MMTU-benchmark/MMTU huggingface dataset:
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |    Col_2 |    Col_3 |    Col_4 |    Col_5 |    Col_6 |    Col_7 |    Col_8 |    Col_9 |   Col_10 |   Col_11 |   Col_12 |
|--------:|--------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|      25 |   25010 | 11765.9  | 11377.4  |  9985.22 |  9741.27 |  9128.89 |  8642.34 |  7907.29 |  7847.65 |  6737.72 |  6693.29 |  6095.67 |
|      81 |   81010 | 16074    | 16059.6  | 16248.1  | 16152.3  | 16278.8  | 16093.5  | 16270.8  | 16226.2  | 16105    | 15934.8  | 16453.8  |
|      50 |   50230 | 15269.8  | 15264.1  | 15407.3  | 15353.2  | 15145.1  | 15054.7  | 14942.6  | 14918    | 14822.6  | 14733.6  | 14607    |
|      82 |   82010 | 75677.4  | 73435.6  | 67002.9  | 66351.2  | 60254.8  | 59011    | 45464.1  | 45191.1  | 44418.1  | 43980.8  | 43238.9  |
|      74 |   74010 |  9735.54 |  9563.67 |  9338.89 |  9205.84 |  9224.11 |  9123    |  8983.02 |  8929.84 |  8736.08 |  8592.27 |  8521.62 |

Candidate column headers:
2013 - 2014 - SPRING
2011 - 2012 - FALL
2010 - 2011 - SPRING
2012 - 2013 - SPRING
2009 - 2010 - FALL
ISD Code
2010 - 2011 - FALL
2009 - 2010 - SPRING
2014 - 2015 - FALL
District Code
2011 - 2012 - SPRING
2013 - 2014 - FALL
2012 - 2013 - FALL



headers: 2013 - 2014 - SPRING
2011 - 2012 - FALL
2010 - 2011 - SPRING
2012 - 2013 - SPRING
2009 - 2010 - FALL
ISD Code
2010 - 2011 - FALL
2009 - 2010 - SPRING
2014 - 2015 - FALL
District Code
2011 - 2012 - SPRING
2013 - 2014 - FALL
2012 - 2013 - FALL
label: ISD Code
District Code
2009 - 2010 - FALL
2009 - 2010 - SPRING
2010 - 2011 - FALL
2010 - 2011 - SPRING
2011 - 2012 - FALL
2011 - 2012 - SPRING
2012 - 2013 - FALL
2012 - 2013 - SPRING
2013 - 2014 - FALL
2013 - 2014 - SPRING
2014 - 2015 - FALL
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |
|--------:|--------:|--------:|--------:|
|      11 |    12.1 |    16.5 |  23.265 |
|      27 |    29.7 |    40.5 |  57.105 |
|      39 |    42.9 |    58.5 |  82.485 |
|       4 |     4.4 |     6   |   8.46  |
|      22 |    24.2 |    33   |  46.53  |

Candidate column headers:
telefonate
Costo A
Costo C
Costo B



headers: telefonate
Costo A
Costo C
Costo B
label: telefonate
Costo A
Costo B
Costo C
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1      |   Col_2 | Col_3                 |   Col_4 | Col_5   |   Col_6 |   Col_7 |   Col_8 | Col_9        |   Col_10 |   Col_11 |   Col_12 |   Col_13 |   Col_14 |   Col_15 |   Col_16 |   Col_17 |
|:--------|:-----------|--------:|:----------------------|--------:|:--------|--------:|--------:|--------:|:-------------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
| EQUIP   | BOILER     |       6 | BOILER                |       6 | BLRGASW |       7 |      27 |      70 | DIV02_ DIV07 |        1 |        1 |        1 |        2 |        3 |        1 |        1 |        2 |
| EQUIP   | BCKFLWPREV |      10 | BACKFLOW PREVENTER    |      18 | BKFL-DM |       7 |      28 |      87 | DIV02        |        1 |        1 |        1 |        1 |        1 |        1 |        1 |        1 |
| NONE    | AC-UNIT    |       7 | AIR CONDITIONING UNIT |      21 | ACWINDW |       7 |      23 |      69 | DIV01        |        4 |        4 |        4 |        4 |        4 |        4 |        1 |        4 |
| EQUIP   | AC-UNIT    |       7 | AIR CONDITIONING UNIT |      21 | AC-CRAC |       7 |      30 |      43 | DIV08        |        1 |        1 |        1 |        2 |        2 |        1 |        1 |        2 |
| EQUIP   | BATTERY    |       7 | BATTERY               |       7 | BATTDRY |       7 |      12 |      66 | DIV03        |        1 |        1 |        1 |        1 |        1 |        1 |        1 |        1 |

