niryuu/sni-each-converted huggingface dataset:
Command: eq { min { filter_eq { all_rows ; game site ; forbes field } ; date } ; sunday september 20 }, interpretation: select the rows whose team classification record fuzzily matches to relax - gam . for the sprints classification records of these rows , most of them fuzzily match to víctor hugo peña .
Output: no
Command: most_eq { filter_eq { all_rows ; team classification ; relax - gam } ; sprints classification ; víctor hugo peña }, interpretation: select the rows whose team classification record fuzzily matches to relax - gam . for the sprints classification records of these rows , most of them fuzzily match to víctor hugo peña .
Output: yes
Command: most_eq { filter_eq { all_rows ; team classification ; relax - gam } ; sprints classification ; víctor hugo peña }, interpretation: select the rows whose event record fuzzily matches to men 's . the average of the swim ( 1.5 km ) record of these rows is 18:41 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; event ; men 's } ; swim ( 1.5 km ) } ; 18:41 }, interpretation: select the rows whose event record fuzzily matches to men 's . the average of the swim ( 1.5 km ) record of these rows is 18:41 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; event ; men 's } ; swim ( 1.5 km ) } ; 18:41 }, interpretation: select the row whose population record of all rows is maximum . the borough record of this row is fleurimont .
Output: no
Command: eq { hop { argmax { all_rows ; population } ; borough } ; fleurimont }, interpretation: select the row whose population record of all rows is maximum . the borough record of this row is fleurimont .
Output: yes
Command: eq { hop { argmax { all_rows ; population } ; borough } ; fleurimont }, interpretation: for the regional county municipality records of all rows , all of them fuzzily match to not part of a rcm .
Output: no
Command: all_eq { all_rows ; regional county municipality ; not part of a rcm }, interpretation: for the regional county municipality records of all rows , all of them fuzzily match to not part of a rcm .
Output: yes
Command: all_eq { all_rows ; regional county municipality ; not part of a rcm }, interpretation: for the played records of all rows , most of them are equal to 9 .
Output: no
Command: most_eq { all_rows ; played ; 9 }, interpretation: for the played records of all rows , most of them are equal to 9 .
Output: yes
Command: most_eq { all_rows ; played ; 9 }, interpretation: for the date records of all rows , all of them fuzzily match to march .
Output: no
Command: all_eq { all_rows ; date ; march }, interpretation: for the cores records of all rows , all of them are equal to 4 .
Output: no
Command: all_eq { all_rows ; cores ; 4 }, interpretation: for the cores records of all rows , all of them are equal to 4 .
Output: yes
Command: all_eq { all_rows ; cores ; 4 }, interpretation: select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello . there is only one such row in the table . the date record of this unqiue row is 27 april .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } } ; eq { hop { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } ; date } ; 27 april } }, interpretation: select the rows whose race winner record fuzzily matches to andrew pitt . among these rows , select the rows whose fastest lap record fuzzily matches to gianluca vizziello . there is only one such row in the table . the date record of this unqiue row is 27 april .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } } ; eq { hop { filter_eq { filter_eq { all_rows ; race winner ; andrew pitt } ; fastest lap ; gianluca vizziello } ; date } ; 27 april } }, interpretation: select the rows whose date record fuzzily matches to 2004 . among these rows , select the rows whose surface record fuzzily matches to hard . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; date ; 2004 } ; surface ; hard } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to 2004 . among these rows , select the rows whose surface record fuzzily matches to hard . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; date ; 2004 } ; surface ; hard } } ; 2 }, interpretation: select the rows whose 1st place team record fuzzily matches to mary star all - stars . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; 1st place team ; mary star all - stars } } ; 5 }, interpretation: select the rows whose 1st place team record fuzzily matches to mary star all - stars . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 1st place team ; mary star all - stars } } ; 5 }, interpretation: select the rows whose manner of departure record fuzzily matches to resigned . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; manner of departure ; resigned } } ; 6 }, interpretation: select the rows whose manner of departure record fuzzily matches to resigned . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; manner of departure ; resigned } } ; 6 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is 19 may 2007 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; 19 may 2007 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is 19 may 2007 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; 19 may 2007 }, interpretation: 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: no
Command: and { eq { max { all_rows ; score } ; l 83 - 74 } ; eq { hop { argmax { all_rows ; score } ; date } ; february 5 } }, interpretation: 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: yes
Command: and { eq { max { all_rows ; score } ; l 83 - 74 } ; eq { hop { argmax { all_rows ; score } ; date } ; february 5 } }, interpretation: select the rows whose area record fuzzily matches to newmarket . there is only one such row in the table . the name record of this unqiue row is newmarket school .
