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   |
|:-----------------------|:----------|:--------|:--------|--------:|:----------|:---------------|:--------|
| 1313 Red River Street  | Suite 100 | Austin  | Texas   |   78701 | Travis    | (512) 978-9610 |         |
| 1107 Wilcox/Po Box 670 |           | Anahuac | Texas   |   77514 | Chambers  | (409) 267-8356 |         |
| 408 N. Flournoy Road   | Suite C   | Alice   | Texas   |   78332 | Jim Wells | (361) 660-2285 |         |
| 2901 Montopolis Dr.    |           | Austin  | Texas   |   78741 | Travis    | (512) 978-9805 |         |
| 205 N. Cockrell        |           | Alpine  | Texas   |   79830 | Brewster  | (432) 837-3877 |         |

Candidate column headers:
Address2
Zip
City
Extension
Phone Number
County
State
Address1



headers: Address2
Zip
City
Extension
Phone Number
County
State
Address1
label: Address1
Address2
City
State
Zip
County
Phone Number
Extension
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   |
|:--------|:--------|:--------|:--------|:--------|:-------------------|:--------|:--------|--------:|:--------|:---------|:---------|
|         | OK      |         |         |         |                    |         |         |       0 |         |          |          |
| Yes     | OK      | G123    | ITEM G  | Cole    | Item G description | 10.0    | 50.0    |     500 | 50.0    | 14.0     | 50.0     |
|         | OK      |         |         |         |                    |         |         |       0 |         |          |          |
|         | OK      | B123    | ITEM B  | Cole    | Item B description | 20.0    | 100.0   |    2000 | 50.0    | 30.0     | 20.0     |
|         | OK      |         |         |         |                    |         |         |       0 |         |          |          |

Candidate column headers:
REORDER LEVEL
STOCK QUANTITY
NAME
COST PER ITEM
ITEM NO.
ITEM REORDER QUANTITY
ITEM DISCONTINUED?
DAYS PER REORDER
MANUFACTURER
DESCRIPTION
INVENTORY VALUE
REORDER (auto-fill)



headers: REORDER LEVEL
STOCK QUANTITY
NAME
COST PER ITEM
ITEM NO.
ITEM REORDER QUANTITY
ITEM DISCONTINUED?
DAYS PER REORDER
MANUFACTURER
DESCRIPTION
INVENTORY VALUE
REORDER (auto-fill)
label: ITEM DISCONTINUED?
REORDER (auto-fill)
ITEM NO.
NAME
MANUFACTURER
DESCRIPTION
COST PER ITEM
STOCK QUANTITY
INVENTORY VALUE
REORDER LEVEL
DAYS PER REORDER
ITEM REORDER QUANTITY
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 |
|--------:|:--------|--------:|
|    3000 | Rose    |       3 |
|    8000 | Mike    |       1 |
|    4000 | Paul    |       1 |
|    2000 | Mike    |       1 |
|    7000 | Jhon    |       3 |

Candidate column headers:
Order
Employee
Hrs



headers: Order
Employee
Hrs
label: Order
Employee
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 |
|--------:|:--------|:------------------------------|:--------|--------:|--------:|--------:|
|   41902 | James   | Helping brother with homework | Nice    |      65 |       2 |     130 |
|   41807 | James   | Weekly Chores 90%             | Nice    |      50 |       1 |      50 |
|   41849 | Kyle    | Helping brother with homework | Nice    |      65 |       2 |     130 |
|   41764 | Kyle    | Healthy Food                  | Nice    |      25 |       2 |      50 |
|   41825 | James   | Telling lies                  | Naughty |    -100 |       1 |    -100 |

Candidate column headers:
Activity
Activity Count
Points
Total
Name
Date
N/N



headers: Activity
Activity Count
Points
Total
Name
Date
N/N
label: Date
Name
Activity
N/N
Points
Activity Count
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 |   Col_7 |   Col_8 |   Col_9 |   Col_10 |   Col_11 |
|--------:|:----------|--------:|:--------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|---------:|
|   41871 | COPPER    |    1000 | SHORT   |   417   |   410   |     401 |   421   |   421   |       0 |        0 |    -4000 |
|   41872 | ALUMINIUM |    5000 | SHORT   |   125   |   124.2 |     123 |   125.7 |   124.2 |    4000 |        0 |     4000 |
|   41872 | CRUDEOIL  |     100 | LONG    |  5690   |  5730   |    5760 |  5660   |  5730   |    4000 |        0 |     4000 |
|   41870 | LEAD      |    5000 | SHORT   |   135.4 |   134.9 |     134 |   136   |   134.9 |    2500 |        0 |     2500 |
|   41871 | GOLD      |     100 | SHORT   | 28200   | 28000   |   27800 | 28390   | 27800   |   40000 |        0 |    40000 |

