niryuu/sni-each-converted huggingface dataset:
select the rows whose nationality record fuzzily matches to sweden . there is only one such row in the table . the player record of this unqiue row is jacob markstrom .
Output: and { only { filter_eq { all_rows ; nationality ; sweden } } ; eq { hop { filter_eq { all_rows ; nationality ; sweden } ; player } ; jacob markstrom } }
select the rows whose pole position record fuzzily matches to ayrton senna . the number of such rows is 13 .
Output: eq { count { filter_eq { all_rows ; pole position ; ayrton senna } } ; 13 }
select the row whose attendance record of all rows is 3rd maximum . the opponent record of this row is denver broncos .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; opponent } ; denver broncos }
for the played records of all rows , all of them are equal to 18 .
Output: all_eq { all_rows ; played ; 18 }
select the rows whose yard name record fuzzily matches to delta shipbuilding co . take the total vessels built for usmc record of this row . select the rows whose yard name record fuzzily matches to pennsylvania shipyards . take the total vessels built for usmc record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; yard name ; delta shipbuilding co } ; total vessels built for usmc } ; hop { filter_eq { all_rows ; yard name ; pennsylvania shipyards } ; total vessels built for usmc } }
select the rows whose label record fuzzily matches to columbia . take the date record of this row . select the rows whose label record fuzzily matches to legacy recordings . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; label ; columbia } ; date } ; hop { filter_eq { all_rows ; label ; legacy recordings } ; date } }
select the row whose took office record of all rows is 4th minimum . the delegate record of this row is brian k mchale .
Output: eq { hop { nth_argmin { all_rows ; took office ; 4 } ; delegate } ; brian k mchale }
select the rows whose year record is equal to 2011 . there is only one such row in the table . the title of movie record of this unqiue row is already famous 《 一泡而紅 》 .
Output: and { only { filter_eq { all_rows ; year ; 2011 } } ; eq { hop { filter_eq { all_rows ; year ; 2011 } ; title of movie } ; already famous 《 一泡而紅 》 } }
select the rows whose engine name record fuzzily matches to 1.6 tdi ecomotive . there is only one such row in the table .
Output: only { filter_eq { all_rows ; engine name ; 1.6 tdi ecomotive } }
select the row whose pieces record of all rows is maximum . the release record of this row is 1981 .
Output: eq { hop { argmax { all_rows ; pieces } ; release } ; 1981 }
select the rows whose interregnum began record fuzzily matches to 20 february 1790 death of joseph ii . take the duration record of this row . select the rows whose interregnum began record fuzzily matches to 1 march 1792 death of leopold ii . take the duration record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; interregnum began ; 20 february 1790 death of joseph ii } ; duration } ; hop { filter_eq { all_rows ; interregnum began ; 1 march 1792 death of leopold ii } ; duration } }
select the rows whose directed by record fuzzily matches to guy norman bee . take the episode record of this row . select the rows whose directed by record fuzzily matches to james head . take the episode record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; directed by ; guy norman bee } ; episode } ; hop { filter_eq { all_rows ; directed by ; james head } ; episode } }
select the row whose points record of all rows is maximum . the team record of this row is soviet union .
Output: eq { hop { argmax { all_rows ; points } ; team } ; soviet union }
select the rows whose week record is greater than or equal to 13 . the sum of the attendance record of these rows is 95,901 .
Output: round_eq { sum { filter_greater_eq { all_rows ; week ; 13 } ; attendance } ; 95,901 }
for the high points records of all rows , most of them fuzzily match to tim duncan .
Output: most_eq { all_rows ; high points ; tim duncan }
the maximum qual record of all rows is 225.496 .
Output: eq { max { all_rows ; qual } ; 225.496 }
for the location records of all rows , most of them fuzzily match to usa .
Output: most_eq { all_rows ; location ; usa }
select the rows whose mascot record fuzzily matches to tigers . there is only one such row in the table . the school record of this unqiue row is yorktown .
Output: and { only { filter_eq { all_rows ; mascot ; tigers } } ; eq { hop { filter_eq { all_rows ; mascot ; tigers } ; school } ; yorktown } }
for the venue records of all rows , most of them fuzzily match to millennium stadium .
Output: most_eq { all_rows ; venue ; millennium stadium }
select the rows whose winner record fuzzily matches to 6 - 1 , 6 - 1 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; winner ; 6 - 1 , 6 - 1 } }
select the rows whose name record fuzzily matches to amangul mollayeva . take the ties record of this row . select the rows whose name record fuzzily matches to ayna ereshova . take the ties record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; amangul mollayeva } ; ties } ; hop { filter_eq { all_rows ; name ; ayna ereshova } ; ties } }
the average of the points record of all rows is 30 .
Output: round_eq { avg { all_rows ; points } ; 30 }
select the row whose distance record of all rows is 1st maximum . the race record of this row is mercedes classic .