Output: no
Command: and { only { filter_eq { all_rows ; area ; newmarket } } ; eq { hop { filter_eq { all_rows ; area ; newmarket } ; name } ; newmarket school } }, interpretation: select the rows whose area record fuzzily matches to newmarket . there is only one such row in the table . the name record of this unqiue row is newmarket school .
Output: yes
Command: and { only { filter_eq { all_rows ; area ; newmarket } } ; eq { hop { filter_eq { all_rows ; area ; newmarket } ; name } ; newmarket school } }, interpretation: select the row whose height ( m ) record of all rows is 2nd maximum . the island record of this row is ailsa craig .
Output: no
Command: eq { hop { nth_argmax { all_rows ; height ( m ) ; 2 } ; island } ; ailsa craig }, interpretation: select the row whose height ( m ) record of all rows is 2nd maximum . the island record of this row is ailsa craig .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; height ( m ) ; 2 } ; island } ; ailsa craig }, interpretation: select the rows whose directed by record fuzzily matches to peter deluise . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; directed by ; peter deluise } }, interpretation: select the rows whose directed by record fuzzily matches to peter deluise . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; directed by ; peter deluise } }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than 153 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; total ; 153 } } ; 2 }, interpretation: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than 153 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; total ; 153 } } ; 2 }, interpretation: select the row whose attendance / g record of all rows is maximum . the season record of this row is 2004 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance / g } ; season } ; 2004 }, interpretation: select the row whose attendance / g record of all rows is maximum . the season record of this row is 2004 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance / g } ; season } ; 2004 }, interpretation: select the row whose gold record of all rows is 2nd maximum . the nation record of this row is romania .
Output: no
Command: eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; romania }, interpretation: select the row whose gold record of all rows is 2nd maximum . the nation record of this row is romania .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; romania }, interpretation: for the pilot records of all rows , all of them fuzzily match to donald campbell .
Output: no
Command: all_eq { all_rows ; pilot ; donald campbell }, interpretation: for the pilot records of all rows , all of them fuzzily match to donald campbell .
Output: yes
Command: all_eq { all_rows ; pilot ; donald campbell }, interpretation: select the rows whose pole position record fuzzily matches to alain prost . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; pole position ; alain prost } } ; 2 }, interpretation: select the rows whose pole position record fuzzily matches to alain prost . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; pole position ; alain prost } } ; 2 }, interpretation: select the row whose population ( 2007 ) record of all rows is 2nd maximum . the municipality record of this row is labo .
Output: no
Command: eq { hop { nth_argmax { all_rows ; population ( 2007 ) ; 2 } ; municipality } ; labo }, interpretation: select the row whose population ( 2007 ) record of all rows is 2nd maximum . the municipality record of this row is labo .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; population ( 2007 ) ; 2 } ; municipality } ; labo }, interpretation: select the row whose south asians 2011 record of all rows is maximum . the province record of this row is ontario .
Output: no
Command: eq { hop { argmax { all_rows ; south asians 2011 } ; province } ; ontario }, interpretation: select the row whose south asians 2011 record of all rows is maximum . the province record of this row is ontario .
Output: yes
Command: eq { hop { argmax { all_rows ; south asians 2011 } ; province } ; ontario }, interpretation: select the rows whose time record is greater than 7:00:00 . there is only one such row in the table . the country record of this unqiue row is iraq .
Output: no
Command: and { only { filter_greater { all_rows ; time ; 7:00:00 } } ; eq { hop { filter_greater { all_rows ; time ; 7:00:00 } ; country } ; iraq } }, interpretation: select the rows whose time record is greater than 7:00:00 . there is only one such row in the table . the country record of this unqiue row is iraq .
Output: yes
Command: and { only { filter_greater { all_rows ; time ; 7:00:00 } } ; eq { hop { filter_greater { all_rows ; time ; 7:00:00 } ; country } ; iraq } }, interpretation: the average of the blank ends record of all rows is 6.9 .
Output: no
Command: round_eq { avg { all_rows ; blank ends } ; 6.9 }, interpretation: the average of the blank ends record of all rows is 6.9 .
Output: yes
Command: round_eq { avg { all_rows ; blank ends } ; 6.9 }, interpretation: select the rows whose runs record is equal to 203 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; runs ; 203 } } ; 2 }, interpretation: select the rows whose runs record is equal to 203 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; runs ; 203 } } ; 2 }, interpretation: 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: no
Command: 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 } }, interpretation: 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: yes
Command: 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 } }, interpretation: select the row whose date record of all rows is 2nd maximum . the venue record of this row is vasil levski national stadium , sofia .
