sail/symbolic-instruction-tuning huggingface dataset:
[Table] header: Team | Outgoing manager | Manner of departure | Date of vacancy | Replaced by | Date of appointment row 1 : Rapid Bucureşti | Marian Rada | Contract not renewed | 3 Cannot handle non-empty timest | José Peseiro | 3 Cannot handle non-empty timest
row 2 : Otopeni | Gabriel Mărgărit | Sacked | 8 Cannot handle non-empty timest | Marian Bucurescu | 8 Cannot handle non-empty timest
row 3 : Rapid Bucureşti | José Peseiro | Sacked | 2 Cannot handle non-empty timest | Nicolae Manea | 3 Cannot handle non-empty timest
row 4 : Gloria Buzău | Álvaro Magalhãe | Sacked | 3 Cannot handle non-empty timest | Mario Marinică | 9 Cannot handle non-empty timest
row 5 : Rapid Bucureşti | Nicolae Manea | Caretaker | 9 Cannot handle non-empty timest | José Peseiro | 9 Cannot handle non-empty timest
row 6 : Vaslui | Viorel Hizo | Sacked | 1 Cannot handle non-empty timest | Viorel Moldovan | 1 Cannot handle non-empty timest
row 7 : CFR Cluj | Maurizio Trombetta | Sacked | 6 Cannot handle non-empty timest | Ales Jindra and Dušan Uhrin | 8 Cannot handle non-empty timest [/Table] Answer this question based on the above table: Who was the outgoing manager replaced by Marian Bucurescu?

Answer: Gabriel Mărgărit
[Table] header: Team | Outgoing manager | Manner of departure | Date of vacancy | Replaced by | Date of appointment row 1 : Rapid Bucureşti | Marian Rada | Contract not renewed | 3 Cannot handle non-empty timest | José Peseiro | 3 Cannot handle non-empty timest
row 2 : Otopeni | Gabriel Mărgărit | Sacked | 8 Cannot handle non-empty timest | Marian Bucurescu | 8 Cannot handle non-empty timest
row 3 : Rapid Bucureşti | José Peseiro | Sacked | 2 Cannot handle non-empty timest | Nicolae Manea | 3 Cannot handle non-empty timest
row 4 : Gloria Buzău | Álvaro Magalhãe | Sacked | 3 Cannot handle non-empty timest | Mario Marinică | 9 Cannot handle non-empty timest
row 5 : Rapid Bucureşti | Nicolae Manea | Caretaker | 9 Cannot handle non-empty timest | José Peseiro | 9 Cannot handle non-empty timest
row 6 : Vaslui | Viorel Hizo | Sacked | 1 Cannot handle non-empty timest | Viorel Moldovan | 1 Cannot handle non-empty timest
row 7 : CFR Cluj | Maurizio Trombetta | Sacked | 6 Cannot handle non-empty timest | Ales Jindra and Dušan Uhrin | 8 Cannot handle non-empty timest [/Table] Answer this question based on the above table: What was the manner of departure of Nicolae Manea?

Answer: Caretaker
[Table] header: Game | Date | Opponent | Score | High points | High rebounds | High assists | Location/Attendance | Record row 1 : 4 | June 1 | Connecticut | 73-75 | Perkins (21) | Fowles (12) | Canty (5) | UIC Pavilion 2,276 | 1-3
row 2 : 5 | June 3 | Los Angeles | 77-81 (OT) | Dupree (22) | Dupree (11) | Canty (8) | UIC Pavilion 6,304 | 1-4
row 3 : 6 | June 6 | @ Atlanta | 86-72 | Canty , Perkins (16) | Dupree , Perkins (9) | Price (5) | Philips Arena 7,418 | 2-4
row 4 : 7 | June 7 | Atlanta | 91-70 | Dupree (20) | Melvin (7) | Canty (6) | UIC Pavilion 3,182 | 3-4
row 5 : 8 | June 13 | Washington | 57-64 | Perkins (17) | Dupree (8) | Canty , Price (4) | UIC Pavilion 2,600 | 3-5
row 6 : 9 | June 18 | @ Los Angeles | 67-80 | Perkins (18) | Dupree (8) | Canty , Dupree (3) | STAPLES Center 7,245 | 3-6
row 7 : 10 | June 20 | @ Phoenix | 105-112 (OT) | Perkins (30) | Dupree (12) | Dupree (5) | US Airways Center 7,311 | 3-7
row 8 : 11 | June 22 | @ Sacramento | 70-82 | Perkins (22) | Dupree (11) | Canty (8) | ARCO Arena 6,107 | 3-8
row 9 : 12 | June 26 | Phoenix | 79-89 | Melvin (19) | Melvin (15) | Wyckoff (9) | UIC Pavilion 3,103 | 3-9 [/Table] Answer this question based on the above table: What was the score on game number 12?

Answer: 79-89
[Table] header: Game | Date | Opponent | Score | High points | High rebounds | High assists | Location/Attendance | Record row 1 : 4 | June 1 | Connecticut | 73-75 | Perkins (21) | Fowles (12) | Canty (5) | UIC Pavilion 2,276 | 1-3
row 2 : 5 | June 3 | Los Angeles | 77-81 (OT) | Dupree (22) | Dupree (11) | Canty (8) | UIC Pavilion 6,304 | 1-4
row 3 : 6 | June 6 | @ Atlanta | 86-72 | Canty , Perkins (16) | Dupree , Perkins (9) | Price (5) | Philips Arena 7,418 | 2-4
row 4 : 7 | June 7 | Atlanta | 91-70 | Dupree (20) | Melvin (7) | Canty (6) | UIC Pavilion 3,182 | 3-4
row 5 : 8 | June 13 | Washington | 57-64 | Perkins (17) | Dupree (8) | Canty , Price (4) | UIC Pavilion 2,600 | 3-5
row 6 : 9 | June 18 | @ Los Angeles | 67-80 | Perkins (18) | Dupree (8) | Canty , Dupree (3) | STAPLES Center 7,245 | 3-6
row 7 : 10 | June 20 | @ Phoenix | 105-112 (OT) | Perkins (30) | Dupree (12) | Dupree (5) | US Airways Center 7,311 | 3-7
row 8 : 11 | June 22 | @ Sacramento | 70-82 | Perkins (22) | Dupree (11) | Canty (8) | ARCO Arena 6,107 | 3-8
row 9 : 12 | June 26 | Phoenix | 79-89 | Melvin (19) | Melvin (15) | Wyckoff (9) | UIC Pavilion 3,103 | 3-9 [/Table] Answer this question based on the above table: What was the record when Canty (5) was the high assists?

Answer: 1-3
[Table] header: Game | Date | Opponent | Score | High points | High rebounds | High assists | Location/Attendance | Record row 1 : 4 | June 1 | Connecticut | 73-75 | Perkins (21) | Fowles (12) | Canty (5) | UIC Pavilion 2,276 | 1-3
row 2 : 5 | June 3 | Los Angeles | 77-81 (OT) | Dupree (22) | Dupree (11) | Canty (8) | UIC Pavilion 6,304 | 1-4
row 3 : 6 | June 6 | @ Atlanta | 86-72 | Canty , Perkins (16) | Dupree , Perkins (9) | Price (5) | Philips Arena 7,418 | 2-4
row 4 : 7 | June 7 | Atlanta | 91-70 | Dupree (20) | Melvin (7) | Canty (6) | UIC Pavilion 3,182 | 3-4
row 5 : 8 | June 13 | Washington | 57-64 | Perkins (17) | Dupree (8) | Canty , Price (4) | UIC Pavilion 2,600 | 3-5
row 6 : 9 | June 18 | @ Los Angeles | 67-80 | Perkins (18) | Dupree (8) | Canty , Dupree (3) | STAPLES Center 7,245 | 3-6
row 7 : 10 | June 20 | @ Phoenix | 105-112 (OT) | Perkins (30) | Dupree (12) | Dupree (5) | US Airways Center 7,311 | 3-7
row 8 : 11 | June 22 | @ Sacramento | 70-82 | Perkins (22) | Dupree (11) | Canty (8) | ARCO Arena 6,107 | 3-8
row 9 : 12 | June 26 | Phoenix | 79-89 | Melvin (19) | Melvin (15) | Wyckoff (9) | UIC Pavilion 3,103 | 3-9 [/Table] Answer this question based on the above table: How many players received high points where location/attendance was UIC Pavilion 6,304 respectively?