Output: eq { hop { nth_argmax { all_rows ; distance ; 1 } ; race } ; mercedes classic }
for the crowd records of all rows , all of them are greater than or equal to 8000 .
Output: all_greater_eq { all_rows ; crowd ; 8000 }
select the row whose us viewers ( millions ) record of all rows is maximum . the no in series record of this row is 1 .
Output: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; no in series } ; 1 }
select the row whose 1st run record of all rows is 2nd minimum . the name record of this row is donny robinson ( usa ) .
Output: eq { hop { nth_argmin { all_rows ; 1st run ; 2 } ; name } ; donny robinson ( usa ) }
select the rows whose launched record is equal to 1958 . among these rows , select the rows whose capacity , mln tpa record fuzzily matches to 12 , 0 . there is only one such row in the table . the name record of this unqiue row is lukoil - permnefteorgsintez .
Output: and { only { filter_eq { filter_eq { all_rows ; launched ; 1958 } ; capacity , mln tpa ; 12 , 0 } } ; eq { hop { filter_eq { filter_eq { all_rows ; launched ; 1958 } ; capacity , mln tpa ; 12 , 0 } ; name } ; lukoil - permnefteorgsintez } }
the maximum highest record of all rows is 1398 . the team record of the row with superlative highest record is dumbarton .
Output: and { eq { max { all_rows ; highest } ; 1398 } ; eq { hop { argmax { all_rows ; highest } ; team } ; dumbarton } }
select the rows whose games record is equal to 74 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; games ; 74 } } ; 4 }
select the rows whose section of atlantic city record fuzzily matches to uptown . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; section of atlantic city ; uptown } } ; 4 }
select the rows whose lead record is greater than or equal to 10 % . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { all_rows ; lead ; 10 % } } ; 3 }
the average of the enrollment record of all rows is 4067 .
Output: round_eq { avg { all_rows ; enrollment } ; 4067 }
select the row whose total record of all rows is maximum . the nation record of this row is united states .
Output: eq { hop { argmax { all_rows ; total } ; nation } ; united states }
select the rows whose winning team record fuzzily matches to texas . among these rows , select the rows whose winning pitcher record fuzzily matches to chris young . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; winning team ; texas } ; winning pitcher ; chris young } } ; 2 }
select the rows whose opponent record fuzzily matches to new york rangers . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; opponent ; new york rangers } } ; 3 }
select the rows whose date record fuzzily matches to september 22 . take the result record of this row . select the rows whose date record fuzzily matches to november 17 . take the result record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; september 22 } ; result } ; hop { filter_eq { all_rows ; date ; november 17 } ; result } }
select the rows whose player record is arbitrary . the number of such rows is 10 .
Output: eq { count { filter_all { all_rows ; player } } ; 10 }
select the row whose wins record of all rows is maximum . the wins record of this row is 2 .
Output: eq { hop { argmax { all_rows ; wins } ; wins } ; 2 }
select the rows whose 1st leg record fuzzily matches to 1 - 1 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; 1st leg ; 1 - 1 } } ; 2 }
for the reason for termination records of all rows , most of them fuzzily match to death .
Output: most_eq { all_rows ; reason for termination ; death }
select the row whose events record of all rows is 2nd maximum . the tournament record of this row is pga championship .
Output: eq { hop { nth_argmax { all_rows ; events ; 2 } ; tournament } ; pga championship }
select the rows whose party record fuzzily matches to democratic . among these rows , select the rows whose first elected record is equal to 1974 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; party ; democratic } ; first elected ; 1974 } } ; 2 }
select the rows whose al - wehdat wins record is greater than 20 . there is only one such row in the table . the tournament record of this unqiue row is jordan premier league .
Output: and { only { filter_greater { all_rows ; al - wehdat wins ; 20 } } ; eq { hop { filter_greater { all_rows ; al - wehdat wins ; 20 } ; tournament } ; jordan premier league } }
select the rows whose nation record is arbitrary . the number of such rows is 11 .
Output: eq { count { filter_all { all_rows ; nation } } ; 11 }
select the rows whose athlete record fuzzily matches to shawn crawford . take the result record of this row . select the rows whose athlete record fuzzily matches to kevin little . take the result record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; athlete ; shawn crawford } ; result } ; hop { filter_eq { all_rows ; athlete ; kevin little } ; result } }
select the rows whose try bonus record is equal to 1 . there is only one such row in the table . the club record of this unqiue row is cefn coed rfc .
Output: and { only { filter_eq { all_rows ; try bonus ; 1 } } ; eq { hop { filter_eq { all_rows ; try bonus ; 1 } ; club } ; cefn coed rfc } }
select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the date record of this unqiue row is 3 may 1998 .
Output: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 3 may 1998 } }
select the row whose total record of all rows is 1st maximum . the nation record of this row is venezuela .
Output: eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; venezuela }
select the row whose enrollment record of all rows is 2nd maximum . the name record of this row is orangeville district secondary school .