Output: no
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; venue } ; vasil levski national stadium , sofia }, interpretation: select the row whose date record of all rows is 2nd maximum . the venue record of this row is vasil levski national stadium , sofia .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; venue } ; vasil levski national stadium , sofia }, interpretation: select the rows whose gold record is equal to 3 . there is only one such row in the table . the nation record of this unqiue row is china ( chn ) .
Output: no
Command: and { only { filter_eq { all_rows ; gold ; 3 } } ; eq { hop { filter_eq { all_rows ; gold ; 3 } ; nation } ; china ( chn ) } }, interpretation: select the rows whose gold record is equal to 3 . there is only one such row in the table . the nation record of this unqiue row is china ( chn ) .
Output: yes
Command: and { only { filter_eq { all_rows ; gold ; 3 } } ; eq { hop { filter_eq { all_rows ; gold ; 3 } ; nation } ; china ( chn ) } }, interpretation: select the rows whose opponent record fuzzily matches to toronto maple leafs . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; opponent ; toronto maple leafs } } ; 3 }, interpretation: select the rows whose opponent record fuzzily matches to toronto maple leafs . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; opponent ; toronto maple leafs } } ; 3 }, interpretation: select the row whose points record of all rows is maximum . the opponent record of this row is ottawa senators .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; opponent } ; ottawa senators }, interpretation: select the row whose points record of all rows is maximum . the opponent record of this row is ottawa senators .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; opponent } ; ottawa senators }, interpretation: the average of the population urban area in millions record of all rows is 5.1 .
Output: no
Command: round_eq { avg { all_rows ; population urban area in millions } ; 5.1 }, interpretation: the average of the population urban area in millions record of all rows is 5.1 .
Output: yes
Command: round_eq { avg { all_rows ; population urban area in millions } ; 5.1 }, interpretation: select the rows whose round record fuzzily matches to 1 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; round ; 1 } } ; 4 }, interpretation: select the rows whose round record fuzzily matches to 1 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; round ; 1 } } ; 4 }, interpretation: select the rows whose length record is greater than 4:00 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; length ; 4:00 } } ; 3 }, interpretation: select the rows whose length record is greater than 4:00 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; length ; 4:00 } } ; 3 }, interpretation: select the rows whose result record fuzzily matches to w . the number of such rows is 10 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; w } } ; 10 }, interpretation: select the rows whose result record fuzzily matches to w . the number of such rows is 10 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; w } } ; 10 }, interpretation: select the rows whose traction type record fuzzily matches to petrol . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; traction type ; petrol } } ; 2 }, interpretation: select the rows whose traction type record fuzzily matches to petrol . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; traction type ; petrol } } ; 2 }, interpretation: select the row whose laps record of all rows is 2nd minimum . the driver record of this row is dan gurney .
Output: no
Command: eq { hop { nth_argmin { all_rows ; laps ; 2 } ; driver } ; dan gurney }, interpretation: select the row whose laps record of all rows is 2nd minimum . the driver record of this row is dan gurney .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; laps ; 2 } ; driver } ; dan gurney }, interpretation: select the row whose start record of all rows is minimum . the year record of this row is 1936 .
Output: no
Command: eq { hop { argmin { all_rows ; start } ; year } ; 1936 }, interpretation: select the row whose start record of all rows is minimum . the year record of this row is 1936 .
Output: yes
Command: eq { hop { argmin { all_rows ; start } ; year } ; 1936 }, interpretation: select the rows whose bronze record is equal to 2 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; bronze ; 2 } } ; 5 }, interpretation: select the rows whose bronze record is equal to 2 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; bronze ; 2 } } ; 5 }, interpretation: select the rows whose year record is greater than 2000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { all_rows ; year ; 2000 } } ; 3 }, interpretation: select the rows whose year record is greater than 2000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { all_rows ; year ; 2000 } } ; 3 }, interpretation: select the row whose agg record of all rows is maximum . the team 1 record of this row is canon yaoundé .
Output: no
Command: eq { hop { argmax { all_rows ; agg } ; team 1 } ; canon yaoundé }, interpretation: select the row whose agg record of all rows is maximum . the team 1 record of this row is canon yaoundé .
