# data[].attributes.body
1  F
2 F
3 T
4 T
5 F
6 F
7 T
8 T
9 F
10 T

# data[].attributes.body
1. F
2. T
3. T
4. T
5. T
6. T
7. F
8. T
9. F
10. T

# data[].attributes.body
1F
2 F
3 T
4 T
5T
6T
7T
8T
9F
10T

# data[].attributes.body
1.F
2. F
3.T
4.T
5.T
6.T
7..T
8.T
9.F
10.T

# data[].attributes.body
1. F
2. F
3. T
4. T
5. T
6. F
7. T
8. T
9. F
10. T

# data[].attributes.body
1. F Beijing-Guangjhou
2.F 55- 71
3. T
4.F some of-most of
5. F- the walls- the ceiling panels
6. F 78-366
7. T
8. T
9. F 70%-50%
10. T

# data[].attributes.body
1. F
2. F
3. T
4. T
5. T
6.
7. T
8. T
9. F
10. T

# data[].attributes.body
1.F Beijing-> Guangjhou in China

2.F
3.T
4.T
5.T
6.F
7.F
8.T
9.T
10.T

# data[].attributes.body
1. The Pearl River Tower is located in Beijing.

2. The tower has 55 storeys.

3. The tower is exactly 309 metres high.

4. The wind provides some of its power.

5. Radiant cooling is cooling by the use of water pipes in the walls of the building.

6. The Bank of America Tower is 78 metres high.

7. It is situated in the centre of New York.

8. Construction of the tower was completed in 2010.

9. The building will use 70% less energy than ordinary buildings.

10. The walls are glass, so most of the lighting is natural lighting.

# data[].attributes.body
1. F. Beijing-> Guangzhou
2. F. 55->71
3. T
4. T
5. T
6. F . 78->366
7.T
8.T
9. F
10.T

# data[].attributes.body
1. F
2. F
3. T
4. T
5. T
6. F
7. T
8. T
9. F
10. T

# data[].attributes.body
1. F
2. F
3. T
4. T
5. T
6. T
7. T
8. T
9. F
10. T

# data[].attributes.body
1. F
2. F
3. T
4. T
5. T
6. F
7. T
8. T
9. F
10. T

# data[].attributes.body
1 F : Guangjhou
2 F 71
3 T
4 T
5 T
6 F 78 - 366
7 T
8 T
9 F 70% - 50%
10 T

# data[].attributes.body
1.F in Guangzhou in China
2.F 71 storeys
3.T
4.T
5. F
6.T
7.T
8.T
9.F
10.T

# data[].attributes.body
1 F
2 F
3 T
4 T
5 T
6 F
7 T
8 T
9 F
10 T

# data[].attributes.body
1. F: Beijing -> China
2. F 55 ->71
3.T
4.T
5.T
6.F 78 -> 366
7.T
8.T
9 F
10 T

# data[].attributes.body
1. F
2. F
3. T
4. F
5. T
6. F
7. T
8. T
9. T
10. T

# data[].attributes.body
1.F
2.F
3.T
4.T
5.F
6.F
7.T
8.T
9.F
10.T

# data[].attributes.body
1F
2F
3T
4F
5T
6T
7T
8T
9F
10T

# data[].attributes.body
1. F
2. F
3. T
4. T
5.T
6. F
7. T
8.T
9.F
10.T

# data[].attributes.body
1. F
2. F
3. T
4. T
5. T
6. F
7. T
8. T
9. F
10. T

# data[].attributes.body
1. F
2. F
3. T
4. T
5. T
6. F
7. T
8. T
9. F
10. T

# data[].attributes.body
1 F
2 F
3 T
4  T
5 T
6
7 T
8 T
9 F
10T

# data[].attributes.body
1. F
2. F
3. T
4. T
5. T
6. F
7. T
8. T
9.F
10. T

# data[].attributes.body
1 F
2 F
3 T
4 T
5 T
6 F
7 F
8 T
9 T
10 T

# data[].attributes.body
1 F
2 F
3 T
4 T
5 T
6 T
7 F
8 T
9 F
10 T

# data[].attributes.body
1.F
2.F
3.T
4.T
5.F
6.F
7.T
8.T
9.F
10.T

# data[].attributes.body
1.f
2.tt
3.t
4.t
5.t
6.f
7.t
8.t
9.f
10.t

# data[].attributes.body
1.F
2.F
3.T
4.F
5.T
6.F
7.F
8.T
9.T
10.T

# data[].attributes.body
1 F: Beijing -> Guangzhou
2 F: 55-> 71
3 T
4 T
5 T
6 F: 38 -> 366
7 T
8 T
9 F: 70 -> 50
10 T