niryuu/sni-each-converted huggingface dataset:
select the rows whose position record fuzzily matches to defense . the 4th minimum round record of these rows is 4 . the player record of the row with 4th minimum round record is dave hynek .
Output: and { eq { nth_min { filter_eq { all_rows ; position ; defense } ; round ; 4 } ; 4 } ; eq { hop { nth_argmin { filter_eq { all_rows ; position ; defense } ; round ; 4 } ; player } ; dave hynek } }
select the row whose us viewers ( million ) record of all rows is maximum . the title record of this row is baby not on board .
Output: eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; baby not on board }
the average of the attendance record of all rows is 2270 .
Output: round_eq { avg { all_rows ; attendance } ; 2270 }
select the rows whose location attendance record fuzzily matches to new orleans arena . the sum of the location attendance record of these rows is 117747 .
Output: round_eq { sum { filter_eq { all_rows ; location attendance ; new orleans arena } ; location attendance } ; 117747 }
select the rows whose high points record fuzzily matches to chris bosh . the average of the high points record of these rows is 25.57 .
Output: round_eq { avg { filter_eq { all_rows ; high points ; chris bosh } ; high points } ; 25.57 }
select the rows whose result record fuzzily matches to nominee . there is only one such row in the table . the film title used in nomination record of this unqiue row is before the rain .
Output: and { only { filter_eq { all_rows ; result ; nominee } } ; eq { hop { filter_eq { all_rows ; result ; nominee } ; film title used in nomination } ; before the rain } }
select the rows whose score record fuzzily matches to w . among these rows , select the rows whose team record fuzzily matches to memphis . there is only one such row in the table . the date record of this unqiue row is january 2 .
Output: and { only { filter_eq { filter_eq { all_rows ; score ; w } ; team ; memphis } } ; eq { hop { filter_eq { filter_eq { all_rows ; score ; w } ; team ; memphis } ; date } ; january 2 } }
for the first elected records of all rows , most of them are less than 2000 .
Output: most_less { all_rows ; first elected ; 2000 }
select the rows whose building record fuzzily matches to titanium la portada . take the number of floors record of this row . select the rows whose building record fuzzily matches to torre mall center . take the number of floors record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; building ; titanium la portada } ; number of floors } ; hop { filter_eq { all_rows ; building ; torre mall center } ; number of floors } }
select the row whose time record of all rows is 3rd maximum . the name record of this row is anatoly polyakov .
Output: eq { hop { nth_argmax { all_rows ; time ; 3 } ; name } ; anatoly polyakov }
select the row whose took office record of all rows is maximum . the delegate record of this row is nina r harper .
Output: eq { hop { argmax { all_rows ; took office } ; delegate } ; nina r harper }
the maximum score ( l ) = score in legs , ( s ) = score in sets record of all rows is 11 - 5 ( l ) .
Output: eq { max { all_rows ; score ( l )
select the row whose apogee record of all rows is maximum . the mission record of this row is maxus 1b .
Output: eq { hop { argmax { all_rows ; apogee } ; mission } ; maxus 1b }
select the rows whose location record fuzzily matches to florida . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; florida } } ; 3 }
the sum of the crowd record of all rows is 100600 .
Output: round_eq { sum { all_rows ; crowd } ; 100600 }
select the row whose silver record of all rows is 1st maximum . the nation record of this row is france .
Output: eq { hop { nth_argmax { all_rows ; silver ; 1 } ; nation } ; france }
for the yvert records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; yvert ; 0 }
the average of the attendance record of all rows is 52,519.93 .
Output: round_eq { avg { all_rows ; attendance } ; 52,519.93 }
select the rows whose college record fuzzily matches to pittsburgh . there is only one such row in the table . the player record of this unqiue row is marty schottenheimer .
Output: and { only { filter_eq { all_rows ; college ; pittsburgh } } ; eq { hop { filter_eq { all_rows ; college ; pittsburgh } ; player } ; marty schottenheimer } }
for the chassis records of all rows , all of them fuzzily match to dallara .
Output: all_eq { all_rows ; chassis ; dallara }
select the row whose attendance record of all rows is minimum . the home record of this row is ny islanders .
Output: eq { hop { argmin { all_rows ; attendance } ; home } ; ny islanders }
select the rows whose first broadcast record fuzzily matches to september 2013 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; first broadcast ; september 2013 } } ; 4 }
select the row whose to par record of all rows is maximum . the player record of this row is tiger woods .
Output: eq { hop { argmax { all_rows ; to par } ; player } ; tiger woods }
select the rows whose date record fuzzily matches to october 31 . take the score record of this row . select the rows whose date record fuzzily matches to october 29 . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; october 31 } ; score } ; hop { filter_eq { all_rows ; date ; october 29 } ; score } }
select the rows whose home record fuzzily matches to ny rangers . there is only one such row in the table . the date record of this unqiue row is december 1 .
Output: and { only { filter_eq { all_rows ; home ; ny rangers } } ; eq { hop { filter_eq { all_rows ; home ; ny rangers } ; date } ; december 1 } }
select the row whose year ( ceremony ) record of all rows is 3rd maximum . the english title record of this row is the wind journeys .