Answer: 1.0
[Table] header: Game | Date | Opponent | Score | High points | High rebounds | High assists | Location/Attendance | Record row 1 : 4 | June 1 | Connecticut | 73-75 | Perkins (21) | Fowles (12) | Canty (5) | UIC Pavilion 2,276 | 1-3
row 2 : 5 | June 3 | Los Angeles | 77-81 (OT) | Dupree (22) | Dupree (11) | Canty (8) | UIC Pavilion 6,304 | 1-4
row 3 : 6 | June 6 | @ Atlanta | 86-72 | Canty , Perkins (16) | Dupree , Perkins (9) | Price (5) | Philips Arena 7,418 | 2-4
row 4 : 7 | June 7 | Atlanta | 91-70 | Dupree (20) | Melvin (7) | Canty (6) | UIC Pavilion 3,182 | 3-4
row 5 : 8 | June 13 | Washington | 57-64 | Perkins (17) | Dupree (8) | Canty , Price (4) | UIC Pavilion 2,600 | 3-5
row 6 : 9 | June 18 | @ Los Angeles | 67-80 | Perkins (18) | Dupree (8) | Canty , Dupree (3) | STAPLES Center 7,245 | 3-6
row 7 : 10 | June 20 | @ Phoenix | 105-112 (OT) | Perkins (30) | Dupree (12) | Dupree (5) | US Airways Center 7,311 | 3-7
row 8 : 11 | June 22 | @ Sacramento | 70-82 | Perkins (22) | Dupree (11) | Canty (8) | ARCO Arena 6,107 | 3-8
row 9 : 12 | June 26 | Phoenix | 79-89 | Melvin (19) | Melvin (15) | Wyckoff (9) | UIC Pavilion 3,103 | 3-9 [/Table] Answer this question based on the above table: On what date did Canty (6) receive high assists?

Answer: June 7
[Table] header: Game | Date | Opponent | Score | High points | High rebounds | High assists | Location/Attendance | Record row 1 : 4 | June 1 | Connecticut | 73-75 | Perkins (21) | Fowles (12) | Canty (5) | UIC Pavilion 2,276 | 1-3
row 2 : 5 | June 3 | Los Angeles | 77-81 (OT) | Dupree (22) | Dupree (11) | Canty (8) | UIC Pavilion 6,304 | 1-4
row 3 : 6 | June 6 | @ Atlanta | 86-72 | Canty , Perkins (16) | Dupree , Perkins (9) | Price (5) | Philips Arena 7,418 | 2-4
row 4 : 7 | June 7 | Atlanta | 91-70 | Dupree (20) | Melvin (7) | Canty (6) | UIC Pavilion 3,182 | 3-4
row 5 : 8 | June 13 | Washington | 57-64 | Perkins (17) | Dupree (8) | Canty , Price (4) | UIC Pavilion 2,600 | 3-5
row 6 : 9 | June 18 | @ Los Angeles | 67-80 | Perkins (18) | Dupree (8) | Canty , Dupree (3) | STAPLES Center 7,245 | 3-6
row 7 : 10 | June 20 | @ Phoenix | 105-112 (OT) | Perkins (30) | Dupree (12) | Dupree (5) | US Airways Center 7,311 | 3-7
row 8 : 11 | June 22 | @ Sacramento | 70-82 | Perkins (22) | Dupree (11) | Canty (8) | ARCO Arena 6,107 | 3-8
row 9 : 12 | June 26 | Phoenix | 79-89 | Melvin (19) | Melvin (15) | Wyckoff (9) | UIC Pavilion 3,103 | 3-9 [/Table] Answer this question based on the above table: Which game number had a location/attendance of US Airways Center 7,311 respectively?

Answer: 10.0
[Table] header: Game | Date | Opponent | Score | High points | High rebounds | High assists | Location/Attendance | Record row 1 : 4 | June 1 | Connecticut | 73-75 | Perkins (21) | Fowles (12) | Canty (5) | UIC Pavilion 2,276 | 1-3
row 2 : 5 | June 3 | Los Angeles | 77-81 (OT) | Dupree (22) | Dupree (11) | Canty (8) | UIC Pavilion 6,304 | 1-4
row 3 : 6 | June 6 | @ Atlanta | 86-72 | Canty , Perkins (16) | Dupree , Perkins (9) | Price (5) | Philips Arena 7,418 | 2-4
row 4 : 7 | June 7 | Atlanta | 91-70 | Dupree (20) | Melvin (7) | Canty (6) | UIC Pavilion 3,182 | 3-4
row 5 : 8 | June 13 | Washington | 57-64 | Perkins (17) | Dupree (8) | Canty , Price (4) | UIC Pavilion 2,600 | 3-5
row 6 : 9 | June 18 | @ Los Angeles | 67-80 | Perkins (18) | Dupree (8) | Canty , Dupree (3) | STAPLES Center 7,245 | 3-6
row 7 : 10 | June 20 | @ Phoenix | 105-112 (OT) | Perkins (30) | Dupree (12) | Dupree (5) | US Airways Center 7,311 | 3-7
row 8 : 11 | June 22 | @ Sacramento | 70-82 | Perkins (22) | Dupree (11) | Canty (8) | ARCO Arena 6,107 | 3-8
row 9 : 12 | June 26 | Phoenix | 79-89 | Melvin (19) | Melvin (15) | Wyckoff (9) | UIC Pavilion 3,103 | 3-9 [/Table] Answer this question based on the above table: On which date was the record 1-4?

Answer: June 3
[Table] header: Official Name | Status | Area km 2 | Population | Census Ranking row 1 : Studholm | Parish | 449.43 | 3526 | 891 of 5,008
row 2 : Kingston | Parish | 200.54 | 2888 | 1,016 of 5,008
row 3 : Hampton | Parish | 121.39 | 2724 | 1,068 of 5,008
row 4 : Sussex | Parish | 244.27 | 2427 | 1,164 of 5,008
row 5 : Westfield | Parish | 295.07 | 2053 | 1,290 of 5,008
row 6 : Springfield | Parish | 248.22 | 1572 | 1,554 of 5,008
row 7 : Cardwell | Parish | 311.72 | 1479 | 1,608 of 5,008
row 8 : Upham | Parish | 188.55 | 1267 | 1,765 of 5,008
row 9 : Norton | Parish | 144.62 | 1209 | 1,820 of 5,008
row 10 : Havelock | Parish | 349.34 | 1189 | 1,842 of 5,008
row 11 : Greenwich | Parish | 114.31 | 1043 | 2,006 of 5,008
row 12 : Waterford | Parish | 221.12 | 505 | 3,000 of 5,008
row 13 : Kars | Parish | 75.96 | 424 | 3,243 of 5,008
row 14 : Rothesay | Parish | 7.31 | 350 | 3,496 of 5,008 [/Table] Answer this question based on the above table: What is the population in Westfield?

Answer: 1.0
[Table] header: Official Name | Status | Area km 2 | Population | Census Ranking row 1 : Studholm | Parish | 449.43 | 3526 | 891 of 5,008
row 2 : Kingston | Parish | 200.54 | 2888 | 1,016 of 5,008
row 3 : Hampton | Parish | 121.39 | 2724 | 1,068 of 5,008
row 4 : Sussex | Parish | 244.27 | 2427 | 1,164 of 5,008
row 5 : Westfield | Parish | 295.07 | 2053 | 1,290 of 5,008
row 6 : Springfield | Parish | 248.22 | 1572 | 1,554 of 5,008
row 7 : Cardwell | Parish | 311.72 | 1479 | 1,608 of 5,008
row 8 : Upham | Parish | 188.55 | 1267 | 1,765 of 5,008
row 9 : Norton | Parish | 144.62 | 1209 | 1,820 of 5,008
row 10 : Havelock | Parish | 349.34 | 1189 | 1,842 of 5,008
row 11 : Greenwich | Parish | 114.31 | 1043 | 2,006 of 5,008
row 12 : Waterford | Parish | 221.12 | 505 | 3,000 of 5,008
row 13 : Kars | Parish | 75.96 | 424 | 3,243 of 5,008
row 14 : Rothesay | Parish | 7.31 | 350 | 3,496 of 5,008 [/Table] Answer this question based on the above table: What is the Area KM 2 of the place that has a Census ranking of 1,016 of 5,008?