Candidate column headers:
ADMIN/SUPPORT
DORMITORY
CENTRAL HEAT PLANT
len3
ACADEMIC RESEARCH
DIVISION_(EQ Group)
Record PM Type
ATHLETICS
Keyword (10)
AUXILIARY
BIOMED
len2
Keyword Description(35)
Type(7)
len
ACADEMIC NON-RESEARCH
len4
len1



headers: ADMIN/SUPPORT
DORMITORY
CENTRAL HEAT PLANT
len3
ACADEMIC RESEARCH
DIVISION_(EQ Group)
Record PM Type
ATHLETICS
Keyword (10)
AUXILIARY
BIOMED
len2
Keyword Description(35)
Type(7)
len
ACADEMIC NON-RESEARCH
len4
len1
label: Record PM Type
Keyword (10)
len
Keyword Description(35)
len1
Type(7)
len2
len3
len4
DIVISION_(EQ Group)
ACADEMIC NON-RESEARCH
ACADEMIC RESEARCH
ADMIN/SUPPORT
ATHLETICS
AUXILIARY
BIOMED
CENTRAL HEAT PLANT
DORMITORY
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 | Col_4                 | Col_5   | Col_6                     | Col_7   | Col_8    | Col_9    | Col_10   |
|--------:|--------:|--------:|--------:|:----------------------|:--------|:--------------------------|:--------|:---------|:---------|:---------|
|     881 | 9162165 |    2165 |  115598 | LA maintained schools |         |                           |         |          |          | 107881.0 |
|     572 | 9162001 |    2001 |  138674 | Academies             | 41153.0 | AET                       | Yes     | 111848.0 | 155659.0 |          |
|     539 | 9163018 |    3018 |  115610 | LA maintained schools |         |                           |         |          |          | 107710.0 |
|     382 | 9166905 |    6905 |  136016 | Academies             | 40787.0 | Diocese of Glos & Clifton | Yes     | 110437.0 | 149270.0 |          |
|     801 | 9162134 |    2134 |  115570 | LA maintained schools |         |                           |         |          |          | 107801.0 |

Candidate column headers:
DfE No (shortened)
Sponsored Academy
Central School Cost Centre
Planned Conversion Date
DfE No
Debtor Number
School LA Number
URN
Bank Account School/Academy
Vendor Number
EstablishmentTypeGroup (name)



headers: DfE No (shortened)
Sponsored Academy
Central School Cost Centre
Planned Conversion Date
DfE No
Debtor Number
School LA Number
URN
Bank Account School/Academy
Vendor Number
EstablishmentTypeGroup (name)
label: School LA Number
DfE No
DfE No (shortened)
URN
EstablishmentTypeGroup (name)
Planned Conversion Date
Sponsored Academy
Bank Account School/Academy
Debtor Number
Vendor Number
Central School Cost Centre
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 | Col_2          |    Col_3 |
|:--------|--------:|:---------------|---------:|
|         |       0 |                | -18205.5 |
| 17000.0 |   13600 | personal train |  10100   |
|         |       0 |                | -14300   |
| 8000.0  |    6400 | joining fee    |   -400   |
|         |       0 |                |  11700   |

Candidate column headers:
BANK BALANCE
DESCRIPTION
DEPOSITS
Deposits2



headers: BANK BALANCE
DESCRIPTION
DEPOSITS
Deposits2
label: DEPOSITS
Deposits2
DESCRIPTION
BANK BALANCE
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |
|--------:|--------:|--------:|
|     149 |     744 |  975.61 |
|     100 |    1871 |  975.72 |
|     100 |     548 |  977.34 |
|     100 |     695 |  976.74 |
|     100 |    1528 |  976.03 |

Candidate column headers:
Mean
Y
X



headers: Mean
Y
X
label: X
Y
Mean
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |
|--------:|--------:|--------:|
|   41230 |   41329 |   11090 |
|   40609 |   41669 |   11000 |
|   41313 |   41320 |    4388 |
|   40609 |   41335 |   11000 |
|   41359 |   41366 |    4388 |

Candidate column headers:
departure
arrival date
rate/month



headers: departure
arrival date
rate/month
label: arrival date
departure
rate/month
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 |   Col_2 |   Col_3 |
|:--------|--------:|--------:|--------:|
| Tree 11 |     281 |     358 |     639 |
| Tree 13 |     585 |      68 |     653 |
| Tree 7  |     401 |     231 |     632 |
| Tree 10 |     344 |     120 |     464 |
| Tree 2  |     257 |     120 |     377 |