Output: yes
Command: eq { hop { argmax { all_rows ; agg } ; team 1 } ; canon yaoundé }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 68.67 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 68.67 }, interpretation: select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 68.67 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 68.67 }, interpretation: select the rows whose draws record is greater than 5 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_greater { all_rows ; draws ; 5 } } ; 4 }, interpretation: select the rows whose draws record is greater than 5 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_greater { all_rows ; draws ; 5 } } ; 4 }, interpretation: for the date records of all rows , all of them fuzzily match to 1 may 1926 .
Output: no
Command: all_eq { all_rows ; date ; 1 may 1926 }, interpretation: for the date records of all rows , all of them fuzzily match to 1 may 1926 .
Output: yes
Command: all_eq { all_rows ; date ; 1 may 1926 }, interpretation: select the rows whose year record is greater than 2010 . there is only one such row in the table .
Output: no
Command: only { filter_greater { all_rows ; year ; 2010 } }, interpretation: select the rows whose year record is greater than 2010 . there is only one such row in the table .
Output: yes
Command: only { filter_greater { all_rows ; year ; 2010 } }, interpretation: the average of the size record of all rows is 405.1 .
Output: no
Command: round_eq { avg { all_rows ; size } ; 405.1 }, interpretation: the average of the size record of all rows is 405.1 .
Output: yes
Command: round_eq { avg { all_rows ; size } ; 405.1 }, interpretation: select the rows whose title record fuzzily matches to road to the north pole . take the original air date record of this row . select the rows whose title record fuzzily matches to road to europe . take the original air date record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; title ; road to the north pole } ; original air date } ; hop { filter_eq { all_rows ; title ; road to europe } ; original air date } }, interpretation: select the rows whose title record fuzzily matches to road to the north pole . take the original air date record of this row . select the rows whose title record fuzzily matches to road to europe . take the original air date record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; title ; road to the north pole } ; original air date } ; hop { filter_eq { all_rows ; title ; road to europe } ; original air date } }, interpretation: the 1st minimum place record of all rows is 1 . the artist record of the row with 1st minimum place record is vnia fernandes .
Output: no
Command: and { eq { nth_min { all_rows ; place ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; place ; 1 } ; artist } ; vnia fernandes } }, interpretation: the 1st minimum place record of all rows is 1 . the artist record of the row with 1st minimum place record is vnia fernandes .
Output: yes
Command: and { eq { nth_min { all_rows ; place ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; place ; 1 } ; artist } ; vnia fernandes } }, interpretation: for the type records of all rows , most of them fuzzily match to lake .
Output: no
Command: most_eq { all_rows ; type ; lake }, interpretation: for the type records of all rows , most of them fuzzily match to lake .
Output: yes
Command: most_eq { all_rows ; type ; lake }, interpretation: select the rows whose english name record fuzzily matches to hexi district . take the density record of this row . select the rows whose english name record fuzzily matches to fenghuang . take the density record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; english name ; hexi district } ; density } ; hop { filter_eq { all_rows ; english name ; fenghuang } ; density } }, interpretation: select the rows whose english name record fuzzily matches to hexi district . take the density record of this row . select the rows whose english name record fuzzily matches to fenghuang . take the density record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; english name ; hexi district } ; density } ; hop { filter_eq { all_rows ; english name ; fenghuang } ; density } }, interpretation: 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: no
Command: greater { hop { filter_eq { all_rows ; call sign ; w269ax } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; w230av } ; frequency mhz } }, interpretation: 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: yes
Command: greater { hop { filter_eq { all_rows ; call sign ; w269ax } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; w230av } ; frequency mhz } }, interpretation: the average of the attendance record of all rows is 16261 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 16261 }, interpretation: the average of the attendance record of all rows is 16261 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 16261 }, interpretation: the average of the rest mass mev / c 2 record of all rows is 1352 .
Output: no
Command: round_eq { avg { all_rows ; rest mass mev / c 2 } ; 1352 }, interpretation: the average of the rest mass mev / c 2 record of all rows is 1352 .
Output: yes
Command: round_eq { avg { all_rows ; rest mass mev / c 2 } ; 1352 }, interpretation: select the row whose no record of all rows is 4th maximum . the player record of this row is corliss williamson .
Output: no
Command: eq { hop { nth_argmax { all_rows ; no ; 4 } ; player } ; corliss williamson }, interpretation: select the row whose no record of all rows is 4th maximum . the player record of this row is corliss williamson .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; no ; 4 } ; player } ; corliss williamson }, interpretation: for the mlb draft records of all rows , most of them fuzzily match to 1995 .