Candidate column headers:
10000
0
5700
100
57602
CRUDEOIL 
5860
100002
5760
5925
8/18/2014
SHORT



headers: 10000
0
5700
100
57602
CRUDEOIL 
5860
100002
5760
5925
8/18/2014
SHORT
label: 8/18/2014
CRUDEOIL 
100
SHORT
5860
5760
5700
5925
57602
10000
0
100002
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 |
|:----------------------|:----------|--------:|:--------|:--------|:-----------------------|:----------|:----------|:-----------|:--------------|---------:|
| Berkeley City College | 2009 Fall |       8 | 19-24   | M       | Unknown/Non Respondent | Full-time | Full-time | Transfer   | Matriculation |        2 |
| Berkeley City College | 2009 Fall |       8 | 35-54   | F       | Hispanic               | Full-time | Full-time | Unknown    | Matriculation |        1 |
| Berkeley City College | 2009 Fall |       8 | 55-64   | M       | White Non Hispanic     | Part-time | Part-time | Ed. Dvlp   | Exempt        |        1 |
| Berkeley City College | 2009 Fall |       8 | 55-64   | M       | Unknown/Non Respondent | Part-time | Full-time | AA/Trnsfer | Matriculation |        1 |
| Berkeley City College | 2009 Fall |       8 | 19-24   | M       | Unknown/Non Respondent | Full-time | Full-time | AA/Trnsfer | Matriculation |        1 |

Candidate column headers:
Headcount
Gender
Matric Goal
DSPS
Term
Fulltime District
College
Ethnicity
Fulltime Campus
Matric Status
Age Group



headers: Headcount
Gender
Matric Goal
DSPS
Term
Fulltime District
College
Ethnicity
Fulltime Campus
Matric Status
Age Group
label: College
Term
DSPS
Age Group
Gender
Ethnicity
Fulltime Campus
Fulltime District
Matric Goal
Matric Status
Headcount
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 |
|:------------|--------:|---------:|---------:|---------:|--------:|---------:|---------:|---------:|
| Spain       | 90.5114 | 0.508419 |  1.06795 | 0.116892 | 8.42063 | 0.480484 |  9.48858 | 0.508419 |
| Sweden      | 88.27   | 1.18406  |  7.98558 | 0.849255 | 3.74442 | 0.465496 | 11.73    | 1.18406  |
| Croatia     | 89.2578 | 0.608475 |  7.20241 | 0.454102 | 3.5398  | 0.322489 | 10.7422  | 0.608475 |
| Germany     | 82.3784 | 1.01284  | 11.7174  | 0.793033 | 5.90424 | 0.39336  | 17.6216  | 1.01284  |
| Switzerland | 76.4668 | 0.902053 | 15.1249  | 0.664029 | 8.4083  | 0.530282 | 23.5332  | 0.902053 |

Candidate column headers:
Column7
Column2
Column3
Column62
Column4
Column1
Column6
Column5
Column73



headers: Column7
Column2
Column3
Column62
Column4
Column1
Column6
Column5
Column73
label: Column1
Column2
Column3
Column4
Column5
Column6
Column7
Column62
Column73
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 |
|--------:|------------:|------------:|------------:|--------:|
|   40391 | 2.26367e+06 | 1.24502e+06 | 1.52798e+06 |  735692 |
|   40483 | 2.43677e+06 | 1.6434e+06  | 1.36006e+06 |  736697 |
|   39814 | 2.82713e+06 | 1.52665e+06 | 1.80936e+06 |  791596 |
|   40057 | 2.54442e+06 | 1.47011e+06 | 1.52665e+06 |  735053 |
|   40238 | 2.26367e+06 | 1.41479e+06 | 1.47138e+06 |  679100 |

Candidate column headers:
KOCE
KVCR
KCET
KLCS
Column1



headers: KOCE
KVCR
KCET
KLCS
Column1
label: Column1
KCET
KOCE
KLCS
KVCR
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 |
|--------:|--------:|--------:|--------:|
|   39995 |     8.3 |     2.7 |     5.7 |
|   40695 |     8.8 |     4   |     6.6 |
|   39387 |     3.8 |     1.4 |     2.7 |
|   40087 |     8.6 |     2.9 |     6   |
|   40513 |     8.8 |     3.6 |     6.4 |