Output: eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; name } ; orangeville district secondary school }
select the row whose 1st leg record of all rows is maximum . the team 1 record of this row is flamengo . the team 2 record of this row is américa .
Output: and { eq { hop { argmax { all_rows ; 1st leg } ; team 1 } ; flamengo } ; eq { hop { argmax { all_rows ; 1st leg } ; team 2 } ; américa } }
the average of the 1990 - 95 record of all rows is 0.27 .
Output: round_eq { avg { all_rows ; 1990 - 95 } ; 0.27 }
the 1st minimum date record of all rows is july 23 , 2002 .
Output: eq { nth_min { all_rows ; date ; 1 } ; july 23 , 2002 }
select the rows whose format record fuzzily matches to cd . the 1st minimum date record of these rows is september 10 , 1986 . the label record of the row with 1st minimum date record is alfa records .
Output: and { eq { nth_min { filter_eq { all_rows ; format ; cd } ; date ; 1 } ; september 10 , 1986 } ; eq { hop { nth_argmin { filter_eq { all_rows ; format ; cd } ; date ; 1 } ; label } ; alfa records } }
the average of the points record of all rows is 158 .
Output: round_eq { avg { all_rows ; points } ; 158 }
select the rows whose decision record fuzzily matches to joseph . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; decision ; joseph } } ; 4 }
select the rows whose ostrich record fuzzily matches to egyptian goose . there is only one such row in the table .
Output: only { filter_eq { all_rows ; ostrich ; egyptian goose } }
the average of the total passengers record of all rows is 4773780 .
Output: round_eq { avg { all_rows ; total passengers } ; 4773780 }
for the details records of all rows , most of them fuzzily match to abandoned part of northern heights project .
Output: most_eq { all_rows ; details ; abandoned part of northern heights project }
select the row whose agg record of all rows is maximum . the team 1 record of this row is canon yaoundé .
Output: eq { hop { argmax { all_rows ; agg } ; team 1 } ; canon yaoundé }
select the rows whose opponent record fuzzily matches to philadelphia eagles . the average of the attendance record of these rows is 46450 .
Output: round_eq { avg { filter_eq { all_rows ; opponent ; philadelphia eagles } ; attendance } ; 46450 }
select the rows whose country record fuzzily matches to belgium . take the total record of this row . select the rows whose country record fuzzily matches to brazil . take the total record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; country ; belgium } ; total } ; hop { filter_eq { all_rows ; country ; brazil } ; total } }
the average of the tests won by england record of all rows is 0.47 .
Output: round_eq { avg { all_rows ; tests won by england } ; 0.47 }
the average of the viewers ( in millions ) record of all rows is 7.76 .
Output: round_eq { avg { all_rows ; viewers ( in millions ) } ; 7.76 }
select the rows whose winning team record fuzzily matches to hwa 1 . take the date record of this row . select the rows whose winning team record fuzzily matches to opel team phoenix . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; winning team ; hwa 1 } ; date } ; hop { filter_eq { all_rows ; winning team ; opel team phoenix } ; date } }
select the rows whose chassis record fuzzily matches to lola t100 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; chassis ; lola t100 } } ; 2 }
for the manner of departure records of all rows , most of them fuzzily match to resigned .
Output: most_eq { all_rows ; manner of departure ; resigned }
select the row whose attendance record of all rows is maximum . the date record of this row is december 16 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; december 16 }
select the rows whose home record fuzzily matches to los angeles . for the attendance records of these rows , most of them are equal to 18118 .
Output: most_eq { filter_eq { all_rows ; home ; los angeles } ; attendance ; 18118 }
select the rows whose reason for change record fuzzily matches to died . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; reason for change ; died } } ; 2 }
select the row whose march record of all rows is 12th minimum . the score record of this row is 8 - 7 .
Output: eq { hop { nth_argmin { all_rows ; march ; 12 } ; score } ; 8 - 7 }
select the rows whose architect record fuzzily matches to john mcarthur , jr . select the row whose height ft ( m ) record of these rows is 1st maximum . the name record of this row is philadelphia city hall .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; architect ; john mcarthur , jr } ; height ft ( m ) ; 1 } ; name } ; philadelphia city hall }
select the rows whose result record fuzzily matches to injured . there is only one such row in the table . the week record of this unqiue row is 5 .
Output: and { only { filter_eq { all_rows ; result ; injured } } ; eq { hop { filter_eq { all_rows ; result ; injured } ; week } ; 5 } }
select the rows whose player record fuzzily matches to jack snow . take the pick record of this row . select the rows whose player record fuzzily matches to lou bobich . take the pick record of this row . the first record is 1 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; jack snow } ; pick } ; hop { filter_eq { all_rows ; player ; lou bobich } ; pick } } ; 1 }
the average of the max pressure record of all rows is 26611 .
Output: round_eq { avg { all_rows ; max pressure } ; 26611 }
select the row whose attendance / g record of all rows is maximum . the season record of this row is 2004 .