Answer: 200.54
[Table] header: Official Name | Status | Area km 2 | Population | Census Ranking row 1 : Studholm | Parish | 449.43 | 3526 | 891 of 5,008
row 2 : Kingston | Parish | 200.54 | 2888 | 1,016 of 5,008
row 3 : Hampton | Parish | 121.39 | 2724 | 1,068 of 5,008
row 4 : Sussex | Parish | 244.27 | 2427 | 1,164 of 5,008
row 5 : Westfield | Parish | 295.07 | 2053 | 1,290 of 5,008
row 6 : Springfield | Parish | 248.22 | 1572 | 1,554 of 5,008
row 7 : Cardwell | Parish | 311.72 | 1479 | 1,608 of 5,008
row 8 : Upham | Parish | 188.55 | 1267 | 1,765 of 5,008
row 9 : Norton | Parish | 144.62 | 1209 | 1,820 of 5,008
row 10 : Havelock | Parish | 349.34 | 1189 | 1,842 of 5,008
row 11 : Greenwich | Parish | 114.31 | 1043 | 2,006 of 5,008
row 12 : Waterford | Parish | 221.12 | 505 | 3,000 of 5,008
row 13 : Kars | Parish | 75.96 | 424 | 3,243 of 5,008
row 14 : Rothesay | Parish | 7.31 | 350 | 3,496 of 5,008 [/Table] Answer this question based on the above table: What is the Area KM 2 of the place that has a Census ranking of 1,608 of 5,008?

Answer: 311.72
[Table] header: Official Name | Status | Area km 2 | Population | Census Ranking row 1 : Studholm | Parish | 449.43 | 3526 | 891 of 5,008
row 2 : Kingston | Parish | 200.54 | 2888 | 1,016 of 5,008
row 3 : Hampton | Parish | 121.39 | 2724 | 1,068 of 5,008
row 4 : Sussex | Parish | 244.27 | 2427 | 1,164 of 5,008
row 5 : Westfield | Parish | 295.07 | 2053 | 1,290 of 5,008
row 6 : Springfield | Parish | 248.22 | 1572 | 1,554 of 5,008
row 7 : Cardwell | Parish | 311.72 | 1479 | 1,608 of 5,008
row 8 : Upham | Parish | 188.55 | 1267 | 1,765 of 5,008
row 9 : Norton | Parish | 144.62 | 1209 | 1,820 of 5,008
row 10 : Havelock | Parish | 349.34 | 1189 | 1,842 of 5,008
row 11 : Greenwich | Parish | 114.31 | 1043 | 2,006 of 5,008
row 12 : Waterford | Parish | 221.12 | 505 | 3,000 of 5,008
row 13 : Kars | Parish | 75.96 | 424 | 3,243 of 5,008
row 14 : Rothesay | Parish | 7.31 | 350 | 3,496 of 5,008 [/Table] Answer this question based on the above table: What is the Area KM 2 of Rothesay?

Answer: 1.0
[Table] header: Official Name | Status | Area km 2 | Population | Census Ranking row 1 : Studholm | Parish | 449.43 | 3526 | 891 of 5,008
row 2 : Kingston | Parish | 200.54 | 2888 | 1,016 of 5,008
row 3 : Hampton | Parish | 121.39 | 2724 | 1,068 of 5,008
row 4 : Sussex | Parish | 244.27 | 2427 | 1,164 of 5,008
row 5 : Westfield | Parish | 295.07 | 2053 | 1,290 of 5,008
row 6 : Springfield | Parish | 248.22 | 1572 | 1,554 of 5,008
row 7 : Cardwell | Parish | 311.72 | 1479 | 1,608 of 5,008
row 8 : Upham | Parish | 188.55 | 1267 | 1,765 of 5,008
row 9 : Norton | Parish | 144.62 | 1209 | 1,820 of 5,008
row 10 : Havelock | Parish | 349.34 | 1189 | 1,842 of 5,008
row 11 : Greenwich | Parish | 114.31 | 1043 | 2,006 of 5,008
row 12 : Waterford | Parish | 221.12 | 505 | 3,000 of 5,008
row 13 : Kars | Parish | 75.96 | 424 | 3,243 of 5,008
row 14 : Rothesay | Parish | 7.31 | 350 | 3,496 of 5,008 [/Table] Answer this question based on the above table: What's the official name of the place with an Area KM 2 of 295.07

Answer: Westfield
[Table] header: Official Name | Status | Area km 2 | Population | Census Ranking row 1 : Studholm | Parish | 449.43 | 3526 | 891 of 5,008
row 2 : Kingston | Parish | 200.54 | 2888 | 1,016 of 5,008
row 3 : Hampton | Parish | 121.39 | 2724 | 1,068 of 5,008
row 4 : Sussex | Parish | 244.27 | 2427 | 1,164 of 5,008
row 5 : Westfield | Parish | 295.07 | 2053 | 1,290 of 5,008
row 6 : Springfield | Parish | 248.22 | 1572 | 1,554 of 5,008
row 7 : Cardwell | Parish | 311.72 | 1479 | 1,608 of 5,008
row 8 : Upham | Parish | 188.55 | 1267 | 1,765 of 5,008
row 9 : Norton | Parish | 144.62 | 1209 | 1,820 of 5,008
row 10 : Havelock | Parish | 349.34 | 1189 | 1,842 of 5,008
row 11 : Greenwich | Parish | 114.31 | 1043 | 2,006 of 5,008
row 12 : Waterford | Parish | 221.12 | 505 | 3,000 of 5,008
row 13 : Kars | Parish | 75.96 | 424 | 3,243 of 5,008
row 14 : Rothesay | Parish | 7.31 | 350 | 3,496 of 5,008 [/Table] Answer this question based on the above table: What is the population of Cardwell?

Answer: 1479.0
[Table] header: Official Name | Status | Area km 2 | Population | Census Ranking row 1 : Addington | Parish | 1024.99 | 2727 | 1,064 of 5,008
row 2 : Dalhousie | Parish | 121.73 | 2323 | 1,205 of 5,008
row 3 : Saint-Quentin | Parish | 2,478.88 | 1578 | 1,549 of 5,008
row 4 : Durham | Parish | 410.28 | 1303 | 1,736 of 5,008
row 5 : Eldon | Parish | 1,681.51 | 779 | 2,406 of 5,008
row 6 : Balmoral | Parish | 1,098.18 | 619 | 2,726 of 5,008 [/Table] Answer this question based on the above table: Name the total number of status for eldon

Answer: 1.0
[Table] header: Official Name | Status | Area km 2 | Population | Census Ranking row 1 : Addington | Parish | 1024.99 | 2727 | 1,064 of 5,008
row 2 : Dalhousie | Parish | 121.73 | 2323 | 1,205 of 5,008
row 3 : Saint-Quentin | Parish | 2,478.88 | 1578 | 1,549 of 5,008
row 4 : Durham | Parish | 410.28 | 1303 | 1,736 of 5,008
row 5 : Eldon | Parish | 1,681.51 | 779 | 2,406 of 5,008
row 6 : Balmoral | Parish | 1,098.18 | 619 | 2,726 of 5,008 [/Table] Answer this question based on the above table: Name the area km 2 for balmoral

Answer: 1098.18
[Table] header: Game | Date | Team | Score | High points | High rebounds | High assists | Location Attendance | Record row 1 : 1 | April 18 | Chicago | L 103–105 (OT) | Rajon Rondo (29) | Rajon Rondo (9) | Rajon Rondo (7) | TD Banknorth Garden 18,624 | 0–1
row 2 : 2 | April 20 | Chicago | W 118–115 (OT) | Ray Allen (30) | Rajon Rondo , Kendrick Per | Rajon Rondo (16) | TD Banknorth Garden 18,624 | 1–1
row 3 : 3 | April 23 | @ Chicago | W 107–86 (OT) | Paul Pierce (24) | Rajon Rondo (11) | Glen Davis , Stephon Marbury (6) | United Center 23,072 | 2–1
row 4 : 4 | April 26 | @ Chicago | L 118–121 (2OT) | Paul Pierce (29) | Glen Davis (12) | Rajon Rondo (11) | United Center 23,067 | 2–2
row 5 : 5 | April 28 | Chicago | W 106–104 (OT) | Rajon Rondo (28) | Kendrick Perkins (19) | Rajon Rondo (16) | TD Banknorth Garden 18,624 | 3–2
row 6 : 6 | April 30 | @ Chicago | L 127–128 (3OT) | Ray Allen (51) | Kendrick Perkins (13) | Rajon Rondo (19) | United Center 23,430 | 3–3 [/Table] Answer this question based on the above table: What team(s) did they play on april 23?

