Table:
Rank
Country
Box Office
Year
Box office
from national films[22]

1
Canada/United States
$10.8 billion
2012
–

2
China
$3.6 billion[23]
2013[23]
59% (2013)[24]

3
Japan
$1.88 billion[25]
2013
61% (2013)

4
United Kingdom
$1.7 billion
2012
36.1% (2011)

5
France
$1.7 billion
2012
33.3% (2013)[26]

6
South Korea
$1.47 billion
2013
59.7% (2013)[27]

7
India
$1.4 billion
2012
–

8
Germany
$1.3 billion
2012
–

9
Russia
$1.2 billion
2012
–

10
Australia
$1.2 billion
2012
4.1% (2011)

11
Italy
$0.84 billion
2013
30% (2013)[26]

12
Brazil
$0.72 billion
2013
17% (2013)[28]

-
World
$34.7 billion
2012
–

the 2nd row,
the 7th row,
the 11th row,
the 13th row,
the 12th row,
Extract the contents of cells in the above rows, identified by row indices. Output the cell values in a row as JSON in the format {"row_id":"<row index>", "cell_list":"<a list of cells in this row>"}, e.g., {"row_id":"3", "cell_list": ["123.89", "News gathering", "selected tools"]}.

The extracted row contents are listed below.
For the 2nd row: {"row_id": "2", "cell_list": ['1', 'Canada/United States', '$10.8 billion', '2012', '–']}
For the 7th row: {"row_id": "7", "cell_list": ['6', 'South Korea', '$1.47 billion', '2013', '59.7% (2013)']}
For the 11th row: {"row_id": "11", "cell_list": ['10', 'Australia', '$1.2 billion', '2012', '4.1% (2011)']}
For the 13th row: {"row_id": "13", "cell_list": ['12', 'Brazil', '$0.72 billion', '2013', '17% (2013)']}
For the 12th row: {"row_id": "12", "cell_list": ['11', 'Italy', '$0.84 billion', '2013', '30% (2013)']}