Output: eq { hop { argmax { all_rows ; attendance / g } ; season } ; 2004 }
select the row whose frequency mhz record of all rows is 2nd maximum . the city of license record of this row is lewis , kansas .
Output: eq { hop { nth_argmax { all_rows ; frequency mhz ; 2 } ; city of license } ; lewis , kansas }
select the rows whose loco name record fuzzily matches to earl of mount edgcumbe . take the build date record of this row . select the rows whose loco name record fuzzily matches to pyramus . take the build date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; loco name ; earl of mount edgcumbe } ; build date } ; hop { filter_eq { all_rows ; loco name ; pyramus } ; build date } }
select the rows whose year record fuzzily matches to 1996 . take the winnings record of this row . select the rows whose year record fuzzily matches to 1998 . take the winnings record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1996 } ; winnings } ; hop { filter_eq { all_rows ; year ; 1998 } ; winnings } }
select the row whose start record of all rows is minimum . the year record of this row is 1954 .
Output: eq { hop { argmin { all_rows ; start } ; year } ; 1954 }
select the rows whose date record fuzzily matches to october 16 , 1977 . take the attendance record of this row . select the rows whose date record fuzzily matches to november 13 , 1977 . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; october 16 , 1977 } ; attendance } ; hop { filter_eq { all_rows ; date ; november 13 , 1977 } ; attendance } }
the maximum score record of all rows is l 83 - 74 . the date record of the row with superlative score record is february 5 .
Output: and { eq { max { all_rows ; score } ; l 83 - 74 } ; eq { hop { argmax { all_rows ; score } ; date } ; february 5 } }
select the rows whose sport record fuzzily matches to wrestling . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; sport ; wrestling } } ; 2 }
select the rows whose country record fuzzily matches to united states . select the row whose earnings record of these rows is 2nd maximum . the player record of this row is george archer .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; country ; united states } ; earnings ; 2 } ; player } ; george archer }
select the rows whose qual 1 record is equal to - . there is only one such row in the table . the name record of this unqiue row is a j allmendinger .
Output: and { only { filter_eq { all_rows ; qual 1 ; - } } ; eq { hop { filter_eq { all_rows ; qual 1 ; - } ; name } ; a j allmendinger } }
select the row whose enrollment record of all rows is 2nd minimum . the institution record of this row is philander smith college .
Output: eq { hop { nth_argmin { all_rows ; enrollment ; 2 } ; institution } ; philander smith college }
select the rows whose country record fuzzily matches to united states . the number of such rows is 11 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 11 }
select the rows whose player record fuzzily matches to bob tway . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to phil mickelson . take the year ( s ) won record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; bob tway } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; phil mickelson } ; year ( s ) won } }
select the rows whose tournament record fuzzily matches to shell houston open . take the margin of victory record of this row . select the rows whose tournament record fuzzily matches to the players championship . take the margin of victory record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tournament ; shell houston open } ; margin of victory } ; hop { filter_eq { all_rows ; tournament ; the players championship } ; margin of victory } }
select the rows whose result record fuzzily matches to won . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; result ; won } } ; 8 }
the average of the notes record of all rows is 1.86 .
Output: round_eq { avg { all_rows ; notes } ; 1.86 }
select the rows whose rider record fuzzily matches to wattie brown . take the time record of this row . select the rows whose rider record fuzzily matches to chris swallow . take the time record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; rider ; wattie brown } ; time } ; hop { filter_eq { all_rows ; rider ; chris swallow } ; time } }
select the row whose total record of all rows is 2nd maximum . the player record of this row is djibril cisse .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; player } ; djibril cisse }
select the row whose score record of all rows is 2nd minimum . the date record of this row is august 22 , 2007 .
Output: eq { hop { nth_argmin { all_rows ; score ; 2 } ; date } ; august 22 , 2007 }
select the rows whose wins record is greater than 4 . among these rows , select the rows whose loses record is equal to 2 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; wins ; 4 } ; loses ; 2 } } ; 2 }
for the prone records of all rows , most of them are less than 200 .
Output: most_less { all_rows ; prone ; 200 }
select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is the new normal . the directed by record of this row is michael lange .
Output: and { eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; the new normal } ; eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; directed by } ; michael lange } }
select the rows whose title record fuzzily matches to mount rushmore . take the original air date record of this row . select the rows whose title record fuzzily matches to salem . take the original air date record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; title ; mount rushmore } ; original air date } ; hop { filter_eq { all_rows ; title ; salem } ; original air date } }
select the rows whose position record fuzzily matches to rhp . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; position ; rhp } } ; 6 }
select the row whose number in fleet record of all rows is maximum . the chassis model record of this row is 18.280 hocl - nl . the body model record of this row is abm cb64a .