Answer: @ Chicago
[Table] header: Game | Date | Team | Score | High points | High rebounds | High assists | Location Attendance | Record row 1 : 1 | April 18 | Chicago | L 103–105 (OT) | Rajon Rondo (29) | Rajon Rondo (9) | Rajon Rondo (7) | TD Banknorth Garden 18,624 | 0–1
row 2 : 2 | April 20 | Chicago | W 118–115 (OT) | Ray Allen (30) | Rajon Rondo , Kendrick Per | Rajon Rondo (16) | TD Banknorth Garden 18,624 | 1–1
row 3 : 3 | April 23 | @ Chicago | W 107–86 (OT) | Paul Pierce (24) | Rajon Rondo (11) | Glen Davis , Stephon Marbury (6) | United Center 23,072 | 2–1
row 4 : 4 | April 26 | @ Chicago | L 118–121 (2OT) | Paul Pierce (29) | Glen Davis (12) | Rajon Rondo (11) | United Center 23,067 | 2–2
row 5 : 5 | April 28 | Chicago | W 106–104 (OT) | Rajon Rondo (28) | Kendrick Perkins (19) | Rajon Rondo (16) | TD Banknorth Garden 18,624 | 3–2
row 6 : 6 | April 30 | @ Chicago | L 127–128 (3OT) | Ray Allen (51) | Kendrick Perkins (13) | Rajon Rondo (19) | United Center 23,430 | 3–3 [/Table] Answer this question based on the above table: What score(s) were recorded on april 28?

Answer: W 106–104 (OT)
[Table] header: Series | Title | Broadcasts (TV) 1 | Episodes (TV+extra) 2 | Directors | Aired in Japan 3 | US Release Date row 1 : 1 | Galaxy Angel | 24 | 24+2 | Morio Asaka , Yoshimit | 7 April 2001 to 29 September 2001 | 27 June 2006
row 2 : 2 | Galaxy Angel Z | 9 | 18+1 | Morio Asaka, Yoshimits | 3 February 2002 to 31 March 2002 | 25 July 2006
row 3 : 3 4 | Galaxy Angel A | 13 | 26 | Shigehito Takayanagi | 10 November 2002 to 29 December 2002 | 27 December 2006
row 4 : 3 4 | Galaxy Angel AA | 13 | 25+2 | Shigehito Takayanagi | 5 January 2003 to 30 March 2003 | 19 August 2008
row 5 : 3 4 | Galaxy Angel S 5 | 1 | 2 | Shigehito Takayanagi | 21 December 2003 | 6 May 2008 [/Table] Answer this question based on the above table: When galaxy angel aa is the title what was the series?

Answer: 3 4
[Table] header: Series | Title | Broadcasts (TV) 1 | Episodes (TV+extra) 2 | Directors | Aired in Japan 3 | US Release Date row 1 : 1 | Galaxy Angel | 24 | 24+2 | Morio Asaka , Yoshimit | 7 April 2001 to 29 September 2001 | 27 June 2006
row 2 : 2 | Galaxy Angel Z | 9 | 18+1 | Morio Asaka, Yoshimits | 3 February 2002 to 31 March 2002 | 25 July 2006
row 3 : 3 4 | Galaxy Angel A | 13 | 26 | Shigehito Takayanagi | 10 November 2002 to 29 December 2002 | 27 December 2006
row 4 : 3 4 | Galaxy Angel AA | 13 | 25+2 | Shigehito Takayanagi | 5 January 2003 to 30 March 2003 | 19 August 2008
row 5 : 3 4 | Galaxy Angel S 5 | 1 | 2 | Shigehito Takayanagi | 21 December 2003 | 6 May 2008 [/Table] Answer this question based on the above table: When  7 april 2001 to 29 september 2001 was aired in japan 2 how many titles were there?

Answer: 1.0
[Table] header: Series | Title | Broadcasts (TV) 1 | Episodes (TV+extra) 2 | Directors | Aired in Japan 3 | US Release Date row 1 : 1 | Galaxy Angel | 24 | 24+2 | Morio Asaka , Yoshimit | 7 April 2001 to 29 September 2001 | 27 June 2006
row 2 : 2 | Galaxy Angel Z | 9 | 18+1 | Morio Asaka, Yoshimits | 3 February 2002 to 31 March 2002 | 25 July 2006
row 3 : 3 4 | Galaxy Angel A | 13 | 26 | Shigehito Takayanagi | 10 November 2002 to 29 December 2002 | 27 December 2006
row 4 : 3 4 | Galaxy Angel AA | 13 | 25+2 | Shigehito Takayanagi | 5 January 2003 to 30 March 2003 | 19 August 2008
row 5 : 3 4 | Galaxy Angel S 5 | 1 | 2 | Shigehito Takayanagi | 21 December 2003 | 6 May 2008 [/Table] Answer this question based on the above table: When galaxy angel aa was the title was the us release date?

Answer: 19 August 2008
[Table] header: Rank | Administrative Division | Area (km²) | Area (sq mi) | National Share (%) | Comparable Country row 1 : 1 | Xinjiang | 1664897 | 640900 | 17.23 | Iran
row 2 : 2 | Tibet | 1228400 | 474300 | 12.75 | South Africa
row 3 : 3 | Nei Menggu | 1155092 | 456800 | 12.28 | Colombia
row 4 : 4 | Qinghai | 721000 | 278400 | 7.48 | Zambia
row 5 : 5 | Sichuan | 485000 | 187300 | 5.03 | Turkmenistan
row 6 : 6 | Heilongjiang | 452645 | 177600 | 4.77 | Papua New Guinea
row 7 : 7 | Gansu | 454000 | 175300 | 4.71 | Sweden
row 8 : 8 | Yunnan | 383194 | 152200 | 4.09 | Zimbabwe
row 9 : 9 | Guangxi | 237558 | 91400 | 2.45 | Laos
row 10 : 10 | Hunan | 211855 | 81800 | 2.19 | Guyana
row 11 : 11 | Shaanxi | 205800 | 79500 | 2.13 | Belarus
row 12 : 12 | Hebei | 188434 | 72500 | 1.94 | Kyrgyzstan
row 13 : 13 | Jilin | 191124 | 72400 | 1.94 | Senegal
row 14 : 14 | Hubei | 185888 | 71800 | 1.93 | Syria
row 15 : 15 | Guangdong | 179757 | 68700 | 1.84 | Cambodia
row 16 : 16 | Guizhou | 176153 | 68000 | 1.82 | Uruguay
row 17 : 17 | Henan | 165536 | 64500 | 1.73 | Suriname
row 18 : 18 | Jiangxi | 166894 | 64400 | 1.73 | Tunisia
row 19 : 19 | Shanxi | 156711 | 60540 | 1.62 | Nepal
row 20 : 20 | Shandong | 156700 | 60500 | 1.62 | Bangladesh
row 21 : 21 | Liaoning | 148064 | 56300 | 1.51 | Tajikistan
row 22 : 22 | Anhui | 140126 | 53800 | 1.44 | Greece
row 23 : 23 | Fujian, PRC | 124016 | 46900 | 1.26 | North Korea
row 24 : 24 | Zhejiang | 104141 | 40209 | 1.08 | Iceland
row 25 : 25 | Jiangsu | 102600 | 39600 | 1.06 | South Korea
row 26 : 26 | Chongqing | 82269 | 31800 | 0.85 | United Arab Emirates
row 27 : 27 | Ningxia | 66000 | 25500 | 0.68 | Sri Lanka
row 28 : 28 | Hainan | 35354 | 13100 | 0.35 | Moldova
row 29 : 29 | Beijing | 16411 | 6490 | 0.17 | Swaziland
row 30 : 30 | Tianjin | 11917 | 4602 | 0.12 | Vanuatu
row 31 : 31 | Shanghai | 6340 | 2448 | 0.065 | Brunei
row 32 : 32 | Hong Kong | 1104 | 422 | 0.011 | São Tomé and Príncip
row 33 : 33 | Macau | 30 | 10 | 0.00028 | Tuvalu [/Table] Answer this question based on the above table: What comparable country has a national share of 1.08?