Candidate column headers:
afternoon
total
Apples
morning



headers: afternoon
total
Apples
morning
label: Apples
morning
afternoon
total
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1     | Col_2   | Col_3   | Col_4   | Col_5   | Col_6                          |
|--------:|:----------|:--------|:--------|:--------|:--------|:-------------------------------|
|      25 | 1075-6671 |         | -       | 2002-10 | 2008-10 | OmniFile                       |
|      34 | 0968-0160 |         | -       | 2012-10 | 2015-08 | MEDLINE Complete               |
|      39 | 0023-3234 |         | -       | 2015-10 | present | Hospitality & Tourism Complete |
|      23 | 1528-9729 |         | -       | 2000-01 | 2008-11 | OmniFile                       |
|      12 | 0732-5452 |         | -       | 1998-04 | present | OmniFile                       |

Candidate column headers:
Embargo Days
ISSN
first  year
last  year
Original Company
No
EISSN



headers: Embargo Days
ISSN
first  year
last  year
Original Company
No
EISSN
label: No
ISSN
Embargo Days
EISSN
first  year
last  year
Original Company
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   | Col_1           | Col_2   | Col_3       | Col_4       | Col_5   | Col_6        | Col_7       | Col_8             | Col_9   | Col_10   | Col_11   | Col_12             | Col_13   | Col_14                   |
|:--------|:----------------|:--------|:------------|:------------|:--------|:-------------|:------------|:------------------|:--------|:---------|:---------|:-------------------|:---------|:-------------------------|
| SEAS    | CCM Afghanistan | CCM     | Afghanistan | Afghanistan | Male    | Tuberculosis | Afghanistan | Non-public sector | PLWD    |          |          | 39197.083333333336 | Member   | Non-Recipient            |
| SEAS    | CCM Afghanistan | CCM     | Afghanistan | Afghanistan | Male    |              | Afghanistan | Non-public sector | NGO     |          |          | 41193.083333333336 | Member   | Non-Recipient            |
| EECA    | CCM Albania     | CCM     | Albania     | Albania     | Male    | HIV/AIDS     | Albania     | Public sector     | GOV     |          |          | 40182.04166666666  | Member   | PR - Principal recipient |
| SEAS    | CCM Afghanistan | CCM     | Afghanistan | Afghanistan | Female  |              | Afghanistan |                   | OTH     | UNODC    |          |                    | Member   |                          |
| EECA    | CCM Albania     | CCM     | Albania     | Albania     | Female  | HIV/AIDS     | Albania     | Non-public sector | NGO     |          | 2-Youth  | 39703.083333333336 | Member   |                          |

Candidate column headers:
Public sector or non-Public sector
Indicate if PR or SR or Sub-SR
Organization Coding
Sector Represented 
Regional Team
Principal Expertise
Territory Overseen
Gender
Account Name
KAP category represented_ if any
Countries
Role
Member Since
Organization Represents
Account Level



headers: Public sector or non-Public sector
Indicate if PR or SR or Sub-SR
Organization Coding
Sector Represented 
Regional Team
Principal Expertise
Territory Overseen
Gender
Account Name
KAP category represented_ if any
Countries
Role
Member Since
Organization Represents
Account Level
label: Regional Team
Account Name
Account Level
Territory Overseen
Countries
Gender
Principal Expertise
Organization Represents
Public sector or non-Public sector
Sector Represented 
Organization Coding
KAP category represented_ if any
Member Since
Role
Indicate if PR or SR or Sub-SR
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 | Col_2   |   Col_3 |
|:--------|--------:|:--------|--------:|
|         |  0      |         |     0   |
| 0.002   |  0.0012 | 1.0     |     1.2 |
|         |  0      | 10.0    |     0   |
| 2.5     |  1.5    | 100.0   |    15   |
|         |  0      | 10.0    |     0   |

Candidate column headers:
Ingredient amount  (grams)
Desired  Concentration (g/L)
Extract component strength (g/L)
Extract amount (milliliters)



headers: Ingredient amount  (grams)
Desired  Concentration (g/L)
Extract component strength (g/L)
Extract amount (milliliters)
label: Desired  Concentration (g/L)
Ingredient amount  (grams)
Extract component strength (g/L)
Extract amount (milliliters)
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 | Col_6   |     Col_7 |
|--------:|--------:|--------:|--------:|--------:|--------:|:--------|----------:|
|    1163 |    1010 |    86.8 |    5633 |    5105 | 90.6267 | False   | -3.82666  |
|     155 |     141 |    91   |     635 |     575 | 90.5512 | True    |  0.448819 |
|     393 |     388 |    98.7 |    1194 |    1177 | 98.5762 | True    |  0.123786 |
|     735 |     567 |    77.1 |    3224 |    2506 | 77.7295 | False   | -0.629529 |
|    1051 |     896 |    85.3 |    4113 |    3540 | 86.0686 | False   | -0.768563 |

