
for the builder records of all rows , most of them fuzzily match to andrew barclay & co .
Output: most_eq { all_rows ; builder ; andrew barclay & co }
select the rows whose 2nd member record fuzzily matches to dugald mccoig cowan . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; 2nd member ; dugald mccoig cowan } } ; 4 }
select the rows whose venue record fuzzily matches to athens , greece . take the year record of this row . select the rows whose venue record fuzzily matches to helsinki , finland . take the year record of this row . the second record is 1 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; venue ; athens , greece } ; year } ; hop { filter_eq { all_rows ; venue ; helsinki , finland } ; year } } ; -1 }
the maximum enrollment record of all rows is 580 .
Output: eq { max { all_rows ; enrollment } ; 580 }
select the rows whose area served record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; area served } } ; 6 }
select the rows whose episode summary record fuzzily matches to richard is made into boyfriend material . take the premier date record of this row . select the rows whose episode summary record fuzzily matches to lawryn is made into a bmx biker . take the premier date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; episode summary ; richard is made into boyfriend material } ; premier date } ; hop { filter_eq { all_rows ; episode summary ; lawryn is made into a bmx biker } ; premier date } }
select the row whose score record of all rows is maximum . the date record of this row is may 9 .
Output: eq { hop { argmax { all_rows ; score } ; date } ; may 9 }
select the rows whose date record fuzzily matches to 2006 . select the row whose date record of these rows is minimum . the opponent record of this row is josipa bek .
Output: eq { hop { argmin { filter_eq { all_rows ; date ; 2006 } ; date } ; opponent } ; josipa bek }
select the row whose year erected record of all rows is minimum . the statue record of this row is general josé gervasio artigas .
Output: eq { hop { argmin { all_rows ; year erected } ; statue } ; general josé gervasio artigas }
select the rows whose programming record fuzzily matches to main wybe programming . take the channel record of this row . select the rows whose programming record fuzzily matches to russia today . take the channel record of this row . the second record is 0.3 larger than the first record . the channel record of the first row is 35.1 . the channel record of the second row is 35.4 .
Output: and { eq { diff { hop { filter_eq { all_rows ; programming ; main wybe programming } ; channel } ; hop { filter_eq { all_rows ; programming ; russia today } ; channel } } ; -0.3 } ; and { eq { hop { filter_eq { all_rows ; programming ; main wybe programming } ; channel } ; 35.1 } ; eq { hop { filter_eq { all_rows ; programming ; russia today } ; channel } ; 35.4 } } }
for the kit supplier records of all rows , most of them fuzzily match to adidas .
Output: most_eq { all_rows ; kit supplier ; adidas }
select the row whose size record of all rows is maximum . the school record of this row is mitchell .
Output: eq { hop { argmax { all_rows ; size } ; school } ; mitchell }
select the rows whose division record record fuzzily matches to 4-2 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; division record ; 4-2 } } ; 2 }
the sum of the wins record of all rows is 11 .
Output: round_eq { sum { all_rows ; wins } ; 11 }
select the rows whose founded record is greater than or equal to 1994 . the number of such rows is 6 .
Output: eq { count { filter_greater_eq { all_rows ; founded ; 1994 } } ; 6 }
select the rows whose player record fuzzily matches to jon gott . take the pick record of this row . select the rows whose player record fuzzily matches to paul woldu . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; jon gott } ; pick } ; hop { filter_eq { all_rows ; player ; paul woldu } ; pick } }
select the row whose average record of all rows is maximum . the player record of this row is david hemp .
Output: eq { hop { argmax { all_rows ; average } ; player } ; david hemp }
select the rows whose bronze record is equal to 8 . there is only one such row in the table . the rank record of this unqiue row is 3 .
Output: and { only { filter_eq { all_rows ; bronze ; 8 } } ; eq { hop { filter_eq { all_rows ; bronze ; 8 } ; rank } ; 3 } }
select the rows whose starts record is greater than 30 . among these rows , select the rows whose losses record is equal to 17 . there is only one such row in the table . the name record of this unqiue row is brady quinn .
Output: and { only { filter_eq { filter_greater { all_rows ; starts ; 30 } ; losses ; 17 } } ; eq { hop { filter_eq { filter_greater { all_rows ; starts ; 30 } ; losses ; 17 } ; name } ; brady quinn } }
select the rows whose frequency record fuzzily matches to 450 mhz . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; frequency ; 450 mhz } } ; 2 }
select the rows whose laps record is equal to 200 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; laps ; 200 } } ; 6 }
select the row whose goal difference record of all rows is 3rd maximum . the club record of this row is real murcia .
Output: eq { hop { nth_argmax { all_rows ; goal difference ; 3 } ; club } ; real murcia }
select the rows whose school record fuzzily matches to mogadore . take the tenure record of this row . select the rows whose school record fuzzily matches to field . take the tenure record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; school ; mogadore } ; tenure } ; hop { filter_eq { all_rows ; school ; field } ; tenure } }
for the goals records of all rows , most of them are greater than 1000 .
Output: most_greater { all_rows ; goals ; 1000 }
the sum of the total g record of all rows is 44 .
Output: round_eq { sum { all_rows ; total g } ; 44 }
select the rows whose commissioned or completed record fuzzily matches to 1864 . for the laid down records of these rows , all of them are equal to 1862 .