Answer: Iceland
[Table] header: Rank | Administrative Division | Area (km²) | Area (sq mi) | National Share (%) | Comparable Country row 1 : 1 | Xinjiang | 1664897 | 640900 | 17.23 | Iran
row 2 : 2 | Tibet | 1228400 | 474300 | 12.75 | South Africa
row 3 : 3 | Nei Menggu | 1155092 | 456800 | 12.28 | Colombia
row 4 : 4 | Qinghai | 721000 | 278400 | 7.48 | Zambia
row 5 : 5 | Sichuan | 485000 | 187300 | 5.03 | Turkmenistan
row 6 : 6 | Heilongjiang | 452645 | 177600 | 4.77 | Papua New Guinea
row 7 : 7 | Gansu | 454000 | 175300 | 4.71 | Sweden
row 8 : 8 | Yunnan | 383194 | 152200 | 4.09 | Zimbabwe
row 9 : 9 | Guangxi | 237558 | 91400 | 2.45 | Laos
row 10 : 10 | Hunan | 211855 | 81800 | 2.19 | Guyana
row 11 : 11 | Shaanxi | 205800 | 79500 | 2.13 | Belarus
row 12 : 12 | Hebei | 188434 | 72500 | 1.94 | Kyrgyzstan
row 13 : 13 | Jilin | 191124 | 72400 | 1.94 | Senegal
row 14 : 14 | Hubei | 185888 | 71800 | 1.93 | Syria
row 15 : 15 | Guangdong | 179757 | 68700 | 1.84 | Cambodia
row 16 : 16 | Guizhou | 176153 | 68000 | 1.82 | Uruguay
row 17 : 17 | Henan | 165536 | 64500 | 1.73 | Suriname
row 18 : 18 | Jiangxi | 166894 | 64400 | 1.73 | Tunisia
row 19 : 19 | Shanxi | 156711 | 60540 | 1.62 | Nepal
row 20 : 20 | Shandong | 156700 | 60500 | 1.62 | Bangladesh
row 21 : 21 | Liaoning | 148064 | 56300 | 1.51 | Tajikistan
row 22 : 22 | Anhui | 140126 | 53800 | 1.44 | Greece
row 23 : 23 | Fujian, PRC | 124016 | 46900 | 1.26 | North Korea
row 24 : 24 | Zhejiang | 104141 | 40209 | 1.08 | Iceland
row 25 : 25 | Jiangsu | 102600 | 39600 | 1.06 | South Korea
row 26 : 26 | Chongqing | 82269 | 31800 | 0.85 | United Arab Emirates
row 27 : 27 | Ningxia | 66000 | 25500 | 0.68 | Sri Lanka
row 28 : 28 | Hainan | 35354 | 13100 | 0.35 | Moldova
row 29 : 29 | Beijing | 16411 | 6490 | 0.17 | Swaziland
row 30 : 30 | Tianjin | 11917 | 4602 | 0.12 | Vanuatu
row 31 : 31 | Shanghai | 6340 | 2448 | 0.065 | Brunei
row 32 : 32 | Hong Kong | 1104 | 422 | 0.011 | São Tomé and Príncip
row 33 : 33 | Macau | 30 | 10 | 0.00028 | Tuvalu [/Table] Answer this question based on the above table: What is the national share of the country whose area is 148064 km^2?

Answer: 1.51
[Table] header: Rank | Administrative Division | Area (km²) | Area (sq mi) | National Share (%) | Comparable Country row 1 : 1 | Xinjiang | 1664897 | 640900 | 17.23 | Iran
row 2 : 2 | Tibet | 1228400 | 474300 | 12.75 | South Africa
row 3 : 3 | Nei Menggu | 1155092 | 456800 | 12.28 | Colombia
row 4 : 4 | Qinghai | 721000 | 278400 | 7.48 | Zambia
row 5 : 5 | Sichuan | 485000 | 187300 | 5.03 | Turkmenistan
row 6 : 6 | Heilongjiang | 452645 | 177600 | 4.77 | Papua New Guinea
row 7 : 7 | Gansu | 454000 | 175300 | 4.71 | Sweden
row 8 : 8 | Yunnan | 383194 | 152200 | 4.09 | Zimbabwe
row 9 : 9 | Guangxi | 237558 | 91400 | 2.45 | Laos
row 10 : 10 | Hunan | 211855 | 81800 | 2.19 | Guyana
row 11 : 11 | Shaanxi | 205800 | 79500 | 2.13 | Belarus
row 12 : 12 | Hebei | 188434 | 72500 | 1.94 | Kyrgyzstan
row 13 : 13 | Jilin | 191124 | 72400 | 1.94 | Senegal
row 14 : 14 | Hubei | 185888 | 71800 | 1.93 | Syria
row 15 : 15 | Guangdong | 179757 | 68700 | 1.84 | Cambodia
row 16 : 16 | Guizhou | 176153 | 68000 | 1.82 | Uruguay
row 17 : 17 | Henan | 165536 | 64500 | 1.73 | Suriname
row 18 : 18 | Jiangxi | 166894 | 64400 | 1.73 | Tunisia
row 19 : 19 | Shanxi | 156711 | 60540 | 1.62 | Nepal
row 20 : 20 | Shandong | 156700 | 60500 | 1.62 | Bangladesh
row 21 : 21 | Liaoning | 148064 | 56300 | 1.51 | Tajikistan
row 22 : 22 | Anhui | 140126 | 53800 | 1.44 | Greece
row 23 : 23 | Fujian, PRC | 124016 | 46900 | 1.26 | North Korea
row 24 : 24 | Zhejiang | 104141 | 40209 | 1.08 | Iceland
row 25 : 25 | Jiangsu | 102600 | 39600 | 1.06 | South Korea
row 26 : 26 | Chongqing | 82269 | 31800 | 0.85 | United Arab Emirates
row 27 : 27 | Ningxia | 66000 | 25500 | 0.68 | Sri Lanka
row 28 : 28 | Hainan | 35354 | 13100 | 0.35 | Moldova
row 29 : 29 | Beijing | 16411 | 6490 | 0.17 | Swaziland
row 30 : 30 | Tianjin | 11917 | 4602 | 0.12 | Vanuatu
row 31 : 31 | Shanghai | 6340 | 2448 | 0.065 | Brunei
row 32 : 32 | Hong Kong | 1104 | 422 | 0.011 | São Tomé and Príncip
row 33 : 33 | Macau | 30 | 10 | 0.00028 | Tuvalu [/Table] Answer this question based on the above table: What is the administrative division that has an area of 30 km^2?

Answer: Macau
[Table] header: Rank | Administrative Division | Area (km²) | Area (sq mi) | National Share (%) | Comparable Country row 1 : 1 | Xinjiang | 1664897 | 640900 | 17.23 | Iran
row 2 : 2 | Tibet | 1228400 | 474300 | 12.75 | South Africa
row 3 : 3 | Nei Menggu | 1155092 | 456800 | 12.28 | Colombia
row 4 : 4 | Qinghai | 721000 | 278400 | 7.48 | Zambia
row 5 : 5 | Sichuan | 485000 | 187300 | 5.03 | Turkmenistan
row 6 : 6 | Heilongjiang | 452645 | 177600 | 4.77 | Papua New Guinea
row 7 : 7 | Gansu | 454000 | 175300 | 4.71 | Sweden
row 8 : 8 | Yunnan | 383194 | 152200 | 4.09 | Zimbabwe
row 9 : 9 | Guangxi | 237558 | 91400 | 2.45 | Laos
row 10 : 10 | Hunan | 211855 | 81800 | 2.19 | Guyana
row 11 : 11 | Shaanxi | 205800 | 79500 | 2.13 | Belarus
row 12 : 12 | Hebei | 188434 | 72500 | 1.94 | Kyrgyzstan
row 13 : 13 | Jilin | 191124 | 72400 | 1.94 | Senegal
row 14 : 14 | Hubei | 185888 | 71800 | 1.93 | Syria
row 15 : 15 | Guangdong | 179757 | 68700 | 1.84 | Cambodia
row 16 : 16 | Guizhou | 176153 | 68000 | 1.82 | Uruguay
row 17 : 17 | Henan | 165536 | 64500 | 1.73 | Suriname
row 18 : 18 | Jiangxi | 166894 | 64400 | 1.73 | Tunisia
row 19 : 19 | Shanxi | 156711 | 60540 | 1.62 | Nepal
row 20 : 20 | Shandong | 156700 | 60500 | 1.62 | Bangladesh
row 21 : 21 | Liaoning | 148064 | 56300 | 1.51 | Tajikistan
row 22 : 22 | Anhui | 140126 | 53800 | 1.44 | Greece
row 23 : 23 | Fujian, PRC | 124016 | 46900 | 1.26 | North Korea
row 24 : 24 | Zhejiang | 104141 | 40209 | 1.08 | Iceland
row 25 : 25 | Jiangsu | 102600 | 39600 | 1.06 | South Korea
row 26 : 26 | Chongqing | 82269 | 31800 | 0.85 | United Arab Emirates
row 27 : 27 | Ningxia | 66000 | 25500 | 0.68 | Sri Lanka
row 28 : 28 | Hainan | 35354 | 13100 | 0.35 | Moldova
row 29 : 29 | Beijing | 16411 | 6490 | 0.17 | Swaziland
row 30 : 30 | Tianjin | 11917 | 4602 | 0.12 | Vanuatu
row 31 : 31 | Shanghai | 6340 | 2448 | 0.065 | Brunei
row 32 : 32 | Hong Kong | 1104 | 422 | 0.011 | São Tomé and Príncip
row 33 : 33 | Macau | 30 | 10 | 0.00028 | Tuvalu [/Table] Answer this question based on the above table: What is the national share of Guizhou's administrative division?