Candidate column headers:
PCC Transfers -Success Grades (N)
GreaterOrEqualThanUA
PCC Transfers -Success Rate (%)
UA -Success Grades (N)
Diff_%Points
UA -Success Rate (%)
PCC Transfers - total Grades (N)
UA - total Grades (N)



headers: PCC Transfers -Success Grades (N)
GreaterOrEqualThanUA
PCC Transfers -Success Rate (%)
UA -Success Grades (N)
Diff_%Points
UA -Success Rate (%)
PCC Transfers - total Grades (N)
UA - total Grades (N)
label: PCC Transfers - total Grades (N)
PCC Transfers -Success Grades (N)
PCC Transfers -Success Rate (%)
UA - total Grades (N)
UA -Success Grades (N)
UA -Success Rate (%)
GreaterOrEqualThanUA
Diff_%Points
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1             |            Col_2 |   Col_3 |
|--------:|:------------------|-----------------:|--------:|
|     509 | Elgin             |      1.98451e+06 | 1.71183 |
|     537 | Richland          | 416353           | 1.11252 |
|     517 | Lake Land         |      1.26776e+06 | 3.09592 |
|     502 | College of DuPage |      3.98501e+06 | 2.02073 |
|     515 | Prairie State     | 777206           | 1.80926 |

Candidate column headers:
Dist.  No.
Total Utility  Costs Per  GSF **
Total  Utility Costs*
District



headers: Dist.  No.
Total Utility  Costs Per  GSF **
Total  Utility Costs*
District
label: Dist.  No.
District
Total  Utility Costs*
Total Utility  Costs Per  GSF **
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1                |   Col_2 |
|--------:|:---------------------|--------:|
|   42560 | Antisocial Behaviour |    2016 |
|   42526 | Antisocial Behaviour |    2016 |
|   42542 | Antisocial Behaviour |    2016 |
|   42619 | Antisocial Behaviour |    2016 |
|   42581 | Antisocial Behaviour |    2016 |

Candidate column headers:
Sub category
Year
Incident date



headers: Sub category
Year
Incident date
label: Incident date
Sub category
Year
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1                       | Col_2              | Col_3           | Col_4      |   Col_5 |
|--------:|:----------------------------|:-------------------|:----------------|:-----------|--------:|
|       5 | I - Inherently Governmental | US - United States | CA - California | Dublin     |    2006 |
|      12 | I - Inherently Governmental | US - United States | AL - Alabama    | Mobile     |    2006 |
|      40 | I - Inherently Governmental | US - United States | AL - Alabama    | Huntsville |    2006 |
|       1 | I - Inherently Governmental | US - United States | CA - California | Glendale   |    2006 |
|      11 | I - Inherently Governmental | US - United States | CA - California | Carlsbad   |    2006 |

Candidate column headers:
State
City
First Year on Inventory
Total FTE
Reason Code
Country Code



headers: State
City
First Year on Inventory
Total FTE
Reason Code
Country Code
label: Total FTE
Reason Code
Country Code
State
City
First Year on Inventory
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2   | Col_3   | Col_4   | Col_5             | Col_6          |   Col_7 | Col_8   | Col_9   | Col_10   |
|--------:|:--------|:--------|:--------|:--------|:------------------|:---------------|--------:|:--------|:--------|:---------|
|      36 | m-1     | -       | -       | b       | SDN:P01::BB117G01 | SDN:P061::PMSR |   42023 | active  | 99999.f | float    |
|      11 | degree  | 10.f    | 70.f    | i       | SDN:P01::OXYCPHAB | SDN:P061::UAAA |   41759 | active  | 99999.f | float    |
|      24 | count   | -       | -       | i       | SDN:P01::NVLTOBS1 | SDN:P061::UUUU |   42023 | active  | 99999.f | float    |
|      34 | m-1     | -       | -       | b       | SDN:P01::BSXXXVSF | SDN:P061::PMSR |   42023 | active  | 99999.f | float    |
|      20 | degree  | 10.f    | 70.f    | i       | SDN:P01::OXYCPHAR | SDN:P061::UAAA |   41566 | active  | 99999.f | float    |

Candidate column headers:
status
sdn_parameter_urn
Data Type
unit
valid_min
Order
core/bio/intermediate
valid_max
sdn_uom_urn
update date
Fillvalue



headers: status
sdn_parameter_urn
Data Type
unit
valid_min
Order
core/bio/intermediate
valid_max
sdn_uom_urn
update date
Fillvalue
label: Order
unit
valid_min
valid_max
core/bio/intermediate
sdn_parameter_urn
sdn_uom_urn
update date
status
Fillvalue
Data Type
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1     | Col_2   | Col_3   |   Col_4 |
|--------:|:----------|:--------|:--------|--------:|
|   54514 | BUTTERNUT | WI      | Ashland |     300 |
|   54550 | MONTREAL  | WI      | Iron    |     300 |
|   54559 | SAXON     | WI      | Iron    |     300 |
|   54530 | HAWKINS   | WI      | Rusk    |     300 |
|   54011 | ELLSWORTH | WI      | Pierce  |     200 |