Output: eq { hop { nth_argmax { all_rows ; year ( ceremony ) ; 3 } ; english title } ; the wind journeys }
select the row whose points 1 record of all rows is maximum . the team record of this row is colne dynamoes .
Output: eq { hop { argmax { all_rows ; points 1 } ; team } ; colne dynamoes }
select the rows whose opponent record fuzzily matches to liverpool . take the result record of this row . select the rows whose opponent record fuzzily matches to chelsea . take the result record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; liverpool } ; result } ; hop { filter_eq { all_rows ; opponent ; chelsea } ; result } }
select the row whose current streak record of all rows is 2nd maximum . the missouri vs record of this row is drake .
Output: eq { hop { nth_argmax { all_rows ; current streak ; 2 } ; missouri vs } ; drake }
select the rows whose mens singles record fuzzily matches to andrew smith . there is only one such row in the table . the year record of this unqiue row is 2006 .
Output: and { only { filter_eq { all_rows ; mens singles ; andrew smith } } ; eq { hop { filter_eq { all_rows ; mens singles ; andrew smith } ; year } ; 2006 } }
select the rows whose gold record is equal to 7 . there is only one such row in the table . the nation record of this unqiue row is brazil ( bra ) .
Output: and { only { filter_eq { all_rows ; gold ; 7 } } ; eq { hop { filter_eq { all_rows ; gold ; 7 } ; nation } ; brazil ( bra ) } }
select the row whose height record of all rows is maximum . the player record of this row is josé luis moltó .
Output: eq { hop { argmax { all_rows ; height } ; player } ; josé luis moltó }
select the rows whose gold record fuzzily matches to 1 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; gold ; 1 } } ; 3 }
select the rows whose rank record is arbitrary . the number of such rows is 15 .
Output: eq { count { filter_all { all_rows ; rank } } ; 15 }
select the row whose goals record of all rows is 2nd maximum . the nationality record of this row is ireland .
Output: eq { hop { nth_argmax { all_rows ; goals ; 2 } ; nationality } ; ireland }
the average of the wheels record of all rows is 120 .
Output: round_eq { avg { all_rows ; wheels } ; 120 }
select the row whose tonnes record of all rows is maximum . the airport name record of this row is memphis international airport .
Output: eq { hop { argmax { all_rows ; tonnes } ; airport name } ; memphis international airport }
select the rows whose school / club team record fuzzily matches to penn state . there is only one such row in the table . the player record of this unqiue row is john amaechi .
Output: and { only { filter_eq { all_rows ; school / club team ; penn state } } ; eq { hop { filter_eq { all_rows ; school / club team ; penn state } ; player } ; john amaechi } }
select the rows whose class record fuzzily matches to c1 . there is only one such row in the table . the year record of this unqiue row is 1992 .
Output: and { only { filter_eq { all_rows ; class ; c1 } } ; eq { hop { filter_eq { all_rows ; class ; c1 } ; year } ; 1992 } }
the average of the profit ( mil ) record of all rows is 121.66 .
Output: round_eq { avg { all_rows ; profit ( mil ) } ; 121.66 }
select the rows whose team ( league ) record fuzzily matches to lake superior state university ( ncaa ) . there is only one such row in the table . the player record of this unqiue row is paul constantin .
Output: and { only { filter_eq { all_rows ; team ( league ) ; lake superior state university ( ncaa ) } } ; eq { hop { filter_eq { all_rows ; team ( league ) ; lake superior state university ( ncaa ) } ; player } ; paul constantin } }
the maximum score record of all rows is 8 - 0 .
Output: eq { max { all_rows ; score } ; 8 - 0 }
select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is mel watt .
Output: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; mel watt }
select the rows whose chassis record fuzzily matches to brm p201 . take the year record of this row . select the rows whose chassis record fuzzily matches to hill gh1 . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; chassis ; brm p201 } ; year } ; hop { filter_eq { all_rows ; chassis ; hill gh1 } ; year } }
select the row whose population record of all rows is 2nd maximum . the official name record of this row is hillsborough .
Output: eq { hop { nth_argmax { all_rows ; population ; 2 } ; official name } ; hillsborough }
the average of the rank record of all rows is 4.89 .
Output: round_eq { avg { all_rows ; rank } ; 4.89 }
select the rows whose wins record is greater than 1 . among these rows , select the rows whose country record fuzzily matches to united states . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_greater { all_rows ; wins ; 1 } ; country ; united states } } ; 3 }
the average of the catches record of all rows is 3.125 .
Output: round_eq { avg { all_rows ; catches } ; 3.125 }
select the rows whose competition record fuzzily matches to world cup qualification . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; competition ; world cup qualification } } ; 2 }
select the rows whose engine record fuzzily matches to novi v8 . there is only one such row in the table . the year record of this unqiue row is 1956 .