Output: no
Command: most_eq { all_rows ; mlb draft ; 1995 }, interpretation: for the mlb draft records of all rows , most of them fuzzily match to 1995 .
Output: yes
Command: most_eq { all_rows ; mlb draft ; 1995 }, interpretation: for the completion records of all rows , most of them are greater than or equal to 2000 .
Output: no
Command: most_greater_eq { all_rows ; completion ; 2000 }, interpretation: for the completion records of all rows , most of them are greater than or equal to 2000 .
Output: yes
Command: most_greater_eq { all_rows ; completion ; 2000 }, interpretation: select the rows whose nationality record fuzzily matches to usa . among these rows , select the rows whose position record fuzzily matches to rw . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; nationality ; usa } ; position ; rw } } ; 2 }, interpretation: select the rows whose nationality record fuzzily matches to usa . among these rows , select the rows whose position record fuzzily matches to rw . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; nationality ; usa } ; position ; rw } } ; 2 }, interpretation: select the rows whose format record fuzzily matches to limited edition . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; format ; limited edition } } ; 5 }, interpretation: select the rows whose format record fuzzily matches to limited edition . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; format ; limited edition } } ; 5 }, interpretation: for the result records of all rows , most of them fuzzily match to nominated .
Output: no
Command: most_eq { all_rows ; result ; nominated }, interpretation: for the result records of all rows , most of them fuzzily match to nominated .
Output: yes
Command: most_eq { all_rows ; result ; nominated }, interpretation: the average of the attendance record of all rows is 50799 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 50799 }, interpretation: the average of the attendance record of all rows is 50799 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 50799 }, interpretation: select the rows whose date record is greater than or equal to december 3 , 1989 . among these rows , select the rows whose attendance record is greater than 10000 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_greater { filter_greater_eq { all_rows ; date ; december 3 , 1989 } ; attendance ; 10000 } } ; 3 }, interpretation: select the rows whose date record is greater than or equal to december 3 , 1989 . among these rows , select the rows whose attendance record is greater than 10000 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_greater { filter_greater_eq { all_rows ; date ; december 3 , 1989 } ; attendance ; 10000 } } ; 3 }, interpretation: for the shot pct records of all rows , most of them are greater than 65 % .
Output: no
Command: most_greater { all_rows ; shot pct ; 65 % }, interpretation: for the shot pct records of all rows , most of them are greater than 65 % .
Output: yes
Command: most_greater { all_rows ; shot pct ; 65 % }, interpretation: the maximum result record of all rows is w 45 - 7 .
Output: no
Command: eq { max { all_rows ; result } ; w 45 - 7 }, interpretation: the maximum result record of all rows is w 45 - 7 .
Output: yes
Command: eq { max { all_rows ; result } ; w 45 - 7 }, interpretation: select the rows whose player record fuzzily matches to adam voges . take the runs record of this row . select the rows whose player record fuzzily matches to callum ferguson . take the runs record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; adam voges } ; runs } ; hop { filter_eq { all_rows ; player ; callum ferguson } ; runs } }, interpretation: select the rows whose player record fuzzily matches to adam voges . take the runs record of this row . select the rows whose player record fuzzily matches to callum ferguson . take the runs record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; adam voges } ; runs } ; hop { filter_eq { all_rows ; player ; callum ferguson } ; runs } }, interpretation: the average of the occurrences record of all rows is 10 .
Output: no
Command: round_eq { avg { all_rows ; occurrences } ; 10 }, interpretation: the average of the occurrences record of all rows is 10 .
Output: yes
Command: round_eq { avg { all_rows ; occurrences } ; 10 }, interpretation: for the website records of all rows , most of them fuzzily match to english .
Output: no
Command: most_eq { all_rows ; website ; english }, interpretation: for the website records of all rows , most of them fuzzily match to english .
Output: yes
Command: most_eq { all_rows ; website ; english }, interpretation: select the rows whose nationality record fuzzily matches to canada . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; nationality ; canada } } ; 2 }, interpretation: select the rows whose nationality record fuzzily matches to canada . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; nationality ; canada } } ; 2 }, interpretation: select the rows whose total record is equal to 1 . the number of such rows is 7 .