Candidate column headers:
Zip Code
City
Free Delivery
County
State



headers: Zip Code
City
Free Delivery
County
State
label: Zip Code
City
State
County
Free Delivery
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0   |   Col_1 | Col_2   | Col_3   | Col_4   | Col_5   | Col_6   | Col_7   | Col_8   | Col_9   | Col_10   | Col_11   | Col_12   |
|:--------|--------:|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:--------|:---------|:---------|:---------|
| SWE     |      20 | INA     | ISR     | IRL     | POL     | FRA     | NED     | POR     | ARG     | ARG      | CAN      | BRA      |
| IRL     |      10 | FRA     | POL     | CAN     | BRA     | CHI     | CAN     | ITA     | GBR     | AUS      | RUS      | AUT      |
| ISR     |      32 | CHN     | GBR     | ISR     | JPN     | IND     | IRI     | GER     | GRE     | CAN      | NOR      | NOR      |
| HUN     |      40 | EGY     | NOR     | FIN     | CHI     | SWE     | CZE     | IND     | BRA     | POR      | SUI      | CAN      |
| RSA     |      39 | FRA     | JPN     | IRL     | NED     | CHN     | CHN     | JPN     | ITA     | CHN      | ISL      | ARG      |

Candidate column headers:
Bin05
Bin12
Seq
Bin03
Bin01
Bin07
Bin10
Bin11
Bin08
Bin02
Bin09
Bin04
Bin06



headers: Bin05
Bin12
Seq
Bin03
Bin01
Bin07
Bin10
Bin11
Bin08
Bin02
Bin09
Bin04
Bin06
label: Bin12
Seq
Bin01
Bin02
Bin03
Bin04
Bin05
Bin06
Bin07
Bin08
Bin09
Bin10
Bin11
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1                      |   Col_2 |   Col_3 |
|--------:|:---------------------------|--------:|--------:|
|      83 | RUSSELL CO PBLC SCHS       |    3504 | 72966.1 |
|     103 | BUENA VISTA CITY PBLC SCHS |     886 | 18449.8 |
|       1 | ACCOMACK CO PBLC SCHS      |    4797 | 99891.1 |
|      18 | CARROLL CO PBLC SCHS       |    3505 | 72986.9 |
|      40 | GREENSVILLE CO PBLC SCHS   |    2183 | 45458   |

Candidate column headers:
AVERAGE DAILY ATTENDANCE/ADA
SCHOOL DIVISION/LEA
2018-2019 TOTAL ALLOCATION
DIVISION NO



headers: AVERAGE DAILY ATTENDANCE/ADA
SCHOOL DIVISION/LEA
2018-2019 TOTAL ALLOCATION
DIVISION NO
label: DIVISION NO
SCHOOL DIVISION/LEA
AVERAGE DAILY ATTENDANCE/ADA
2018-2019 TOTAL ALLOCATION
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1     |   Col_2 |   Col_3 |
|--------:|:----------|--------:|--------:|
|       3 | Grapes    |      13 |   42765 |
|       7 | Pineapple |      29 |   42764 |
|       9 | Kiwi      |      37 |   42763 |
|       4 | Pineapple |      17 |   42764 |
|       8 | Pineapple |      33 |   42764 |

Candidate column headers:
Quantity
Fruit
Date
Customer ID



headers: Quantity
Fruit
Date
Customer ID
label: Customer ID
Fruit
Quantity
Date
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0                | Col_1      | Col_2    | Col_3   |   Col_4 |
|:---------------------|:-----------|:---------|:--------|--------:|
| CC-2350/5-2320/5-2   | Cut-Crease | Finished | 2pt     |      29 |
| CC-2340/10-2310/10-3 | Cut-Crease | Ground   | 3pt     |       0 |
| BW-L-23.80-2-42-24MM | Special    |          | 2pt     |      85 |
| CC-2350/10-2320/10-2 | Cut-Crease | Ground   | 2pt     |      12 |
| CC-2350/11-2330/11-2 | Cut-Crease | Ground   | 2pt     |      21 |

Candidate column headers:
Thickness
Product Type
Product Code 
Stock Qty
Product Description



headers: Thickness
Product Type
Product Code 
Stock Qty
Product Description
label: Product Code 
Product Type
Product Description
Thickness
Stock Qty
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0          | Col_1             |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |
|:---------------|:------------------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Germany        | SAMIHA TELLI      |      96 |       0 |     230 |     230 |       9 |     116 |      74 |
| Germany        | ROMAN PRICHOSCHIJ |      95 |       0 |     270 |     270 |      13 |     139 |      79 |
| United Kingdom | VIVEK SINGH       |      96 |       0 |     255 |     255 |       0 |     220 |     128 |
| Lithuania      | SERGEJ BUIVOLENKO |      90 |       2 |     612 |     222 |       0 |     159 |      96 |
| Germany        | ZIED ALAYA        |      89 |       2 |     615 |     225 |      17 |      76 |      43 |