Output: and { only { filter_eq { all_rows ; engine ; novi v8 } } ; eq { hop { filter_eq { all_rows ; engine ; novi v8 } ; year } ; 1956 } }
select the rows whose rank points record is equal to 8 . among these rows , select the rows whose total record is equal to 17 . there is only one such row in the table . the shooter record of this unqiue row is iulian raicea ( rou ) .
Output: and { only { filter_eq { filter_eq { all_rows ; rank points ; 8 } ; total ; 17 } } ; eq { hop { filter_eq { filter_eq { all_rows ; rank points ; 8 } ; total ; 17 } ; shooter } ; iulian raicea ( rou ) } }
select the row whose dcsf number record of all rows is maximum . the name record of this row is galley hill .
Output: eq { hop { argmax { all_rows ; dcsf number } ; name } ; galley hill }
select the rows whose length record is greater than 4:00 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; length ; 4:00 } } ; 3 }
for the category records of all rows , most of them fuzzily match to best actress in a musical .
Output: most_eq { all_rows ; category ; best actress in a musical }
select the rows whose country record fuzzily matches to dominican republic . take the asians record of this row . select the rows whose country record fuzzily matches to costa rica . take the asians record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; country ; dominican republic } ; asians } ; hop { filter_eq { all_rows ; country ; costa rica } ; asians } }
select the row whose average record of all rows is 2nd minimum . the goalkeeper record of this row is rubén martínez .
Output: eq { hop { nth_argmin { all_rows ; average ; 2 } ; goalkeeper } ; rubén martínez }
the sum of the annual payroll ( 1000 ) record of all rows is 3,521,267,581 .
Output: round_eq { sum { all_rows ; annual payroll ( 1000 ) } ; 3,521,267,581 }
for the crowd records of all rows , most of them are less than 20000 .
Output: most_less { all_rows ; crowd ; 20000 }
select the row whose total record of all rows is maximum . the location record of this row is plainview .
Output: eq { hop { argmax { all_rows ; total } ; location } ; plainview }
select the row whose crowd record of all rows is maximum . the venue record of this row is punt road oval .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; punt road oval }
select the rows whose result record fuzzily matches to l . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; result ; l } } ; 8 }
select the row whose launch date record of all rows is 2nd maximum . the name record of this row is corot .
Output: eq { hop { nth_argmax { all_rows ; launch date ; 2 } ; name } ; corot }
for the original release records of all rows , most of them fuzzily match to 196 .
Output: most_eq { all_rows ; original release ; 196 }
select the rows whose home team score record is greater than 4 . among these rows , 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 { filter_greater { all_rows ; home team score ; 4 } ; venue ; brunswick street oval } } ; eq { hop { filter_eq { filter_greater { all_rows ; home team score ; 4 } ; venue ; brunswick street oval } ; home team } ; fitzroy } }
select the rows whose points record is greater than 100 . the number of such rows is 6 .
Output: eq { count { filter_greater { all_rows ; points ; 100 } } ; 6 }
select the row whose catches record of all rows is 2nd maximum . the player record of this row is adam gilchrist .
Output: eq { hop { nth_argmax { all_rows ; catches ; 2 } ; player } ; adam gilchrist }
select the row whose position in 1992 record of all rows is minimum . the team record of this row is dinamo minsk .
Output: eq { hop { argmin { all_rows ; position in 1992 } ; team } ; dinamo minsk }
the average of the high points record of all rows is 25 .
Output: round_eq { avg { all_rows ; high points } ; 25 }
select the row whose age at appointment record of all rows is maximum . the romanised name record of this row is elsie leung oi - see .
Output: eq { hop { argmax { all_rows ; age at appointment } ; romanised name } ; elsie leung oi - see }
the average of the result record of all rows is 26 .
Output: round_eq { avg { all_rows ; result } ; 26 }
select the rows whose location / attendance record fuzzily matches to ford center . the minimum date record of these rows is nov 1 .
Output: eq { min { filter_eq { all_rows ; location / attendance ; ford center } ; date } ; nov 1 }
for the country of origin records of all rows , most of them fuzzily match to philippines .
Output: most_eq { all_rows ; country of origin ; philippines }
select the rows whose played record is equal to 27 . the maximum position record of these rows is 12th . the competition record of the row with superlative position record is super league xiv .
Output: and { eq { max { filter_eq { all_rows ; played ; 27 } ; position } ; 12th } ; eq { hop { argmax { filter_eq { all_rows ; played ; 27 } ; position } ; competition } ; super league xiv } }
select the rows whose date record is equal to - . there is only one such row in the table . the week record of this unqiue row is 10 .
Output: and { only { filter_eq { all_rows ; date ; - } } ; eq { hop { filter_eq { all_rows ; date ; - } ; week } ; 10 } }
for the director records of all rows , all of them fuzzily match to robert alvarez .
Output: all_eq { all_rows ; director ; robert alvarez }
select the row whose points record of all rows is 2nd maximum . the player record of this row is andy kaufmann .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; player } ; andy kaufmann }
select the rows whose result record fuzzily matches to won . there is only one such row in the table . the nominee record of this unqiue row is daniel day - lewis .