Answer: 1.82
[Table] header: Rank | Administrative Division | Area (km²) | Area (sq mi) | National Share (%) | Comparable Country row 1 : 1 | Xinjiang | 1664897 | 640900 | 17.23 | Iran
row 2 : 2 | Tibet | 1228400 | 474300 | 12.75 | South Africa
row 3 : 3 | Nei Menggu | 1155092 | 456800 | 12.28 | Colombia
row 4 : 4 | Qinghai | 721000 | 278400 | 7.48 | Zambia
row 5 : 5 | Sichuan | 485000 | 187300 | 5.03 | Turkmenistan
row 6 : 6 | Heilongjiang | 452645 | 177600 | 4.77 | Papua New Guinea
row 7 : 7 | Gansu | 454000 | 175300 | 4.71 | Sweden
row 8 : 8 | Yunnan | 383194 | 152200 | 4.09 | Zimbabwe
row 9 : 9 | Guangxi | 237558 | 91400 | 2.45 | Laos
row 10 : 10 | Hunan | 211855 | 81800 | 2.19 | Guyana
row 11 : 11 | Shaanxi | 205800 | 79500 | 2.13 | Belarus
row 12 : 12 | Hebei | 188434 | 72500 | 1.94 | Kyrgyzstan
row 13 : 13 | Jilin | 191124 | 72400 | 1.94 | Senegal
row 14 : 14 | Hubei | 185888 | 71800 | 1.93 | Syria
row 15 : 15 | Guangdong | 179757 | 68700 | 1.84 | Cambodia
row 16 : 16 | Guizhou | 176153 | 68000 | 1.82 | Uruguay
row 17 : 17 | Henan | 165536 | 64500 | 1.73 | Suriname
row 18 : 18 | Jiangxi | 166894 | 64400 | 1.73 | Tunisia
row 19 : 19 | Shanxi | 156711 | 60540 | 1.62 | Nepal
row 20 : 20 | Shandong | 156700 | 60500 | 1.62 | Bangladesh
row 21 : 21 | Liaoning | 148064 | 56300 | 1.51 | Tajikistan
row 22 : 22 | Anhui | 140126 | 53800 | 1.44 | Greece
row 23 : 23 | Fujian, PRC | 124016 | 46900 | 1.26 | North Korea
row 24 : 24 | Zhejiang | 104141 | 40209 | 1.08 | Iceland
row 25 : 25 | Jiangsu | 102600 | 39600 | 1.06 | South Korea
row 26 : 26 | Chongqing | 82269 | 31800 | 0.85 | United Arab Emirates
row 27 : 27 | Ningxia | 66000 | 25500 | 0.68 | Sri Lanka
row 28 : 28 | Hainan | 35354 | 13100 | 0.35 | Moldova
row 29 : 29 | Beijing | 16411 | 6490 | 0.17 | Swaziland
row 30 : 30 | Tianjin | 11917 | 4602 | 0.12 | Vanuatu
row 31 : 31 | Shanghai | 6340 | 2448 | 0.065 | Brunei
row 32 : 32 | Hong Kong | 1104 | 422 | 0.011 | São Tomé and Príncip
row 33 : 33 | Macau | 30 | 10 | 0.00028 | Tuvalu [/Table] Answer this question based on the above table: What is the rank of the division with an area of 456800 sq mi?

Answer: 3.0
[Table] header: Period | % trains arriving within 5 mins of scheduled time (over three months) | Change over same quarter the previous year | % trains arriving within 5 mins of scheduled time Moving Annual Average (MAA) | Change over previous year as a whole row 1 : Apr - Jun 2007 | 91.4% | Up 0.8% | 89.0% | Up 0.2%
row 2 : Jul - Sep 2007 | 93.0% | Up 2.2% | 89.6% | Up 0.7%
row 3 : Oct - Dec 2007 | 87.3% | Up 2.8% | 90.1% | Up 0.6%
row 4 : Jan - Mar 2008 | 90.5% | Up 2.0% | 90.6% | Up 0.6%
row 5 : Apr - Jun 2008 | 93.6% | Up 2.4% | 91.1% | Up 0.6%
row 6 : Jul - Sep 2008 | 92.8% | Down 0.2% | 91.0% | Up 0.4%
row 7 : Oct - Dec 2008 | 86.5% | Down 0.9% | 90.9% | Up 0.3%
row 8 : Jan - Mar 2009 | 89.6% | Down 1.0% > | 90.6% | Down 0.3%
row 9 : Apr - Jun 2009 | 93.0% | Down 0.6% | 90.5% | Down 0.1%
row 10 : Jul - Sep 2009 | 93.5% | Up 0.9% | 90.7% | Up 0.2%
row 11 : Oct - Dec 2009 | 86.7% | Up 0.2% | 90.7% | Unchanged
row 12 : Jan - Mar 2010 | 89.5% | Down 0.1% | 90.7% | Unchanged
row 13 : Jul - Sep 2010 | 94.5% | Up 1.1% | 91.4% | Up 0.8%
row 14 : Oct - Dec 2010 | 78.4% | Down 9.6% | 92.9% | Up 2.4% [/Table] Answer this question based on the above table: What's the change over previous year as a whole in the period in which the change over same quarter the previous year was up 2.8%?

Answer: Up 0.6%
[Table] header: Period | % trains arriving within 5 mins of scheduled time (over three months) | Change over same quarter the previous year | % trains arriving within 5 mins of scheduled time Moving Annual Average (MAA) | Change over previous year as a whole row 1 : Apr - Jun 2007 | 91.4% | Up 0.8% | 89.0% | Up 0.2%
row 2 : Jul - Sep 2007 | 93.0% | Up 2.2% | 89.6% | Up 0.7%
row 3 : Oct - Dec 2007 | 87.3% | Up 2.8% | 90.1% | Up 0.6%
row 4 : Jan - Mar 2008 | 90.5% | Up 2.0% | 90.6% | Up 0.6%
row 5 : Apr - Jun 2008 | 93.6% | Up 2.4% | 91.1% | Up 0.6%
row 6 : Jul - Sep 2008 | 92.8% | Down 0.2% | 91.0% | Up 0.4%
row 7 : Oct - Dec 2008 | 86.5% | Down 0.9% | 90.9% | Up 0.3%
row 8 : Jan - Mar 2009 | 89.6% | Down 1.0% > | 90.6% | Down 0.3%
row 9 : Apr - Jun 2009 | 93.0% | Down 0.6% | 90.5% | Down 0.1%
row 10 : Jul - Sep 2009 | 93.5% | Up 0.9% | 90.7% | Up 0.2%
row 11 : Oct - Dec 2009 | 86.7% | Up 0.2% | 90.7% | Unchanged
row 12 : Jan - Mar 2010 | 89.5% | Down 0.1% | 90.7% | Unchanged
row 13 : Jul - Sep 2010 | 94.5% | Up 1.1% | 91.4% | Up 0.8%
row 14 : Oct - Dec 2010 | 78.4% | Down 9.6% | 92.9% | Up 2.4% [/Table] Answer this question based on the above table: When was the percentage of trains arriving within 5 minutes of scheduled time 89.6%?