Output: and { eq { hop { argmax { all_rows ; number in fleet } ; chassis model } ; 18.280 hocl - nl } ; eq { hop { argmax { all_rows ; number in fleet } ; body model } ; abm cb64a } }
select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is princes park .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; princes park }
select the rows whose points record is less than 260 . there is only one such row in the table . the name record of this unqiue row is andreas kofler .
Output: and { only { filter_less { all_rows ; points ; 260 } } ; eq { hop { filter_less { all_rows ; points ; 260 } ; name } ; andreas kofler } }
select the rows whose area ( km 2 ) record is less than 10000 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; area ( km 2 ) ; 10000 } } ; 3 }
for the winner records of all rows , most of them fuzzily match to aus .
Output: most_eq { all_rows ; winner ; aus }
select the row whose enrollment record of all rows is minimum . the school record of this row is cloverdale .
Output: eq { hop { argmin { all_rows ; enrollment } ; school } ; cloverdale }
select the rows whose season record is greater than or equal to 1973 . the sum of the won record of these rows is 77 .
Output: round_eq { sum { filter_greater_eq { all_rows ; season ; 1973 } ; won } ; 77 }
select the rows whose place ( result ) record fuzzily matches to 1st runner - up . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; place ( result ) ; 1st runner - up } } ; 2 }
select the rows whose written by record fuzzily matches to donald p bellisario . there is only one such row in the table . the title record of this unqiue row is we the people .
Output: and { only { filter_eq { all_rows ; written by ; donald p bellisario } } ; eq { hop { filter_eq { all_rows ; written by ; donald p bellisario } ; title } ; we the people } }
select the rows whose opponent record fuzzily matches to buffalo sabres . select the row whose game record of these rows is 2nd minimum . the november record of this row is 22 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; buffalo sabres } ; game ; 2 } ; november } ; 22 }
select the rows whose vacator record fuzzily matches to william f norrell ( d ) . take the reason for change record of this row . select the rows whose vacator record fuzzily matches to walter m mumma ( r ) . 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 ; william f norrell ( d ) } ; reason for change } ; hop { filter_eq { all_rows ; vacator ; walter m mumma ( r ) } ; reason for change } }
select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is philadelphia eagles .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; philadelphia eagles }
for the original air date records of all rows , most of them fuzzily match to 2007 .
Output: most_eq { all_rows ; original air date ; 2007 }
select the rows whose points record is greater than 90 . there is only one such row in the table .
Output: only { filter_greater { all_rows ; points ; 90 } }
select the row whose mintage record of all rows is maximum . the theme record of this row is steam buggy .
Output: eq { hop { argmax { all_rows ; mintage } ; theme } ; steam buggy }
the average of the elevation ( m ) record of all rows is 2069.16 .
Output: round_eq { avg { all_rows ; elevation ( m ) } ; 2069.16 }
select the rows whose title record fuzzily matches to viscount . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; title ; viscount } } ; 3 }
select the row whose win % record of all rows is 1st maximum . the opposition record of this row is pune warriors india .
Output: eq { hop { nth_argmax { all_rows ; win % ; 1 } ; opposition } ; pune warriors india }
select the rows whose event record is arbitrary . the number of such rows is 14 .
Output: eq { count { filter_all { all_rows ; event } } ; 14 }
select the rows whose industry record fuzzily matches to oil and gas . the sum of the profits ( billion ) record of these rows is 151.9 .
Output: round_eq { sum { filter_eq { all_rows ; industry ; oil and gas } ; profits ( billion ) } ; 151.9 }
select the rows whose game record fuzzily matches to 69 . take the score record of this row . select the rows whose game record fuzzily matches to 66 . take the score record of this row . the first record is greater than the second record . the opponent record of the first row is boston bruins . the opponent record of the second row is boston bruins .
Output: and { greater { hop { filter_eq { all_rows ; game ; 69 } ; score } ; hop { filter_eq { all_rows ; game ; 66 } ; score } } ; and { eq { hop { filter_eq { all_rows ; game ; 69 } ; opponent } ; boston bruins } ; eq { hop { filter_eq { all_rows ; game ; 66 } ; opponent } ; boston bruins } } }
select the rows whose opponent record fuzzily matches to detroit lions . take the date record of this row . select the rows whose opponent record fuzzily matches to los angeles raiders . take the date record of this row . the second record is 7 days larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; opponent ; detroit lions } ; date } ; hop { filter_eq { all_rows ; opponent ; los angeles raiders } ; date } } ; -7 days }
select the rows whose time record fuzzily matches to 12:00 pm . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; time ; 12:00 pm } } ; 3 }
select the rows whose season record is arbitrary . the number of such rows is 10 .
Output: eq { count { filter_all { all_rows ; season } } ; 10 }
select the rows whose driver record fuzzily matches to erik darnell . take the car record of this row . select the rows whose driver record fuzzily matches to bobby east . take the car record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; driver ; erik darnell } ; car } ; hop { filter_eq { all_rows ; driver ; bobby east } ; car } }
the average of the lwat record of all rows is 26 .