Output: all_eq { filter_eq { all_rows ; commissioned or completed ; 1864 } ; laid down ; 1862 }
select the row whose gold record of all rows is maximum . the nation record of this row is united states .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; united states }
select the rows whose player record fuzzily matches to gary springer . take the pick record of this row . select the rows whose player record fuzzily matches to rich congo . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; gary springer } ; pick } ; hop { filter_eq { all_rows ; player ; rich congo } ; pick } }
for the result records of all rows , most of them fuzzily match to advanced .
Output: most_eq { all_rows ; result ; advanced }
select the rows whose winning driver record fuzzily matches to aj foyt . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; winning driver ; aj foyt } } ; 4 }
select the row whose earnings record of all rows is 2nd maximum . the year record of this row is 2009 .
Output: eq { hop { nth_argmax { all_rows ; earnings ; 2 } ; year } ; 2009 }
select the rows whose home team score record is greater than 15.0 . among these rows , select the rows whose crowd record is less than 10000 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_greater { all_rows ; home team score ; 15.0 } ; crowd ; 10000 } } ; 2 }
select the rows whose mccain % record fuzzily matches to 75.7 % . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; mccain % ; 75.7 % } } ; 1 }
select the row whose location attendance record of all rows is 1st maximum . the date record of this row is december 27 .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; date } ; december 27 }
select the rows whose away team score record is greater than 10 . among these rows , select the rows whose crowd record is greater than 12000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; away team score ; 10 } ; crowd ; 12000 } } ; 2 }
select the rows whose name record fuzzily matches to joe bradford . take the years record of this row . select the rows whose name record fuzzily matches to trevor francis . take the years record of this row . the second record is 50 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; joe bradford } ; years } ; hop { filter_eq { all_rows ; name ; trevor francis } ; years } } ; -50 years }
the average of the mult record of all rows is 3.90 90 repeating .
Output: round_eq { avg { all_rows ; mult } ; 3.90 90 repeating }
for the performer 1 records of all rows , all of them fuzzily match to john sessions .
Output: all_eq { all_rows ; performer 1 ; john sessions }
select the rows whose nation record fuzzily matches to australia ( aus ) . take the silver record of this row . select the rows whose nation record fuzzily matches to japan ( jpn ) . take the silver record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; australia ( aus ) } ; silver } ; hop { filter_eq { all_rows ; nation ; japan ( jpn ) } ; silver } }
the average of the points record of all rows is 0 .
Output: round_eq { avg { all_rows ; points } ; 0 }
select the rows whose country record fuzzily matches to united states . select the row whose began record of these rows is minimum . the tournament record of this row is cincinnati masters .
Output: eq { hop { argmin { filter_eq { all_rows ; country ; united states } ; began } ; tournament } ; cincinnati masters }
select the rows whose top 10 record is equal to 4 . there is only one such row in the table . the year record of this unqiue row is 1995 .
Output: and { only { filter_eq { all_rows ; top 10 ; 4 } } ; eq { hop { filter_eq { all_rows ; top 10 ; 4 } ; year } ; 1995 } }
select the rows whose opponent record fuzzily matches to indianapolis colts . select the row whose date record of these rows is minimum . the game site record of this row is hoosier dome .
Output: eq { hop { argmin { filter_eq { all_rows ; opponent ; indianapolis colts } ; date } ; game site } ; hoosier dome }
select the row whose home team score record of all rows is maximum . the home team record of this row is richmond . the away team score record of this row is 14.10 ( 94 ) .
Output: and { eq { hop { argmax { all_rows ; home team score } ; home team } ; richmond } ; eq { hop { argmax { all_rows ; home team score } ; away team score } ; 14.10 ( 94 ) } }
select the row whose us viewers ( millions ) record of all rows is maximum . the no in season record of this row is 1 .
Output: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; no in season } ; 1 }
select the rows whose notes record fuzzily matches to enid . take the built record of this row . select the rows whose notes record fuzzily matches to snowdon . take the built record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; notes ; enid } ; built } ; hop { filter_eq { all_rows ; notes ; snowdon } ; built } }
for the laps records of all rows , most of them are equal to 47 .
Output: most_eq { all_rows ; laps ; 47 }
the average of the attendance record of all rows is 15000 .
Output: round_eq { avg { all_rows ; attendance } ; 15000 }
select the rows whose last title record fuzzily matches to ( none ) . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; last title ; ( none ) } } ; 7 }
select the row whose gold record of all rows is maximum . the nation record of this row is cuba .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; cuba }
select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is clarence f lea .
Output: and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; clarence f lea } }
select the rows whose result record fuzzily matches to lost re - election republican gain . there is only one such row in the table . the incumbent record of this unqiue row is dixie gilmer .
Output: and { only { filter_eq { all_rows ; result ; lost re - election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; incumbent } ; dixie gilmer } }
for the home captain records of all rows , all of them fuzzily match to alec stewart .
Output: all_eq { all_rows ; home captain ; alec stewart }
select the rows whose team record fuzzily matches to lietuvos rytas vilnius . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; team ; lietuvos rytas vilnius } } ; 2 }
for the crowd records of all rows , most of them are less than 30000 .
Output: most_less { all_rows ; crowd ; 30000 }
for the formats records of all rows , most of them fuzzily match to vinyl .
Output: most_eq { all_rows ; formats ; vinyl }
select the rows whose country record fuzzily matches to united states . the average of the to par record of these rows is 3.4 .
Output: round_eq { avg { filter_eq { all_rows ; country ; united states } ; to par } ; 3.4 }
for the date records of all rows , all of them fuzzily match to 17 august 1940 .
Output: all_eq { all_rows ; date ; 17 august 1940 }
for the duration records of all rows , most of them are less than 5:00 .
Output: most_less { all_rows ; duration ; 5:00 }