Output: no
Command: eq { count { filter_eq { all_rows ; total ; 1 } } ; 7 }, interpretation: select the rows whose total record is equal to 1 . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_eq { all_rows ; total ; 1 } } ; 7 }, interpretation: select the row whose record record of all rows is 2nd maximum . the year record of this row is 1979 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; record ; 2 } ; year } ; 1979 }, interpretation: select the row whose record record of all rows is 2nd maximum . the year record of this row is 1979 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; record ; 2 } ; year } ; 1979 }, interpretation: select the rows whose event record fuzzily matches to wc beijing . the sum of the rank points record of these rows is 23 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; event ; wc beijing } ; rank points } ; 23 }, interpretation: select the rows whose event record fuzzily matches to wc beijing . the sum of the rank points record of these rows is 23 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; event ; wc beijing } ; rank points } ; 23 }, interpretation: select the rows whose date record fuzzily matches to 14 jun 2005 . take the margin of victory record of this row . select the rows whose date record fuzzily matches to 12 jul 2006 . take the margin of victory record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; date ; 14 jun 2005 } ; margin of victory } ; hop { filter_eq { all_rows ; date ; 12 jul 2006 } ; margin of victory } }, interpretation: select the rows whose date record fuzzily matches to 14 jun 2005 . take the margin of victory record of this row . select the rows whose date record fuzzily matches to 12 jul 2006 . take the margin of victory record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; date ; 14 jun 2005 } ; margin of victory } ; hop { filter_eq { all_rows ; date ; 12 jul 2006 } ; margin of victory } }, interpretation: select the rows whose year record is less than 1967 . among these rows , select the rows whose result record fuzzily matches to 2nd . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_less { all_rows ; year ; 1967 } ; result ; 2nd } } ; 2 }, interpretation: select the rows whose year record is less than 1967 . among these rows , select the rows whose result record fuzzily matches to 2nd . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_less { all_rows ; year ; 1967 } ; result ; 2nd } } ; 2 }, interpretation: for the surface records of all rows , most of them fuzzily match to grass .
Output: no
Command: most_eq { all_rows ; surface ; grass }, interpretation: for the surface records of all rows , most of them fuzzily match to grass .
Output: yes
Command: most_eq { all_rows ; surface ; grass }, interpretation: select the rows whose us viewers ( in millions ) record is equal to 3.69 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; us viewers ( in millions ) ; 3.69 } } ; 2 }, interpretation: select the rows whose us viewers ( in millions ) record is equal to 3.69 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; us viewers ( in millions ) ; 3.69 } } ; 2 }, interpretation: select the rows whose date record fuzzily matches to 6 march 1976 . select the row whose tie no record of these rows is minimum . the home team record of this row is sunderland . the away team record of this row is crystal palace .
Output: no
Command: and { eq { hop { argmin { filter_eq { all_rows ; date ; 6 march 1976 } ; tie no } ; home team } ; sunderland } ; eq { hop { argmin { filter_eq { all_rows ; date ; 6 march 1976 } ; tie no } ; away team } ; crystal palace } }, interpretation: select the rows whose date record fuzzily matches to 6 march 1976 . select the row whose tie no record of these rows is minimum . the home team record of this row is sunderland . the away team record of this row is crystal palace .
Output: yes
Command: and { eq { hop { argmin { filter_eq { all_rows ; date ; 6 march 1976 } ; tie no } ; home team } ; sunderland } ; eq { hop { argmin { filter_eq { all_rows ; date ; 6 march 1976 } ; tie no } ; away team } ; crystal palace } }, interpretation: select the row whose years for rockets record of all rows is minimum . the player record of this row is perry , curtis curtis perry .
Output: no
Command: eq { hop { argmin { all_rows ; years for rockets } ; player } ; perry , curtis curtis perry }, interpretation: select the row whose years for rockets record of all rows is minimum . the player record of this row is perry , curtis curtis perry .
Output: yes
Command: eq { hop { argmin { all_rows ; years for rockets } ; player } ; perry , curtis curtis perry }, interpretation: select the rows whose goals record is equal to 64 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; goals ; 64 } } ; 3 }, interpretation: select the rows whose goals record is equal to 64 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; goals ; 64 } } ; 3 }, interpretation: select the rows whose venue record fuzzily matches to victoria park . there is only one such row in the table . the home team record of this unqiue row is collingwood . the away team record of this unqiue row is north melbourne .
Output: no
Command: and { only { filter_eq { all_rows ; venue ; victoria park } } ; and { eq { hop { filter_eq { all_rows ; venue ; victoria park } ; home team } ; collingwood } ; eq { hop { filter_eq { all_rows ; venue ; victoria park } ; away team } ; north melbourne } } }, interpretation: select the rows whose venue record fuzzily matches to victoria park . there is only one such row in the table . the home team record of this unqiue row is collingwood . the away team record of this unqiue row is north melbourne .