Output: round_eq { avg { all_rows ; lwat } ; 26 }
select the rows whose place record fuzzily matches to t2 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; place ; t2 } } ; 7 }
select the rows whose partnerships record fuzzily matches to younis khan / shoaib malik . take the runs ( balls ) record of this row . select the rows whose partnerships record fuzzily matches to gautam gambhir / virender sehwag . take the runs ( balls ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; partnerships ; younis khan / shoaib malik } ; runs ( balls ) } ; hop { filter_eq { all_rows ; partnerships ; gautam gambhir / virender sehwag } ; runs ( balls ) } }
select the rows whose directed by record fuzzily matches to ch greenblatt & william reiss . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; directed by ; ch greenblatt & william reiss } } ; 7 }
select the rows whose money record is greater than 100000 . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; money ; 100000 } } ; 4 }
select the row whose episode number record of all rows is 1st minimum . the air date record of this row is 5 april 2010 .
Output: eq { hop { nth_argmin { all_rows ; episode number ; 1 } ; air date } ; 5 april 2010 }
select the row whose virtual record of all rows is maximum . the call sign record of this row is kndx .
Output: eq { hop { argmax { all_rows ; virtual } ; call sign } ; kndx }
for the attendance records of all rows , most of them are greater than or equal to 40000 .
Output: most_greater_eq { all_rows ; attendance ; 40000 }
select the rows whose high points record fuzzily matches to al harrington . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; high points ; al harrington } } ; 4 }
select the rows whose main venue record fuzzily matches to dover park . there is only one such row in the table . the event name record of this unqiue row is dieppe kite international .
Output: and { only { filter_eq { all_rows ; main venue ; dover park } } ; eq { hop { filter_eq { all_rows ; main venue ; dover park } ; event name } ; dieppe kite international } }
select the row whose attendance record of all rows is 2nd minimum . the date record of this row is december 22 .
Output: eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; december 22 }
select the rows whose event record fuzzily matches to quiksilver pro . among these rows , select the rows whose country record fuzzily matches to australia . there is only one such row in the table .
Output: only { filter_eq { filter_eq { all_rows ; event ; quiksilver pro } ; country ; australia } }
select the rows whose tournament record fuzzily matches to masters tournament . take the top - 5 record of this row . select the rows whose tournament record fuzzily matches to us open . take the top - 5 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 5 } ; hop { filter_eq { all_rows ; tournament ; us open } ; top - 5 } }
for the tires records of all rows , most of them fuzzily match to goodyear .
Output: most_eq { all_rows ; tires ; goodyear }
select the rows whose 2008 club record fuzzily matches to army . there is only one such row in the table . the name record of this unqiue row is guo peng .
Output: and { only { filter_eq { all_rows ; 2008 club ; army } } ; eq { hop { filter_eq { all_rows ; 2008 club ; army } ; name } ; guo peng } }
the average of the points record of all rows is 84 .
Output: round_eq { avg { all_rows ; points } ; 84 }
select the row whose crowd record of all rows is maximum . the venue record of this row is windy hill .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; windy hill }
select the rows whose launched record fuzzily matches to november 1942 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; launched ; november 1942 } } ; 2 }
select the rows whose opposition record fuzzily matches to pune warriors india . take the win % record of this row . select the rows whose opposition record fuzzily matches to kochi tuskers kerala . take the win % record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opposition ; pune warriors india } ; win % } ; hop { filter_eq { all_rows ; opposition ; kochi tuskers kerala } ; win % } }
select the rows whose month record fuzzily matches to april . there is only one such row in the table .
Output: only { filter_eq { all_rows ; month ; april } }
select the row whose attendance record of all rows is 3rd maximum . the game record of this row is 6 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; game } ; 6 }
select the rows whose player record fuzzily matches to kj choi . take the reset points record of this row . select the rows whose player record fuzzily matches to charles howell iii . take the reset points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; kj choi } ; reset points } ; hop { filter_eq { all_rows ; player ; charles howell iii } ; reset points } }
select the rows whose callsign record fuzzily matches to dyjr - fm . take the frequency record of this row . select the rows whose callsign record fuzzily matches to dwdj - fm . take the frequency record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; callsign ; dyjr - fm } ; frequency } ; hop { filter_eq { all_rows ; callsign ; dwdj - fm } ; frequency } }
select the rows whose opponent record fuzzily matches to chicago bears . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 47475 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; chicago bears } ; date ; 2 } ; attendance } ; 47475 }
for the position records of all rows , most of them fuzzily match to guard .
Output: most_eq { all_rows ; position ; guard }
select the row whose points record of all rows is maximum . the team record of this row is olympiacos cfp .
Output: eq { hop { argmax { all_rows ; points } ; team } ; olympiacos cfp }
select the rows whose cores record is equal to 6 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; cores ; 6 } } ; 2 }
select the row whose date record of all rows is maximum . the date record of this row is 7 september 2005 .