Output: and { only { filter_eq { all_rows ; result ; won } } ; eq { hop { filter_eq { all_rows ; result ; won } ; nominee } ; daniel day - lewis } }
select the row whose gold record of all rows is maximum . the nation record of this row is germany .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; germany }
select the row whose attendance record of all rows is 1st maximum . the date record of this row is september 25 , 1960 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; september 25 , 1960 }
select the rows whose points record is less than 100 . there is only one such row in the table . the artist record of this unqiue row is giorgos gavriel .
Output: and { only { filter_less { all_rows ; points ; 100 } } ; eq { hop { filter_less { all_rows ; points ; 100 } ; artist } ; giorgos gavriel } }
select the rows whose tournament record fuzzily matches to australian open . take the 2011 record of this row . select the rows whose tournament record fuzzily matches to australian open . take the 2011 record of this row . the first record fuzzily matches to the second record . the 2011 record of the first row is qf . the 2011 record of the second row is qf .
Output: and { eq { hop { filter_eq { all_rows ; tournament ; australian open } ; 2011 } ; hop { filter_eq { all_rows ; tournament ; australian open } ; 2011 } } ; and { eq { hop { filter_eq { all_rows ; tournament ; australian open } ; 2011 } ; qf } ; eq { hop { filter_eq { all_rows ; tournament ; australian open } ; 2011 } ; qf } } }
select the row whose no in season record of all rows is 1st minimum . the title record of this row is grampires ( part 1 ) .
Output: eq { hop { nth_argmin { all_rows ; no in season ; 1 } ; title } ; grampires ( part 1 ) }
select the row whose year joined record of all rows is 1st maximum . the school record of this row is fishers .
Output: eq { hop { nth_argmax { all_rows ; year joined ; 1 } ; school } ; fishers }
select the rows whose region record fuzzily matches to united states . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; region ; united states } } ; 3 }
select the row whose position record of all rows is 2nd maximum . the year record of this row is 1987 .
Output: eq { hop { nth_argmax { all_rows ; position ; 2 } ; year } ; 1987 }
the 3rd minimum episode number record of all rows is 3 . the musical guest ( song performed ) record of the row with 3rd minimum episode number record is nerd ( everyone nose ) .
Output: and { eq { nth_min { all_rows ; episode number ; 3 } ; 3 } ; eq { hop { nth_argmin { all_rows ; episode number ; 3 } ; musical guest ( song performed ) } ; nerd ( everyone nose ) } }
select the rows whose academic ranking of world universities 2012 record fuzzily matches to not ranked . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; academic ranking of world universities 2012 ; not ranked } } ; 3 }
for the date records of all rows , all of them fuzzily match to january .
Output: all_eq { all_rows ; date ; january }
select the rows whose driver record fuzzily matches to jeff simmons . take the laps record of this row . select the rows whose driver record fuzzily matches to sarah fisher . take the laps record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; driver ; jeff simmons } ; laps } ; hop { filter_eq { all_rows ; driver ; sarah fisher } ; laps } }
select the rows whose incumbent record fuzzily matches to gene taylor . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to harold volkmer . take the first elected record of this row . the second record is 4 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; incumbent ; gene taylor } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; harold volkmer } ; first elected } } ; -4 years }
select the rows whose game site record fuzzily matches to bears stadium . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; game site ; bears stadium } } ; 6 }
select the rows whose 2012 club record fuzzily matches to vk primorje rijeka . there is only one such row in the table . the name record of this unqiue row is xavier garcía .
Output: and { only { filter_eq { all_rows ; 2012 club ; vk primorje rijeka } } ; eq { hop { filter_eq { all_rows ; 2012 club ; vk primorje rijeka } ; name } ; xavier garcía } }
select the rows whose owner record fuzzily matches to cairo communication . there is only one such row in the table . the channel record of this unqiue row is la7 .
Output: and { only { filter_eq { all_rows ; owner ; cairo communication } } ; eq { hop { filter_eq { all_rows ; owner ; cairo communication } ; channel } ; la7 } }
select the rows whose kit manufacturer record fuzzily matches to umbro . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; kit manufacturer ; umbro } } ; 4 }
the average of the to par record of all rows is 15.6 .
Output: round_eq { avg { all_rows ; to par } ; 15.6 }
for the reg gp records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; reg gp ; 0 }
select the rows whose opponent record fuzzily matches to philadelphia eagles . there is only one such row in the table . the week record of this unqiue row is 1 .
Output: and { only { filter_eq { all_rows ; opponent ; philadelphia eagles } } ; eq { hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; week } ; 1 } }
select the rows whose owner ( s ) record fuzzily matches to private owner . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; owner ( s ) ; private owner } } ; 5 }
for the frequency records of all rows , most of them fuzzily match to 00 9 .
Output: most_eq { all_rows ; frequency ; 00 9 }
select the rows whose party record fuzzily matches to cd & v . among these rows , select the rows whose name record fuzzily matches to yves leterme . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; party ; cd & v } ; name ; yves leterme } } ; 2 }
select the rows whose college record fuzzily matches to nu . there is only one such row in the table . the player record of this unqiue row is rey mendoza .