Answer: Jul - Sep 2007
[Table] header: Period | % trains arriving within 5 mins of scheduled time (over three months) | Change over same quarter the previous year | % trains arriving within 5 mins of scheduled time Moving Annual Average (MAA) | Change over previous year as a whole row 1 : Apr - Jun 2007 | 91.4% | Up 0.8% | 89.0% | Up 0.2%
row 2 : Jul - Sep 2007 | 93.0% | Up 2.2% | 89.6% | Up 0.7%
row 3 : Oct - Dec 2007 | 87.3% | Up 2.8% | 90.1% | Up 0.6%
row 4 : Jan - Mar 2008 | 90.5% | Up 2.0% | 90.6% | Up 0.6%
row 5 : Apr - Jun 2008 | 93.6% | Up 2.4% | 91.1% | Up 0.6%
row 6 : Jul - Sep 2008 | 92.8% | Down 0.2% | 91.0% | Up 0.4%
row 7 : Oct - Dec 2008 | 86.5% | Down 0.9% | 90.9% | Up 0.3%
row 8 : Jan - Mar 2009 | 89.6% | Down 1.0% > | 90.6% | Down 0.3%
row 9 : Apr - Jun 2009 | 93.0% | Down 0.6% | 90.5% | Down 0.1%
row 10 : Jul - Sep 2009 | 93.5% | Up 0.9% | 90.7% | Up 0.2%
row 11 : Oct - Dec 2009 | 86.7% | Up 0.2% | 90.7% | Unchanged
row 12 : Jan - Mar 2010 | 89.5% | Down 0.1% | 90.7% | Unchanged
row 13 : Jul - Sep 2010 | 94.5% | Up 1.1% | 91.4% | Up 0.8%
row 14 : Oct - Dec 2010 | 78.4% | Down 9.6% | 92.9% | Up 2.4% [/Table] Answer this question based on the above table: What's the change over same quarter the previous year in the period when the 89.6% of the trains arrive within 5 minutes of scheduled time (over three months)?

Answer: Down 1.0% >
[Table] header: Municipality | Shire | Population | Area in km 2 | Density in inhab\km 2 | Distance to San Sebastians city centre in km row 1 : Donostia-San Sebastian | Donostialdea | 183308 | 60.89 | 3,006.9 | 0
row 2 : Irun | Bidasoa-Txingudi | 60261 | 42.40 | 1,421.25 | 16.5
row 3 : Errenteria | Donostialdea | 38336 | 32.26 | 1,188.34 | 6.8
row 4 : Hernani | Donostialdea | 19138 | 39.82 | 480.61 | 6
row 5 : Lasarte-Oria | Donostialdea | 17647 | 6.01 | 2,936.27 | 6.5
row 6 : Pasaia | Donostialdea | 16091 | 11.34 | 1,418.96 | 4
row 7 : Hondarribia | Bidasoa-Txingudi | 16073 | 28.63 | 561.4 | 17.5
row 8 : Andoain | Donostialdea | 14215 | 27.17 | 523.19 | 11.5
row 9 : Hendaye | Arrondissement of Bayonne ( France | 12596 | 8 | 1,574.5 | 18
row 10 : Oiartzun | Donostialdea | 9806 | 59.71 | 164.23 | 10.5
row 11 : Urnieta | Donostialdea | 5998 | 22.4 | 267.77 | 8
row 12 : Lezo | Donostialdea | 5912 | 8.59 | 688.24 | 10.5
row 13 : Usurbil | Donostialdea | 5718 | 25.64 | 223.01 | 7.6 [/Table] Answer this question based on the above table: how many shire in the land of area in km 2 is 27.17

Answer: 1.0
[Table] header: Country | Amateur Era | Open Era | All-time | First title | Last title row 1 : United States (USA) | 17 | 24 | 41 | 1926 | 2013
row 2 : Australia (AUS) | 26 | 13 | 39 | 1935 | 2000
row 3 : France (FRA) | 20 | 2 | 22 | 1925 | 1984
row 4 : Sweden (SWE) | 1 | 6 | 7 | 1948 | 2006
row 5 : Netherlands (NED) | 0 | 6 | 6 | 1973 | 2002
row 6 : India (IND) | 0 | 5 | 5 | 1999 | 2009
row 7 : South Africa (RSA) | 2 | 2 | 4 | 1947 | 1972
row 8 : Spain (ESP) | 1 | 3 | 4 | 1963 | 1990
row 9 : Belarus (BLR) | 0 | 4 | 4 | 2005 | 2012
row 10 : Canada (CAN) | 0 | 4 | 4 | 2007 | 2012
row 11 : Switzerland (SUI) | 0 | 3 | 3 | 1981 | 1992
row 12 : Russia (RUS) | 0 | 3 | 3 | 1996 | 2002
row 13 : Czech Republic (CZE) | 0 | 3 | 3 | 1996 | 2009
row 14 : Great Britain (GBR) | 2 | 0 | 2 | 1933 | 1933
row 15 : Germany (GER) | 2 | 0 | 2 | 1937 | 1937
row 16 : Czechoslovakia (TCH) | 1 | 1 | 2 | 1948 | 1986
row 17 : Italy (ITA) | 2 | 0 | 2 | 1959 | 1959
row 18 : Romania (ROU) | 0 | 2 | 2 | 1970 | 1970
row 19 : Mexico (MEX) | 0 | 2 | 2 | 1975 | 1977
row 20 : Belgium (BEL) | 0 | 2 | 2 | 2004 | 2004
row 21 : New Zealand (NZL) | 0 | 1 | 1 | 1974 | 1974
row 22 : Hungary (HUN) | 0 | 1 | 1 | 1981 | 1981
row 23 : Ecuador (ECU) | 0 | 1 | 1 | 1988 | 1988
row 24 : Zimbabwe (ZIM) | 0 | 1 | 1 | 1994 | 1994
row 25 : Bahamas (BAH) | 0 | 1 | 1 | 2007 | 2007
row 26 : Peru (PER) | 0 | 1 | 1 | 2008 | 2008
row 27 : Uruguay (URU) | 0 | 1 | 1 | 2008 | 2008 [/Table] Answer this question based on the above table: How many open era titles does the team with their first title in 1925 have?

Answer: 2.0
[Table] header: Team | Outgoing manager | Manner of departure | Date of vacancy | Replaced by | Date of appointment row 1 : Rimini | Leonardo Acori | Mutual consent | 1 Cannot handle non-empty timest | Elvio Selighini | 3 Cannot handle non-empty timest
row 2 : Frosinone | Alberto Cavasin | Mutual consent | 5 Cannot handle non-empty timest | Piero Braglia | 6 Cannot handle non-empty timest
row 3 : Avellino | Giuseppe Incocciati | Sacked | 7 Cannot handle non-empty timest | Salvatore Campilongo | 7 Cannot handle non-empty timest
row 4 : Ascoli | Vincenzo Chiarenza | Sacked | 7 Cannot handle non-empty timest | Franco Colomba | 7 Cannot handle non-empty timest
row 5 : Salernitana | Fabrizio Castori | Sacked | 6 Cannot handle non-empty timest | Bortolo Mutti | 7 Cannot handle non-empty timest
row 6 : Mantova | Alessandro Costacurta | Resigned | 9 Cannot handle non-empty timest | Mario Somma | 9 Cannot handle non-empty timest
row 7 : Salernitana | Fabrizio Castori | Sacked | 4 Cannot handle non-empty timest | Fabio Brini | 4 Cannot handle non-empty timest [/Table] Answer this question based on the above table: Name the replacced by with ascoli

Answer: Franco Colomba
[Table] header: Team | Outgoing manager | Manner of departure | Date of vacancy | Replaced by | Date of appointment row 1 : Rimini | Leonardo Acori | Mutual consent | 1 Cannot handle non-empty timest | Elvio Selighini | 3 Cannot handle non-empty timest
row 2 : Frosinone | Alberto Cavasin | Mutual consent | 5 Cannot handle non-empty timest | Piero Braglia | 6 Cannot handle non-empty timest
row 3 : Avellino | Giuseppe Incocciati | Sacked | 7 Cannot handle non-empty timest | Salvatore Campilongo | 7 Cannot handle non-empty timest
row 4 : Ascoli | Vincenzo Chiarenza | Sacked | 7 Cannot handle non-empty timest | Franco Colomba | 7 Cannot handle non-empty timest
row 5 : Salernitana | Fabrizio Castori | Sacked | 6 Cannot handle non-empty timest | Bortolo Mutti | 7 Cannot handle non-empty timest
row 6 : Mantova | Alessandro Costacurta | Resigned | 9 Cannot handle non-empty timest | Mario Somma | 9 Cannot handle non-empty timest
row 7 : Salernitana | Fabrizio Castori | Sacked | 4 Cannot handle non-empty timest | Fabio Brini | 4 Cannot handle non-empty timest [/Table] Answer this question based on the above table: Name the team for mario somma