Output: eq { hop { argmax { all_rows ; date } ; date } ; 7 september 2005 }
select the rows whose date record fuzzily matches to december . among these rows , select the rows whose result record fuzzily matches to l . there is only one such row in the table . the opponent record of this unqiue row is philadelphia eagles .
Output: and { only { filter_eq { filter_eq { all_rows ; date ; december } ; result ; l } } ; eq { hop { filter_eq { filter_eq { all_rows ; date ; december } ; result ; l } ; opponent } ; philadelphia eagles } }
select the row whose capacity record of all rows is maximum . the home venue record of this row is gwangju world cup stadium .
Output: eq { hop { argmax { all_rows ; capacity } ; home venue } ; gwangju world cup stadium }
select the rows whose category record fuzzily matches to best newcomer . there is only one such row in the table . the year record of this unqiue row is 2008 . the film or series record of this unqiue row is eastenders .
Output: and { only { filter_eq { all_rows ; category ; best newcomer } } ; and { eq { hop { filter_eq { all_rows ; category ; best newcomer } ; year } ; 2008 } ; eq { hop { filter_eq { all_rows ; category ; best newcomer } ; film or series } ; eastenders } } }
select the rows whose country record fuzzily matches to spain . there is only one such row in the table . the player record of this unqiue row is josé maría olazábal .
Output: and { only { filter_eq { all_rows ; country ; spain } } ; eq { hop { filter_eq { all_rows ; country ; spain } ; player } ; josé maría olazábal } }
the average of the faces record of all rows is 66 .
Output: round_eq { avg { all_rows ; faces } ; 66 }
for the run time records of all rows , most of them are greater than 4:00 .
Output: most_greater { all_rows ; run time ; 4:00 }
select the row whose no record of all rows is 3rd maximum . the player record of this row is terry dehere .
Output: eq { hop { nth_argmax { all_rows ; no ; 3 } ; player } ; terry dehere }
select the rows whose competition record fuzzily matches to european zone . select the row whose year record of these rows is minimum . the score record of this row is 1 - 4 .
Output: eq { hop { argmin { filter_eq { all_rows ; competition ; european zone } ; year } ; score } ; 1 - 4 }
select the row whose position record of all rows is minimum . the pilot record of this row is mario kiessling .
Output: eq { hop { argmin { all_rows ; position } ; pilot } ; mario kiessling }
select the rows whose directed by record fuzzily matches to dean holland . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; directed by ; dean holland } } ; 3 }
select the rows whose region 2 record fuzzily matches to 2004 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; region 2 ; 2004 } } ; 3 }
select the rows whose call sign record fuzzily matches to w269ax . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to w230av . take the frequency mhz record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; call sign ; w269ax } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; w230av } ; frequency mhz } }
select the rows whose result record fuzzily matches to w . the number of such rows is 10 .
Output: eq { count { filter_eq { all_rows ; result ; w } } ; 10 }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is greater than 1999 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; money ; 1999 } } ; 3 }
for the aspect records of all rows , all of them fuzzily match to 4:3 .
Output: all_eq { all_rows ; aspect ; 4:3 }
select the rows whose no in series record is arbitrary . the number of such rows is 12 .
Output: eq { count { filter_all { all_rows ; no in series } } ; 12 }
select the rows whose us viewers ( million ) record is less than 1.0 . the number of such rows is 5 .
Output: eq { count { filter_less { all_rows ; us viewers ( million ) ; 1.0 } } ; 5 }
the sum of the inhabitants record of all rows is 521,203 .
Output: round_eq { sum { all_rows ; inhabitants } ; 521,203 }
select the rows whose 2011 record fuzzily matches to 2r . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; 2011 ; 2r } } ; 2 }
the sum of the number of cars record of all rows is 63 .
Output: round_eq { sum { all_rows ; number of cars } ; 63 }
select the rows whose chassis record fuzzily matches to lola . there is only one such row in the table . the year record of this unqiue row is 1993 .
Output: and { only { filter_eq { all_rows ; chassis ; lola } } ; eq { hop { filter_eq { all_rows ; chassis ; lola } ; year } ; 1993 } }
for the party records of all rows , most of them fuzzily match to democratic .
Output: most_eq { all_rows ; party ; democratic }
select the row whose enrollment record of all rows is maximum . the institution record of this row is southern university at new orleans .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; southern university at new orleans }
select the rows whose date record fuzzily matches to 19 november 1997 . take the score record of this row . select the rows whose date record fuzzily matches to 19 november 2003 . take the score record of this row . the first record is greater than the second record . the score record of the first row is 1 - 0 . the score record of the second row is 0 - 3 .