Output: and { only { filter_eq { all_rows ; college ; nu } } ; eq { hop { filter_eq { all_rows ; college ; nu } ; player } ; rey mendoza } }
select the rows whose poison / klesha record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; poison / klesha } } ; 5 }
select the rows whose date record fuzzily matches to 1997 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date ; 1997 } } ; 2 }
select the rows whose bronze record is equal to 2 . select the row whose gold record of these rows is 1st maximum . the rank record of this row is 4 .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; bronze ; 2 } ; gold ; 1 } ; rank } ; 4 }
for the date records of all rows , most of them fuzzily match to friday .
Output: most_eq { all_rows ; date ; friday }
select the rows whose opponent record fuzzily matches to toronto maple leafs . the sum of the attendance record of these rows is 38,002 .
Output: round_eq { sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; attendance } ; 38,002 }
select the rows whose name record fuzzily matches to newport . take the year withdrawn record of this row . select the rows whose name record fuzzily matches to cowes . take the year withdrawn record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; newport } ; year withdrawn } ; hop { filter_eq { all_rows ; name ; cowes } ; year withdrawn } }
select the rows whose manner of departure record fuzzily matches to signed by blackburn rovers ( mutual consent ) . there is only one such row in the table . the outgoing manager record of this unqiue row is paul ince .
Output: and { only { filter_eq { all_rows ; manner of departure ; signed by blackburn rovers ( mutual consent ) } } ; eq { hop { filter_eq { all_rows ; manner of departure ; signed by blackburn rovers ( mutual consent ) } ; outgoing manager } ; paul ince } }
select the rows whose winning driver record fuzzily matches to jackie stewart . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; winning driver ; jackie stewart } } ; 2 }
select the rows whose spacecraft record fuzzily matches to voyager 1 . the 1st minimum time elapsed record of these rows is 547 days ( 1 yr , 6 mo , 1 d ) .
Output: eq { nth_min { filter_eq { all_rows ; spacecraft ; voyager 1 } ; time elapsed ; 1 } ; 547 days ( 1 yr , 6 mo , 1 d ) }
for the total points records of all rows , most of them are less than 2000 .
Output: most_less { all_rows ; total points ; 2000 }
select the rows whose draws record is greater than 5 . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; draws ; 5 } } ; 4 }
select the rows whose episode record is less than or equal to 175 . among these rows , select the rows whose segment a record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { filter_less_eq { all_rows ; episode ; 175 } ; segment a } } ; 6 }
select the rows whose city record fuzzily matches to santa clara , california . there is only one such row in the table . the athlete record of this unqiue row is mike ryan .
Output: and { only { filter_eq { all_rows ; city ; santa clara , california } } ; eq { hop { filter_eq { all_rows ; city ; santa clara , california } ; athlete } ; mike ryan } }
select the rows whose club record fuzzily matches to sussex . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; club ; sussex } } ; 3 }
the average of the crowd record of all rows is 21996 .
Output: round_eq { avg { all_rows ; crowd } ; 21996 }
select the rows whose school / club team record fuzzily matches to minnesota . for the round records of these rows , most of them are less than or equal to 11 .
Output: most_less_eq { filter_eq { all_rows ; school / club team ; minnesota } ; round ; 11 }
select the rows whose location record fuzzily matches to seattle . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; seattle } } ; 3 }
select the rows whose country record fuzzily matches to united kingdom . for the points records of these rows , most of them are less than 10 .
Output: most_less { filter_eq { all_rows ; country ; united kingdom } ; points ; 10 }
select the rows whose winner record fuzzily matches to brad jones . the minimum date record of these rows is 26 - 27 apr .
Output: eq { min { filter_eq { all_rows ; winner ; brad jones } ; date } ; 26 - 27 apr }
select the rows whose sport record fuzzily matches to baseball . there is only one such row in the table . the sportsperson record of this unqiue row is albert pujols .
Output: and { only { filter_eq { all_rows ; sport ; baseball } } ; eq { hop { filter_eq { all_rows ; sport ; baseball } ; sportsperson } ; albert pujols } }
select the rows whose country record fuzzily matches to england . among these rows , select the rows whose score record is equal to 71 . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; country ; england } ; score ; 71 } } ; 3 }
select the row whose built year record of all rows is 2nd minimum . the warship record of this row is covadonga .
Output: eq { hop { nth_argmin { all_rows ; built year ; 2 } ; warship } ; covadonga }
select the rows whose time of day record fuzzily matches to afternoon . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; time of day ; afternoon } } ; 3 }
select the rows whose school record fuzzily matches to jackson . take the join date record of this row . select the rows whose school record fuzzily matches to lake . take the join date record of this row . the second record is 23 years larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; school ; jackson } ; join date } ; hop { filter_eq { all_rows ; school ; lake } ; join date } } ; -23 years }
select the rows whose name record is arbitrary . the number of such rows is 10 .
Output: eq { count { filter_all { all_rows ; name } } ; 10 }
for the gold records of all rows , most of them are less than 2 .
Output: most_less { all_rows ; gold ; 2 }
select the row whose round record of all rows is 3rd maximum . the player record of this row is tyree davis .