Candidate column headers:
Name
Late_Minutes
Country
Attendance_Percentage
Wasted_Minutes
Sessions_Off
Early_Minutes_mornings
Late_Minutes_mornings
Early_Minutes



headers: Name
Late_Minutes
Country
Attendance_Percentage
Wasted_Minutes
Sessions_Off
Early_Minutes_mornings
Late_Minutes_mornings
Early_Minutes
label: Country
Name
Attendance_Percentage
Sessions_Off
Wasted_Minutes
Late_Minutes
Late_Minutes_mornings
Early_Minutes
Early_Minutes_mornings
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0        |   Col_1 | Col_2   |   Col_3 | Col_4   |   Col_5 |   Col_6 | Col_7   | Col_8   |   Col_9 |   Col_10 | Col_11   |   Col_12 | Col_13            | Col_14       |
|:-------------|--------:|:--------|--------:|:--------|--------:|--------:|:--------|:--------|--------:|---------:|:---------|---------:|:------------------|:-------------|
| G33A (2q4ht) |      75 | 0-1     |       0 | 4w      |      25 |      30 | 10      | a/d     |       5 |       86 | 3        |     0.1  | Wet Meadow        | Wet          |
| G12A (2q3tt) |      11 | 0-3     |       2 | 3s      |      43 |      31 | 10      | c       |       5 |       86 | 4L       |     0.24 | Saline Lowland    | Saline       |
| G29A (2q4hs) |      14 | 0-2     |       1 | 2s      |      70 |      34 | 2kk     | c       |       5 |       86 | 4L       |     0.24 | Limy Subirrigated | Subirrigated |
| G12A (2q3tt) |       7 | 0-1     |       0 | 4w      |      40 |      31 | 10      | c/d     |       5 |       48 | 6        |     0.32 | Wet Meadow        | Wet          |
| G25A (2q3tv) |      74 | 0-1     |       0 | 4w      |      30 |      33 | 10      | b/d     |       3 |       86 | 4L       |     0.28 | Wet Meadow        | Wet          |

Candidate column headers:
CTSG
Slope (%)
Ecological Site
Kw
Map Unit Symbol (National Symbol)
T
WEI
HSG
WEG
Composition (%)
Component PI
Slope RV (%)
Map Unit PI
FSG
LCC



headers: CTSG
Slope (%)
Ecological Site
Kw
Map Unit Symbol (National Symbol)
T
WEI
HSG
WEG
Composition (%)
Component PI
Slope RV (%)
Map Unit PI
FSG
LCC
label: Map Unit Symbol (National Symbol)
Composition (%)
Slope (%)
Slope RV (%)
LCC
Component PI
Map Unit PI
CTSG
HSG
T
WEI
WEG
Kw
Ecological Site
FSG
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0    |   Col_1 |   Col_2 |   Col_3 |   Col_4 |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 | Col_12   | Col_13         |
|:---------|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|:---------|:---------------|
| Wyoming  |     369 |     253 | 275.877 |      24 | 526.708 |      30 | 452.667 |      39 | 350.564 |       46 |  153.174 | 1.0      | 269.0          |
| Indiana  |    7906 |    5752 | 469.25  |     214 | 707.977 |    1563 | 863.155 |     404 | 620.418 |      187 |  173.439 |          |                |
| Other    |    4927 |    3589 | 414.038 |     306 | 399.817 |     589 | 524.553 |     690 | 823.355 |       56 |  185.482 | 3.0      | 493.0          |
| New York |    5334 |    3521 | 302.555 |     656 | 465.166 |     876 | 369.935 |     599 | 592.898 |      305 |  184.416 | 33.0     | 537.3636363636 |
| Hawaii   |     988 |     735 | 302.733 |      57 | 579.316 |     133 | 347.421 |      51 | 468.157 |       66 |  136.379 | 3.0      | 174.6666666667 |