Answer: Mantova
[Table] header: Team | Outgoing manager | Manner of departure | Date of vacancy | Replaced by | Date of appointment row 1 : Rimini | Leonardo Acori | Mutual consent | 1 Cannot handle non-empty timest | Elvio Selighini | 3 Cannot handle non-empty timest
row 2 : Frosinone | Alberto Cavasin | Mutual consent | 5 Cannot handle non-empty timest | Piero Braglia | 6 Cannot handle non-empty timest
row 3 : Avellino | Giuseppe Incocciati | Sacked | 7 Cannot handle non-empty timest | Salvatore Campilongo | 7 Cannot handle non-empty timest
row 4 : Ascoli | Vincenzo Chiarenza | Sacked | 7 Cannot handle non-empty timest | Franco Colomba | 7 Cannot handle non-empty timest
row 5 : Salernitana | Fabrizio Castori | Sacked | 6 Cannot handle non-empty timest | Bortolo Mutti | 7 Cannot handle non-empty timest
row 6 : Mantova | Alessandro Costacurta | Resigned | 9 Cannot handle non-empty timest | Mario Somma | 9 Cannot handle non-empty timest
row 7 : Salernitana | Fabrizio Castori | Sacked | 4 Cannot handle non-empty timest | Fabio Brini | 4 Cannot handle non-empty timest [/Table] Answer this question based on the above table: Name the date of appointment for ascoli

Answer: 7 Cannot handle non-empty timest
[Table] header: Team | Outgoing manager | Manner of departure | Date of vacancy | Replaced by | Date of appointment row 1 : Rimini | Leonardo Acori | Mutual consent | 1 Cannot handle non-empty timest | Elvio Selighini | 3 Cannot handle non-empty timest
row 2 : Frosinone | Alberto Cavasin | Mutual consent | 5 Cannot handle non-empty timest | Piero Braglia | 6 Cannot handle non-empty timest
row 3 : Avellino | Giuseppe Incocciati | Sacked | 7 Cannot handle non-empty timest | Salvatore Campilongo | 7 Cannot handle non-empty timest
row 4 : Ascoli | Vincenzo Chiarenza | Sacked | 7 Cannot handle non-empty timest | Franco Colomba | 7 Cannot handle non-empty timest
row 5 : Salernitana | Fabrizio Castori | Sacked | 6 Cannot handle non-empty timest | Bortolo Mutti | 7 Cannot handle non-empty timest
row 6 : Mantova | Alessandro Costacurta | Resigned | 9 Cannot handle non-empty timest | Mario Somma | 9 Cannot handle non-empty timest
row 7 : Salernitana | Fabrizio Castori | Sacked | 4 Cannot handle non-empty timest | Fabio Brini | 4 Cannot handle non-empty timest [/Table] Answer this question based on the above table: Name the manner of departure for elvio selighini

Answer: Mutual consent
[Table] header: Team | Outgoing manager | Manner of departure | Date of vacancy | Replaced by | Date of appointment row 1 : Rimini | Leonardo Acori | Mutual consent | 1 Cannot handle non-empty timest | Elvio Selighini | 3 Cannot handle non-empty timest
row 2 : Frosinone | Alberto Cavasin | Mutual consent | 5 Cannot handle non-empty timest | Piero Braglia | 6 Cannot handle non-empty timest
row 3 : Avellino | Giuseppe Incocciati | Sacked | 7 Cannot handle non-empty timest | Salvatore Campilongo | 7 Cannot handle non-empty timest
row 4 : Ascoli | Vincenzo Chiarenza | Sacked | 7 Cannot handle non-empty timest | Franco Colomba | 7 Cannot handle non-empty timest
row 5 : Salernitana | Fabrizio Castori | Sacked | 6 Cannot handle non-empty timest | Bortolo Mutti | 7 Cannot handle non-empty timest
row 6 : Mantova | Alessandro Costacurta | Resigned | 9 Cannot handle non-empty timest | Mario Somma | 9 Cannot handle non-empty timest
row 7 : Salernitana | Fabrizio Castori | Sacked | 4 Cannot handle non-empty timest | Fabio Brini | 4 Cannot handle non-empty timest [/Table] Answer this question based on the above table: Name the date of vacancy for fabio brini

Answer: 4 Cannot handle non-empty timest
[Table] header: Team | Outgoing manager | Manner of departure | Date of vacancy | Replaced by | Date of appointment row 1 : Rimini | Leonardo Acori | Mutual consent | 1 Cannot handle non-empty timest | Elvio Selighini | 3 Cannot handle non-empty timest
row 2 : Frosinone | Alberto Cavasin | Mutual consent | 5 Cannot handle non-empty timest | Piero Braglia | 6 Cannot handle non-empty timest
row 3 : Avellino | Giuseppe Incocciati | Sacked | 7 Cannot handle non-empty timest | Salvatore Campilongo | 7 Cannot handle non-empty timest
row 4 : Ascoli | Vincenzo Chiarenza | Sacked | 7 Cannot handle non-empty timest | Franco Colomba | 7 Cannot handle non-empty timest
row 5 : Salernitana | Fabrizio Castori | Sacked | 6 Cannot handle non-empty timest | Bortolo Mutti | 7 Cannot handle non-empty timest
row 6 : Mantova | Alessandro Costacurta | Resigned | 9 Cannot handle non-empty timest | Mario Somma | 9 Cannot handle non-empty timest
row 7 : Salernitana | Fabrizio Castori | Sacked | 4 Cannot handle non-empty timest | Fabio Brini | 4 Cannot handle non-empty timest [/Table] Answer this question based on the above table: Name the outgoing manager for mantova

Answer: Alessandro Costacurta
[Table] header: Game | Date | Team | Score | High points | High rebounds | High assists | Location Attendance | Record row 1 : 3 | November 1 | Oklahoma City | W 89–77 (OT) | Tracy McGrady (22) | Carl Landry , Yao Ming (1 | Tracy McGrady (5) | Toyota Center 16,996 | 3–0
row 2 : 4 | November 4 | Boston | L 99–103 (OT) | Tracy McGrady (26) | Luis Scola (9) | Rafer Alston (7) | Toyota Center 18,291 | 3–1
row 3 : 5 | November 6 | @ Portland | L 99–101 (OT) | Tracy McGrady (30) | Tracy McGrady , Chuck Hayes | Tracy McGrady (8) | Rose Garden 20,611 | 3–2
row 4 : 6 | November 7 | @ L.A. Clippers | W 92–83 (OT) | Ron Artest (23) | Carl Landry , Yao Ming ( | Rafer Alston (8) | Staples Center 14,670 | 4–2
row 5 : 7 | November 9 | @ L.A. Lakers | L 82–111 (OT) | Aaron Brooks (20) | Luis Scola (9) | Aaron Brooks , Tracy McGrady  | Staples Center 18,997 | 4–3
row 6 : 8 | November 12 | @ Phoenix | W 94–82 (OT) | Tracy McGrady (27) | Yao Ming (15) | Rafer Alston (4) | US Airways Center 18,422 | 5–3
row 7 : 9 | November 14 | @ San Antonio | L 75–77 (OT) | Ron Artest (18) | Aaron Brooks , Yao Ming  | Tracy McGrady (5) | AT&T Center 18,797 | 5–4
row 8 : 10 | November 15 | New Orleans | W 91–82 (OT) | Yao Ming (21) | Luis Scola (10) | Ron Artest (7) | Toyota Center 18,303 | 6–4
row 9 : 11 | November 17 | @ Oklahoma City | W 100–89 (OT) | Luis Scola (23) | Yao Ming (12) | Aaron Brooks (4) | Ford Center 18,145 | 7–4
row 10 : 12 | November 19 | Dallas | L 86–96 (OT) | Rafer Alston , Tracy McGrady | Ron Artest (13) | Rafer Alston (8) | Toyota Center 18,203 | 7–5
row 11 : 13 | November 21 | @ Washington | W 103–91 (OT) | Tracy McGrady (20) | Ron Artest (9) | Ron Artest (6) | Verizon Center 20,173 | 8–5
row 12 : 14 | November 22 | @ Orlando | W 100–95 (OT) | Yao Ming (22) | Yao Ming (13) | Rafer Alston (7) | Amway Arena 17,461 | 9–5
row 13 : 15 | November 24 | @ Miami | W 107–98 (OT) | Yao Ming (28) | Luis Scola (13) | Yao Ming , Tracy McGrady | American Airlines Arena 18,704 | 10–5
row 14 : 16 | November 26 | Indiana | L 90–91 (OT) | Ron Artest , Yao Ming (19 | Luis Scola (18) | Rafer Alston (7) | Toyota Center 18,194 | 10–6
row 15 : 17 | November 29 | San Antonio | W 103–84 (OT) | Luther Head (21) | Ron Artest (8) | Rafer Alston (6) | Toyota Center 18,282 | 11–6 [/Table] Answer this question based on the above table: What is the San Antonio's team score?

Answer: W 103–84 (OT)