Output: yes
Command: and { only { filter_eq { all_rows ; venue ; victoria park } } ; and { eq { hop { filter_eq { all_rows ; venue ; victoria park } ; home team } ; collingwood } ; eq { hop { filter_eq { all_rows ; venue ; victoria park } ; away team } ; north melbourne } } }, interpretation: select the row whose time record of all rows is 1st minimum . the opponent record of this row is mark sinclair .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; opponent } ; mark sinclair }, interpretation: select the row whose time record of all rows is 1st minimum . the opponent record of this row is mark sinclair .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; opponent } ; mark sinclair }, interpretation: select the row whose award record of all rows is 2nd maximum . the film record of this row is mercy .
Output: no
Command: eq { hop { nth_argmax { all_rows ; award ; 2 } ; film } ; mercy }, interpretation: select the row whose award record of all rows is 2nd maximum . the film record of this row is mercy .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; award ; 2 } ; film } ; mercy }, interpretation: select the row whose overall record record of all rows is maximum . the texas vs record of this row is texas a & m .
Output: no
Command: eq { hop { argmax { all_rows ; overall record } ; texas vs } ; texas a & m }, interpretation: select the row whose overall record record of all rows is maximum . the texas vs record of this row is texas a & m .
Output: yes
Command: eq { hop { argmax { all_rows ; overall record } ; texas vs } ; texas a & m }, interpretation: select the rows whose goal difference record is less than 0 . the number of such rows is 8 .
Output: no
Command: eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 8 }, interpretation: select the rows whose goal difference record is less than 0 . the number of such rows is 8 .
Output: yes
Command: eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 8 }, interpretation: select the rows whose entrant record fuzzily matches to north american racing team . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; entrant ; north american racing team } } ; 3 }, interpretation: select the rows whose entrant record fuzzily matches to north american racing team . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; entrant ; north american racing team } } ; 3 }, interpretation: select the rows whose film title used in nomination record fuzzily matches to the other bank . take the year ( ceremony ) record of this row . select the rows whose film title used in nomination record fuzzily matches to keep smiling . take the year ( ceremony ) record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; film title used in nomination ; the other bank } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; film title used in nomination ; keep smiling } ; year ( ceremony ) } }, interpretation: select the rows whose film title used in nomination record fuzzily matches to the other bank . take the year ( ceremony ) record of this row . select the rows whose film title used in nomination record fuzzily matches to keep smiling . take the year ( ceremony ) record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; film title used in nomination ; the other bank } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; film title used in nomination ; keep smiling } ; year ( ceremony ) } }, interpretation: select the rows whose margin record fuzzily matches to 3 strokes . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; margin ; 3 strokes } } ; 2 }, interpretation: select the rows whose margin record fuzzily matches to 3 strokes . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; margin ; 3 strokes } } ; 2 }, interpretation: select the rows whose losses record is equal to 8 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; losses ; 8 } } ; 2 }, interpretation: select the rows whose losses record is equal to 8 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; losses ; 8 } } ; 2 }, interpretation: select the row whose crowd record of all rows is 3rd maximum . the home team score record of this row is 13.7 ( 85 ) . the away team record of this row is st kilda .
Output: no
Command: and { eq { hop { nth_argmax { all_rows ; crowd ; 3 } ; home team score } ; 13.7 ( 85 ) } ; eq { hop { nth_argmax { all_rows ; crowd ; 3 } ; away team } ; st kilda } }, interpretation: select the row whose crowd record of all rows is 3rd maximum . the home team score record of this row is 13.7 ( 85 ) . the away team record of this row is st kilda .
Output: yes
Command: and { eq { hop { nth_argmax { all_rows ; crowd ; 3 } ; home team score } ; 13.7 ( 85 ) } ; eq { hop { nth_argmax { all_rows ; crowd ; 3 } ; away team } ; st kilda } }, interpretation: select the rows whose local government area record fuzzily matches to anangu pitjantjatjara yankunytjatjara . take the pop 2006 record of this row . select the rows whose local government area record fuzzily matches to outback areas community development trust . take the pop 2006 record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; local government area ; anangu pitjantjatjara yankunytjatjara } ; pop 2006 } ; hop { filter_eq { all_rows ; local government area ; outback areas community development trust } ; pop 2006 } }, interpretation: select the rows whose local government area record fuzzily matches to anangu pitjantjatjara yankunytjatjara . take the pop 2006 record of this row . select the rows whose local government area record fuzzily matches to outback areas community development trust . take the pop 2006 record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; local government area ; anangu pitjantjatjara yankunytjatjara } ; pop 2006 } ; hop { filter_eq { all_rows ; local government area ; outback areas community development trust } ; pop 2006 } }, interpretation: select the rows whose release date record fuzzily matches to january 2011 . the average of the release price ( usd ) record of these rows is 507.71 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; release date ; january 2011 } ; release price ( usd ) } ; 507.71 }, interpretation: select the rows whose release date record fuzzily matches to january 2011 . the average of the release price ( usd ) record of these rows is 507.71 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; release date ; january 2011 } ; release price ( usd ) } ; 507.71 }, interpretation: select the rows whose gold record is greater than 100 . select the row whose silver record of these rows is maximum . the country record of this row is indonesia .