Output: and { greater { hop { filter_eq { all_rows ; date ; 19 november 1997 } ; score } ; hop { filter_eq { all_rows ; date ; 19 november 2003 } ; score } } ; and { eq { hop { filter_eq { all_rows ; date ; 19 november 1997 } ; score } ; 1 - 0 } ; eq { hop { filter_eq { all_rows ; date ; 19 november 2003 } ; score } ; 0 - 3 } } }
the average of the gold record of all rows is 2.86 .
Output: round_eq { avg { all_rows ; gold } ; 2.86 }
select the rows whose premier date record fuzzily matches to 2004 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; premier date ; 2004 } } ; 4 }
select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is tom barrett .
Output: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; tom barrett }
select the rows whose points record is equal to 17 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; points ; 17 } } ; 3 }
select the rows whose location record fuzzily matches to china . for the status records of these rows , all of them fuzzily match to valid .
Output: all_eq { filter_eq { all_rows ; location ; china } ; status ; valid }
select the rows whose location record fuzzily matches to usa . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; usa } } ; 2 }
for the clubs involved records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; clubs involved ; 10 }
select the row whose year erected record of all rows is 3rd minimum . the statue record of this row is benito juarez .
Output: eq { hop { nth_argmin { all_rows ; year erected ; 3 } ; statue } ; benito juarez }
the average of the height ( m ) record of all rows is 2.01 .
Output: round_eq { avg { all_rows ; height ( m ) } ; 2.01 }
select the row whose time record of all rows is 3rd minimum . the athlete record of this row is gabriella bascelli .
Output: eq { hop { nth_argmin { all_rows ; time ; 3 } ; athlete } ; gabriella bascelli }
select the rows whose home team record fuzzily matches to carlton . take the home team score record of this row . select the rows whose home team record fuzzily matches to footscray . take the home team score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; carlton } ; home team score } ; hop { filter_eq { all_rows ; home team ; footscray } ; home team score } }
select the rows whose result record fuzzily matches to final . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; result ; final } } ; 4 }
select the row whose overall record of all rows is maximum . the name record of this row is sean renfree .
Output: eq { hop { argmax { all_rows ; overall } ; name } ; sean renfree }
select the rows whose location record fuzzily matches to minsk . take the capacity record of this row . select the rows whose location record fuzzily matches to lida . take the capacity record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; location ; minsk } ; capacity } ; hop { filter_eq { all_rows ; location ; lida } ; capacity } }
select the rows whose a score record is greater than 7 . among these rows , select the rows whose b score record is less than 9 . the number of such rows is 1 .
Output: eq { count { filter_less { filter_greater { all_rows ; a score ; 7 } ; b score ; 9 } } ; 1 }
for the engine records of all rows , most of them fuzzily match to bmw straight - 4 .
Output: most_eq { all_rows ; engine ; bmw straight - 4 }
select the rows whose position record fuzzily matches to wide receiver . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; wide receiver } } ; 2 }
for the nba draft records of all rows , most of them fuzzily match to 1st round .
Output: most_eq { all_rows ; nba draft ; 1st round }
for the away team score records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; away team score ; 10 }
select the rows whose ties record is equal to 3 . there is only one such row in the table . the season record of this unqiue row is 2008 .
Output: and { only { filter_eq { all_rows ; ties ; 3 } } ; eq { hop { filter_eq { all_rows ; ties ; 3 } ; season } ; 2008 } }
select the rows whose laps record is equal to 39 . there is only one such row in the table . the year record of this unqiue row is 1949 .
Output: and { only { filter_eq { all_rows ; laps ; 39 } } ; eq { hop { filter_eq { all_rows ; laps ; 39 } ; year } ; 1949 } }
select the row whose attendance record of all rows is maximum . the opponent record of this row is buffalo bills .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; buffalo bills }
select the rows whose college record fuzzily matches to north dakota . there is only one such row in the table . the player record of this unqiue row is tim prinsen .
Output: and { only { filter_eq { all_rows ; college ; north dakota } } ; eq { hop { filter_eq { all_rows ; college ; north dakota } ; player } ; tim prinsen } }
select the row whose points record of all rows is 2nd maximum . the song record of this row is are you sure .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; song } ; are you sure }
select the row whose first - team goals record of all rows is maximum . the player record of this row is gareth barry .
Output: eq { hop { argmax { all_rows ; first - team goals } ; player } ; gareth barry }
for the method records of all rows , most of them fuzzily match to decision .
Output: most_eq { all_rows ; method ; decision }
select the row whose attendance record of all rows is minimum . the date record of this row is 17 september 1997 .
Output: eq { hop { argmin { all_rows ; attendance } ; date } ; 17 september 1997 }
the sum of the scored record of all rows is 28 .
Output: round_eq { sum { all_rows ; scored } ; 28 }
select the rows whose candidates record fuzzily matches to % . there is only one such row in the table . the district record of this unqiue row is arkansas 5 .
Output: and { only { filter_eq { all_rows ; candidates ; % } } ; eq { hop { filter_eq { all_rows ; candidates ; % } ; district } ; arkansas 5 } }