Output: eq { hop { nth_argmax { all_rows ; round ; 3 } ; player } ; tyree davis }
select the rows whose date record fuzzily matches to december . among these rows , select the rows whose game site record fuzzily matches to rich stadium . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; date ; december } ; game site ; rich stadium } } ; 2 }
select the row whose year ( ceremony ) record of all rows is 1st maximum . the film title used in nomination record of this row is labyrinth .
Output: eq { hop { nth_argmax { all_rows ; year ( ceremony ) ; 1 } ; film title used in nomination } ; labyrinth }
select the rows whose vacator record fuzzily matches to john l mcclellan ( d ) . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to lee metcalf ( d ) . take the reason for change record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; vacator ; john l mcclellan ( d ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; lee metcalf ( d ) } ; reason for change } }
for the wins records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; wins ; 10 }
select the rows whose 2006 record fuzzily matches to 1r . there is only one such row in the table . the tournament record of this unqiue row is australian open .
Output: and { only { filter_eq { all_rows ; 2006 ; 1r } } ; eq { hop { filter_eq { all_rows ; 2006 ; 1r } ; tournament } ; australian open } }
select the rows whose location record fuzzily matches to briggs stadium . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; briggs stadium } } ; 3 }
select the rows whose construction start record fuzzily matches to 1985 . there is only one such row in the table . the unit record of this unqiue row is kakrapar 2 .
Output: and { only { filter_eq { all_rows ; construction start ; 1985 } } ; eq { hop { filter_eq { all_rows ; construction start ; 1985 } ; unit } ; kakrapar 2 } }
select the rows whose name record fuzzily matches to dick farman . take the overall record of this row . select the rows whose name record fuzzily matches to paul coop . take the overall record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; dick farman } ; overall } ; hop { filter_eq { all_rows ; name ; paul coop } ; overall } }
the sum of the score record of all rows is 30 .
Output: round_eq { sum { all_rows ; score } ; 30 }
select the rows whose county record fuzzily matches to essex , nj . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; county ; essex , nj } } ; 6 }
the average of the winner 's share record of all rows is 355169.4 .
Output: round_eq { avg { all_rows ; winner 's share } ; 355169.4 }
for the played records of all rows , all of them are equal to 16 .
Output: all_eq { all_rows ; played ; 16 }
the average of the score record of all rows is 100 .
Output: round_eq { avg { all_rows ; score } ; 100 }
select the row whose game record of all rows is minimum . the location attendance record of this row is pepsi center 14243 .
Output: eq { hop { argmin { all_rows ; game } ; location attendance } ; pepsi center 14243 }
select the rows whose visitor record fuzzily matches to edmonton . take the date record of this row . select the rows whose visitor record fuzzily matches to colorado . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; visitor ; edmonton } ; date } ; hop { filter_eq { all_rows ; visitor ; colorado } ; date } }
select the rows whose designated home record fuzzily matches to miami dolphins . take the year record of this row . select the rows whose designated home record fuzzily matches to new orleans saints . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; designated home ; miami dolphins } ; year } ; hop { filter_eq { all_rows ; designated home ; new orleans saints } ; year } }
for the lead records of all rows , most of them are greater than or equal to 10 .
Output: most_greater_eq { all_rows ; lead ; 10 }
select the rows whose writer / composer record fuzzily matches to ken walther . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; writer / composer ; ken walther } } ; 2 }
for the opened records of all rows , most of them are less than 2000 .
Output: most_less { all_rows ; opened ; 2000 }
select the rows whose laps record is greater than 312 . there is only one such row in the table .
Output: only { filter_greater { all_rows ; laps ; 312 } }
for the comment records of all rows , most of them fuzzily match to landed with 747 .
Output: most_eq { all_rows ; comment ; landed with 747 }
select the rows whose animal name record fuzzily matches to the rabbits . take the tv seasons record of this row . select the rows whose animal name record fuzzily matches to mole . take the tv seasons record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; animal name ; the rabbits } ; tv seasons } ; hop { filter_eq { all_rows ; animal name ; mole } ; tv seasons } }
select the rows whose year record is equal to 2013 . there is only one such row in the table . the nominated work record of this unqiue row is commitment .
Output: and { only { filter_eq { all_rows ; year ; 2013 } } ; eq { hop { filter_eq { all_rows ; year ; 2013 } ; nominated work } ; commitment } }
for the bronze records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; bronze ; 0 }
select the rows whose selected caribbean and n latin america countries record fuzzily matches to mexico . take the internl tourist arrivals 2011 ( x1000 ) record of this row . select the rows whose selected caribbean and n latin america countries record fuzzily matches to barbados . take the internl tourist arrivals 2011 ( x1000 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; selected caribbean and n latin america countries ; mexico } ; internl tourist arrivals 2011 ( x1000 ) } ; hop { filter_eq { all_rows ; selected caribbean and n latin america countries ; barbados } ; internl tourist arrivals 2011 ( x1000 ) } }
select the row whose laps record of all rows is maximum . the year record of this row is 2003 .