Output: no
Command: eq { hop { argmax { filter_greater { all_rows ; gold ; 100 } ; silver } ; country } ; indonesia }, interpretation: select the rows whose gold record is greater than 100 . select the row whose silver record of these rows is maximum . the country record of this row is indonesia .
Output: yes
Command: eq { hop { argmax { filter_greater { all_rows ; gold ; 100 } ; silver } ; country } ; indonesia }, interpretation: for the primary sponsor ( s ) records of all rows , most of them do not match to n / a .
Output: no
Command: most_not_eq { all_rows ; primary sponsor ( s ) ; n / a }, interpretation: for the primary sponsor ( s ) records of all rows , most of them do not match to n / a .
Output: yes
Command: most_not_eq { all_rows ; primary sponsor ( s ) ; n / a }, interpretation: the sum of the w record of all rows is 21 .
Output: no
Command: round_eq { sum { all_rows ; w } ; 21 }, interpretation: the sum of the w record of all rows is 21 .
Output: yes
Command: round_eq { sum { all_rows ; w } ; 21 }, interpretation: select the rows whose driver / passenger record fuzzily matches to jan hendrickx / tim smeuninx . take the points record of this row . select the rows whose driver / passenger record fuzzily matches to marko happich / meinrad schelbert . take the points record of this row . the first record is greater than the second record . the points record of the first row is 405 . the points record of the second row is 317 .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; driver / passenger ; jan hendrickx / tim smeuninx } ; points } ; hop { filter_eq { all_rows ; driver / passenger ; marko happich / meinrad schelbert } ; points } } ; and { eq { hop { filter_eq { all_rows ; driver / passenger ; jan hendrickx / tim smeuninx } ; points } ; 405 } ; eq { hop { filter_eq { all_rows ; driver / passenger ; marko happich / meinrad schelbert } ; points } ; 317 } } }, interpretation: select the rows whose driver / passenger record fuzzily matches to jan hendrickx / tim smeuninx . take the points record of this row . select the rows whose driver / passenger record fuzzily matches to marko happich / meinrad schelbert . take the points record of this row . the first record is greater than the second record . the points record of the first row is 405 . the points record of the second row is 317 .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; driver / passenger ; jan hendrickx / tim smeuninx } ; points } ; hop { filter_eq { all_rows ; driver / passenger ; marko happich / meinrad schelbert } ; points } } ; and { eq { hop { filter_eq { all_rows ; driver / passenger ; jan hendrickx / tim smeuninx } ; points } ; 405 } ; eq { hop { filter_eq { all_rows ; driver / passenger ; marko happich / meinrad schelbert } ; points } ; 317 } } }, interpretation: select the rows whose country record fuzzily matches to zimbabwe . there is only one such row in the table . the player record of this unqiue row is nick price .
Output: no
Command: and { only { filter_eq { all_rows ; country ; zimbabwe } } ; eq { hop { filter_eq { all_rows ; country ; zimbabwe } ; player } ; nick price } }, interpretation: select the rows whose country record fuzzily matches to zimbabwe . there is only one such row in the table . the player record of this unqiue row is nick price .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; zimbabwe } } ; eq { hop { filter_eq { all_rows ; country ; zimbabwe } ; player } ; nick price } }, interpretation: the average of the total record of all rows is 9.29 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 9.29 }, interpretation: the average of the total record of all rows is 9.29 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 9.29 }, interpretation: select the rows whose result record is arbitrary . the number of such rows is 5 .
Output: no
Command: eq { count { filter_all { all_rows ; result } } ; 5 }, interpretation: select the rows whose result record is arbitrary . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_all { all_rows ; result } } ; 5 }, interpretation: the average of the pick record of all rows is 16.6 .
Output: no