Table:
#

Name

Term[N 2]

Regular season

Playoffs

Accomplishments

Ref.

Yrs

First

Last

GC

W

L

T

Win%

GC

W

L

1

Tom Coughlin

8

1995

2002

128

68

60

0

.531

8

4

4

2 AFC Central Championships (1998, 1999)
4 Playoff Berths

[3]

2

Jack Del Rio

9

2003

2011[N 3]

139

68

71

0

.489

3

1

2

2 Playoff Berths

[4]

3

Mel Tucker*

1

2011[N 3]

5

2

3

0

.400

—

[5]

4

Mike Mularkey

1

2012

16

2

14

0

.125

—

[6]

5

Gus Bradley*

4

2013

2016[N 4]

62

14

48

0

.226

—

[7]

6

Doug Marrone

3

2016[N 4]

present

34

16

18

0

.471

3

2

1

1 AFC South Championship (2017)
1 Playoff Berth

[8]

Examine the given table and locate the cells that contain the following values:
1. 'mike mularkey'
2. '128'
3. '1,153,523'
4. '3,145'
5. '2003'
6. 'mel tucker'
7. 'gus bradley'
8. '139'
9. '1,361'
10. 'jack del rio'
11. '679,538'
Return the cell locations using the JSON format {'value': '<cell value>', 'location': (row_id, column_id)}, where both row and column id commence from 1. Use 'DOES NOT EXIST' as the location for a non-existing cell in the table.

The target cell locations are as follows:
1. {'value': 'mike mularkey', 'location': (6, 2)}
2. {'value': '128', 'location': (3, 6)}
3. {'value': '1,153,523', 'location': 'DOES NOT EXIST'}
4. {'value': '3,145', 'location': 'DOES NOT EXIST'}
5. {'value': '2003', 'location': (4, 4)}
6. {'value': 'mel tucker', 'location': (5, 2)}
7. {'value': 'gus bradley', 'location': (7, 2)}
8. {'value': '139', 'location': (4, 6)}
9. {'value': '1,361', 'location': 'DOES NOT EXIST'}
10. {'value': 'jack del rio', 'location': (4, 2)}
11. {'value': '679,538', 'location': 'DOES NOT EXIST'}