Output: eq { hop { argmax { all_rows ; laps } ; year } ; 2003 }
select the rows whose position record fuzzily matches to defensive back . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; defensive back } } ; 2 }
select the rows whose headquarters record fuzzily matches to japan . there is only one such row in the table . the name record of this unqiue row is toyota motor corporation .
Output: and { only { filter_eq { all_rows ; headquarters ; japan } } ; eq { hop { filter_eq { all_rows ; headquarters ; japan } ; name } ; toyota motor corporation } }
select the rows whose cuts made record is less than 20 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; cuts made ; 20 } } ; 3 }
select the row whose winnings record of all rows is 2nd maximum . the year record of this row is 2003 .
Output: eq { hop { nth_argmax { all_rows ; winnings ; 2 } ; year } ; 2003 }
select the rows whose max memory record fuzzily matches to 128 gb . among these rows , select the rows whose max processors record fuzzily matches to 1 ultrasparc t2 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; max memory ; 128 gb } ; max processors ; 1 ultrasparc t2 } } ; 2 }
select the rows whose game site record fuzzily matches to memorial stadium . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; game site ; memorial stadium } } ; 6 }
the sum of the quantity made record of all rows is 151 .
Output: round_eq { sum { all_rows ; quantity made } ; 151 }
select the rows whose position record is arbitrary . the number of such rows is 7 .
Output: eq { count { filter_all { all_rows ; position } } ; 7 }
select the row whose rounds record of all rows is maximum . the team record of this row is henderson motorsports .
Output: eq { hop { argmax { all_rows ; rounds } ; team } ; henderson motorsports }
select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is george m grant .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; george m grant }
select the rows whose venue record fuzzily matches to home . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; venue ; home } } ; 4 }
select the rows whose nationality record fuzzily matches to united kingdom . there is only one such row in the table . the pick record of this unqiue row is 51 .
Output: and { only { filter_eq { all_rows ; nationality ; united kingdom } } ; eq { hop { filter_eq { all_rows ; nationality ; united kingdom } ; pick } ; 51 } }
the sum of the score record of all rows is 12 .
Output: round_eq { sum { all_rows ; score } ; 12 }
select the row whose score record of all rows is maximum . the team 1 record of this row is as monaco ( d1 ) .
Output: eq { hop { argmax { all_rows ; score } ; team 1 } ; as monaco ( d1 ) }
select the rows whose name record fuzzily matches to kevin barnett . take the weight record of this row . select the rows whose name record fuzzily matches to gabriel gardner . take the weight record of this row . the second record is 9 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; kevin barnett } ; weight } ; hop { filter_eq { all_rows ; name ; gabriel gardner } ; weight } } ; -9 }
select the rows whose college record fuzzily matches to utah . there is only one such row in the table . the name record of this unqiue row is dewey nelson .
Output: and { only { filter_eq { all_rows ; college ; utah } } ; eq { hop { filter_eq { all_rows ; college ; utah } ; name } ; dewey nelson } }
select the rows whose date record fuzzily matches to sept . the sum of the wildcats points record of these rows is 40 .
Output: round_eq { sum { filter_eq { all_rows ; date ; sept } ; wildcats points } ; 40 }
select the rows whose opponent record fuzzily matches to oakland raiders . there is only one such row in the table . the date record of this unqiue row is october 6 , 1968 .
Output: and { only { filter_eq { all_rows ; opponent ; oakland raiders } } ; eq { hop { filter_eq { all_rows ; opponent ; oakland raiders } ; date } ; october 6 , 1968 } }
select the rows whose directed by record fuzzily matches to michael lange . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; directed by ; michael lange } } ; 9 }
select the row whose first elected record of all rows is 1st maximum . the incumbent record of this row is dale alford .
Output: eq { hop { nth_argmax { all_rows ; first elected ; 1 } ; incumbent } ; dale alford }
for the batting style records of all rows , most of them fuzzily match to right hand bat .
Output: most_eq { all_rows ; batting style ; right hand bat }
select the rows whose nationality record does not match to ireland . the 1st maximum goals record of these rows is 138 . the name record of the row with 1st maximum goals record is johnny armstrong .
Output: and { eq { nth_max { filter_not_eq { all_rows ; nationality ; ireland } ; goals ; 1 } ; 138 } ; eq { hop { nth_argmax { filter_not_eq { all_rows ; nationality ; ireland } ; goals ; 1 } ; name } ; johnny armstrong } }
select the rows whose channel record fuzzily matches to rai 2 . take the launch date record of this row . select the rows whose channel record fuzzily matches to canale 5 . take the launch date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; channel ; rai 2 } ; launch date } ; hop { filter_eq { all_rows ; channel ; canale 5 } ; launch date } }
select the rows whose attendance record fuzzily matches to rose garden . the sum of the attendance record of these rows is 121570 .
Output: round_eq { sum { filter_eq { all_rows ; attendance ; rose garden } ; attendance } ; 121570 }
select the rows whose yards record is equal to 0 . there is only one such row in the table . the player record of this unqiue row is andy kelly .