Candidate column headers:
RMND_TB_READY_ADP
RMND_TB_READY_INVENTORY
FORM9_INVENTORY
APPEALS_INVENTORY
SOC_INVENTORY
MMWR_APP_STATE_COMP
RMND_AT_AMC_ADP
RMND_AT_RO_INVENTORY
NOD_ADP
RMND_AT_RO_ADP
NOD_INVENTORY
SOC_ADP
RMND_AT_AMC_INVENTORY
FORM9_ADP



headers: RMND_TB_READY_ADP
RMND_TB_READY_INVENTORY
FORM9_INVENTORY
APPEALS_INVENTORY
SOC_INVENTORY
MMWR_APP_STATE_COMP
RMND_AT_AMC_ADP
RMND_AT_RO_INVENTORY
NOD_ADP
RMND_AT_RO_ADP
NOD_INVENTORY
SOC_ADP
RMND_AT_AMC_INVENTORY
FORM9_ADP
label: MMWR_APP_STATE_COMP
APPEALS_INVENTORY
NOD_INVENTORY
NOD_ADP
SOC_INVENTORY
SOC_ADP
FORM9_INVENTORY
FORM9_ADP
RMND_AT_RO_INVENTORY
RMND_AT_RO_ADP
RMND_AT_AMC_INVENTORY
RMND_AT_AMC_ADP
RMND_TB_READY_INVENTORY
RMND_TB_READY_ADP
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0              | Col_1         | Col_2           | Col_3   | Col_4                       | Col_5        | Col_6       | Col_7   |   Col_8 | Col_9   | Col_10   |   Col_11 |   Col_12 | Col_13            | Col_14   |   Col_15 |
|:-------------------|:--------------|:----------------|:--------|:----------------------------|:-------------|:------------|:--------|--------:|:--------|:---------|---------:|---------:|:------------------|:---------|---------:|
| Integral LED Lamps | LED-PAR38-COB | Alite CO._ LTD. | ALITE   | Reflector (Flood/Spot)      | E26 (Medium) | Directional | LED     |       3 | 4104.0  | 38.0     |     1300 |       17 | 75.0              | No       |    25000 |
| Integral LED Lamps | LED-PAR20-COB | Alite CO._ LTD. | ALITE   | Reflector (Flood/Spot)      | E26 (Medium) | Directional | LED     |       3 | 1806.0  | 38.0     |      580 |        8 | 70.0              | No       |    25000 |
| BLA-11             | BLA-11        | Broada_ Inc.    | Broada  | General Purpose Replacement | E26 (Medium) |             | LED     |       3 |         |          |      800 |       11 |                   |          |    25000 |
| 1800920            | 1800920       | Broada_ Inc.    | Broada  | Reflector (Flood/Spot)      | E26 (Medium) | Directional | LED     |       3 |         | 107.0    |      550 |        7 | 78.59999999999998 | No       |    25000 |
| Integral LED Lamps | LED-PAR20-COB | Alite CO._ LTD. | ALITE   | Reflector (Flood/Spot)      | E26 (Medium) | Directional | LED     |       3 | 1665.0  | 38.0     |      580 |        8 | 71.3              | No       |    25000 |

Candidate column headers:
Efficacy (lm/W)
Three Way
Brightness (lm)
CBCP
Base Type
Brand Name
Product Finder Bulb Type
Warranty (years)
Model Name
Energy Used (Watts)
Life (hrs)
Light Bulb Category
Technology
Model Number
Beam Angle
ENERGY STAR Partner



headers: Efficacy (lm/W)
Three Way
Brightness (lm)
CBCP
Base Type
Brand Name
Product Finder Bulb Type
Warranty (years)
Model Name
Energy Used (Watts)
Life (hrs)
Light Bulb Category
Technology
Model Number
Beam Angle
ENERGY STAR Partner
label: Model Name
Model Number
ENERGY STAR Partner
Brand Name
Product Finder Bulb Type
Base Type
Light Bulb Category
Technology
Warranty (years)
CBCP
Beam Angle
Brightness (lm)
Energy Used (Watts)
Efficacy (lm/W)
Three Way
Life (hrs)
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0      | Col_1       | Col_2           | Col_3   | Col_4       |   Col_5 |   Col_6 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |   Col_12 | Col_13             | Col_14             |
|:-----------|:------------|:----------------|:--------|:------------|--------:|--------:|--------:|--------:|--------:|---------:|---------:|---------:|:-------------------|:-------------------|
| DPOW       | C8 Overflow | DPOWC8 Overflow | 2 DPW   | C8 Overflow |       0 |       0 |       0 |       0 |       0 |        0 |        0 |   0      |                    |                    |
| DPOW       | B4          | DPOWB4          | 01 DPW  | WARD B4     |      36 |       9 |       2 |       0 |       3 |        0 |       50 |  62      | 0.9                | 0.06               |
| GDH        | Daycases    | GDHDaycases     | 03 GDH  | Daycases    |     100 |       9 |       3 |       1 |       0 |        0 |      113 |  84.9558 | 0.9646017699115044 | 0.0088495575221238 |
| DPOW Total |             | DPOW Total      | 01 DPW  | 01 DPW      |     505 |      97 |      15 |       1 |       4 |        0 |      610 |  79.5082 | 0.9868852459016394 | 0.0081967213114754 |
| DPOW       | C1 Kendall  | DPOWC1 Kendall  | 01 DPW  | C1 KENDALL  |      14 |      10 |       0 |       0 |       0 |        0 |       24 |  58.3333 | 1.0                | 0.0                |