Output: and { only { filter_eq { all_rows ; yards ; 0 } } ; eq { hop { filter_eq { all_rows ; yards ; 0 } ; player } ; andy kelly } }
select the row whose points record of all rows is maximum . the year record of this row is 2007 - 08 .
Output: eq { hop { argmax { all_rows ; points } ; year } ; 2007 - 08 }
the average of the prize money record of all rows is 4831665 .
Output: round_eq { avg { all_rows ; prize money } ; 4831665 }
select the rows whose party record fuzzily matches to democratic . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; party ; democratic } } ; 5 }
select the rows whose competition record fuzzily matches to k league classic . there is only one such row in the table . the name record of this unqiue row is molina .
Output: and { only { filter_eq { all_rows ; competition ; k league classic } } ; eq { hop { filter_eq { all_rows ; competition ; k league classic } ; name } ; molina } }
select the rows whose venue record fuzzily matches to san francisco . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; venue ; san francisco } } ; 2 }
select the rows whose written by record fuzzily matches to mark ellis , stephanie morgenstern & alex levine . there is only one such row in the table . the title record of this unqiue row is priority of life .
Output: and { only { filter_eq { all_rows ; written by ; mark ellis , stephanie morgenstern & alex levine } } ; eq { hop { filter_eq { all_rows ; written by ; mark ellis , stephanie morgenstern & alex levine } ; title } ; priority of life } }
for the aircraft movements records of all rows , most of them are greater than 100 .
Output: most_greater { all_rows ; aircraft movements ; 100 }
for the team records of all rows , most of them fuzzily match to suzuki .
Output: most_eq { all_rows ; team ; suzuki }
the sum of the points record of all rows is 19 .
Output: round_eq { sum { all_rows ; points } ; 19 }
select the rows whose director record fuzzily matches to pete travis . there is only one such row in the table . the episode record of this unqiue row is episode 5 .
Output: and { only { filter_eq { all_rows ; director ; pete travis } } ; eq { hop { filter_eq { all_rows ; director ; pete travis } ; episode } ; episode 5 } }
select the rows whose format record fuzzily matches to campus radio . there is only one such row in the table . the call sign record of this unqiue row is cfak - fm .
Output: and { only { filter_eq { all_rows ; format ; campus radio } } ; eq { hop { filter_eq { all_rows ; format ; campus radio } ; call sign } ; cfak - fm } }
select the rows whose date record fuzzily matches to 12 june 1972 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; date ; 12 june 1972 } ; crowd ; 20000 } } ; 2 }
select the row whose cuts made record of all rows is maximum . the year record of this row is 2009 .
Output: eq { hop { argmax { all_rows ; cuts made } ; year } ; 2009 }
for the name records of all rows , most of them fuzzily match to - .
Output: most_eq { all_rows ; name ; - }
for the type records of all rows , most of them fuzzily match to norteño .
Output: most_eq { all_rows ; type ; norteño }
for the bronze records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; bronze ; 10 }
the sum of the population record of all rows is 46350 .
Output: round_eq { sum { all_rows ; population } ; 46350 }
select the rows whose tournament record fuzzily matches to israel open . take the date record of this row . select the rows whose tournament record fuzzily matches to president 's cup ( tennis ) . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; tournament ; israel open } ; date } ; hop { filter_eq { all_rows ; tournament ; president 's cup ( tennis ) } ; date } }
select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . select the rows whose venue record fuzzily matches to lake oval . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; venue ; mcg } ; crowd } ; hop { filter_eq { all_rows ; venue ; lake oval } ; crowd } }
select the rows whose interview subject record fuzzily matches to tobey maguire . take the date record of this row . select the rows whose interview subject record fuzzily matches to mike piazza . take the date record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; interview subject ; tobey maguire } ; date } ; hop { filter_eq { all_rows ; interview subject ; mike piazza } ; date } }
select the rows whose week record fuzzily matches to top 24 ( 12 women ) . take the order record of this row . select the rows whose week record fuzzily matches to top 20 ( 10 women ) . take the order record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; week ; top 24 ( 12 women ) } ; order } ; hop { filter_eq { all_rows ; week ; top 20 ( 10 women ) } ; order } }
select the rows whose stadium record fuzzily matches to mikheil meskhi stadium . take the capacity record of this row . select the rows whose stadium record fuzzily matches to sasha kvaratskhelia stadium . take the capacity record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; stadium ; mikheil meskhi stadium } ; capacity } ; hop { filter_eq { all_rows ; stadium ; sasha kvaratskhelia stadium } ; capacity } }
for the rare metals records of all rows , most of them fuzzily match to no .
Output: most_eq { all_rows ; rare metals ; no }
select the rows whose opponents record fuzzily matches to palamós . select the row whose kick off record of these rows is minimum . the referee record of this row is daudén ibáñez .
Output: eq { hop { argmin { filter_eq { all_rows ; opponents ; palamós } ; kick off } ; referee } ; daudén ibáñez }
select the rows whose location record fuzzily matches to wabash . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; wabash } } ; 3 }
select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose party record fuzzily matches to democratic . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; party ; democratic } } ; 2 }