Candidate column headers:
5 - Extremely unlikely
Net Promoter Score By Ward
2 - Likely
In-patient  FFT score (% recommended)
4 - Unlikely
3 - Neither likely nor unlikely
Site
Ward name
Hospital Ward
Ward
Hospital
6 - Don't Know
Total Responses
In-patient  FFT score (% Not recommended)
1 - Extremely Likely



headers: 5 - Extremely unlikely
Net Promoter Score By Ward
2 - Likely
In-patient  FFT score (% recommended)
4 - Unlikely
3 - Neither likely nor unlikely
Site
Ward name
Hospital Ward
Ward
Hospital
6 - Don't Know
Total Responses
In-patient  FFT score (% Not recommended)
1 - Extremely Likely
label: Hospital
Ward
Hospital Ward
Site
Ward name
1 - Extremely Likely
2 - Likely
3 - Neither likely nor unlikely
4 - Unlikely
5 - Extremely unlikely
6 - Don't Know
Total Responses
Net Promoter Score By Ward
In-patient  FFT score (% recommended)
In-patient  FFT score (% Not recommended)
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0         | Col_1   |   Col_2 |   Col_3 |   Col_4 |       Col_5 |       Col_6 |
|:--------------|:--------|--------:|--------:|--------:|------------:|------------:|
| PF3D7_0508500 | RCC1    | 1.49151 | 11.4543 | 57.3824 | 3.5881e-14  | 3.59967e-12 |
| PF3D7_0717500 | CDPK4   | 1.72499 | 11.3368 | 64.9804 | 7.56452e-16 | 8.71321e-14 |
| PF3D7_0722200 | RALP1   | 1.69942 | 11.6172 | 90.632  | 1.73042e-21 | 3.84401e-19 |
| PF3D7_0932100 | MAM3    | 1.32312 | 11.3409 | 37.0577 | 1.14687e-09 | 5.32354e-08 |
| PF3D7_0802600 | ACbeta  | 1.44603 | 11.3912 | 48.902  | 2.69078e-12 | 2.09208e-10 |

Candidate column headers:
FDR
Gene_name
logCPM
logFC
LR
PValue
Gene



headers: FDR
Gene_name
logCPM
logFC
LR
PValue
Gene
label: Gene
Gene_name
logFC
logCPM
LR
PValue
FDR
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 | Col_1   | Col_2   | Col_3        |
|--------:|:--------|:--------|:-------------|
|   42800 | A_ B_ D | Open    | Anita_ Kirk  |
|   42793 | All     | Closed  | UVA          |
|   42802 | All     | Open    | Matt         |
|   42794 |         | Closed  | Alan Higgins |
|   42800 | All     | Open    | CE/IT?       |

Candidate column headers:
Owner
Status
Date:
Room#



headers: Owner
Status
Date:
Room#
label: Date:
Room#
Status
Owner
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
| Col_0       |   Col_1 |   Col_2 |
|:------------|--------:|--------:|
| A.Tomlinson |      64 |    0.86 |
| H.Perryman  |      61 |    0.51 |
| T.Taranto   |      67 |    0.79 |
| J.Cameron   |      72 |    0.64 |
| D.Shiel     |     123 |    0.86 |

Candidate column headers:
SC Score
PLAYER NAME
Time On Ground %



headers: SC Score
PLAYER NAME
Time On Ground %
label: PLAYER NAME
SC Score
Time On Ground %
Task: header-value-matching (source dataset: TableGPT)

Given the input table data and the list of candidate headers, please determine the most suitable column header for each column in the table. Please only choose column headers from the candidate list. Please only return the most suitable column header for each column. Return the chosen column headers in a list. Do not return the entire table. No explanation and return your answer in JSON format: {"column_headers": "<a list of headers for each column chosen from the candidate list>"}.

Table Data:
|   Col_0 |   Col_1 | Col_2     |   Col_3 |   Col_4 |   Col_5 | Col_6   |
|--------:|--------:|:----------|--------:|--------:|--------:|:--------|
|    1002 |   40932 | Robinson  |       9 |    1828 |   16452 | COMP    |
|    1041 |   40983 | Maxi      |       6 |    1067 |    6402 | COMP    |
|    1036 |   41007 | Henderson |       7 |     579 |    4053 | AV      |
|    1032 |   41026 | Flanagan  |       1 |    2132 |    2132 | COMP    |
|    1029 |   40919 | Gulacsi   |       1 |    1437 |    1437 | AV      |

Candidate column headers:
Type
Unit_Cost
Date
Qty
Order
Salesman
Value



headers: Type
Unit_Cost
Date
Qty
Order
Salesman
Value
label: Order
Date
Salesman
Qty
Unit_Cost
Value
Type