Candidate column headers:
Column1
Female
Male
Total



headers: Column1
Female
Male
Total
label: Column1
Male
Female
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 |
|:--------|--------:|:-----------|--------:|
| ACTIVE  | 201     | 2ND ST W   |       2 |
| ACTIVE  | 205     | HOLLY LN   |       1 |
| ACTIVE  | 105     | ALLYN ST W |       1 |
| ACTIVE  | 607     | 2ND ST W   |       1 |
| ACTIVE  | 412.414 | MOTT ST N  |       2 |

Candidate column headers:
Units
House #
Street Name
Status



headers: Units
House #
Street Name
Status
label: Status
House #
Street Name
Units
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 |
|:------------|:--------|:-----------|--------:|:--------|:--------|--------:|--------:|----------:|----------:|
| Ullings     | Theo    | Klooster   |      27 | 6118 EL | Hei     |   28546 |      37 | 712518291 | 653716799 |
| Janssen     | Leon    | Vlasstraat |       7 | 6098 EL | Baexem  |   24106 |      51 | 664513339 | 653717207 |
| Verdonschot | Klaas   | Vlasstraat |      19 | 6110 EL | Neer    |   25216 |      47 | 676514577 | 653717105 |
| Janssen     | Klaas   | Park laan  |       8 | 6099 EL | Baexem  |   23387 |      52 | 580504673 | 653717921 |
| Putsdank    | Klaas   | Park       |      32 | 6123 EL | Weert   |   32431 |      27 | 754522624 | 653716442 |

Candidate column headers:
Naam
Nr.
Adres
V.n
leeftijd
Geb.
Plaats
Mobiel
Telefoon
Code



headers: Naam
Nr.
Adres
V.n
leeftijd
Geb.
Plaats
Mobiel
Telefoon
Code
label: Naam
V.n
Adres
Nr.
Code
Plaats
Geb.
leeftijd
Telefoon
Mobiel
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 |
|--------:|--------:|:----------------|:----------------|:---------|----------:|----------:|--------:|--------:|--------:|---------:|---------:|---------:|---------:|---------:|-----------:|
|       0 |       0 | Heather Walters | Ashley Sommer   | <2 days  |      0.36 |      0.62 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |      0.49  |
|       0 |       0 | Lisa Angello    | Margaret Warner | <30 days |     17.3  |     18.4  |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |     17.85  |
|       0 |       0 |                 |                 | 14 days  |     16.28 |     15.43 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |     15.855 |
|       0 |       0 | Lisa Angello    | Margaret Warner |          | 134325    | 128261    |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 | 262586     |
|       0 |       0 | Heather Walters | Wanda Mateo     | 30 sec   |      7.48 |     13.16 |       0 |       0 |       0 |        0 |        0 |        0 |        0 |        0 |      9.73  |

Candidate column headers:
Mar
BackUp
Feb
Sep
Jan
Apr
Aug
Oct
Nov
Target
May
Dec
Jun
YTD
Jul
Responsible



headers: Mar
BackUp
Feb
Sep
Jan
Apr
Aug
Oct
Nov
Target
May
Dec
Jun
YTD
Jul
Responsible
label: Nov
Dec
Responsible
BackUp
Target
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
YTD
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 |
|-----------------:|-----------------:|-----------------:|-----------------:|-----------------:|--------:|--------:|--------:|--------:|--------:|
| 295655           |      2.20382e+06 | 550954           |      1.65286e+06 | 413216           | 34434.6 |   34434 |   34434 |   34434 |   34434 |
|      3.6759e+06  | 462214           | 115554           | 346661           |  86665.2         |  7222.1 |    7222 |    7222 |    7222 |    7222 |
|      3.67661e+06 |      3.53534e+06 | 883835           |      2.65151e+06 | 662876           | 55239.7 |   55239 |   55239 |   55239 |   55239 |
|      3.6752e+06  |      2.04758e+06 | 511894           |      1.53568e+06 | 383921           | 31993.4 |   31993 |   31993 |   31993 |   31993 |
| 482897           |      5.531e+06   |      1.38275e+06 |      4.14825e+06 |      1.03706e+06 | 86421.8 |   86421 |   86421 |   86421 |   86421 |

Candidate column headers:
September 2016 Payment
ProviderID
November 2016 Payment
Stage 1 and 2 Target (25%)
UPP Carve-out (25%)
Adjusted DY5 Target (Less UPP Carve-out)
DY5 Target Funding
Monthly Payment Advance (25% / 12 = 2.0833%)
August 2016 Payment
July 2016 Payment



headers: September 2016 Payment
ProviderID
November 2016 Payment
Stage 1 and 2 Target (25%)
UPP Carve-out (25%)
Adjusted DY5 Target (Less UPP Carve-out)
DY5 Target Funding
Monthly Payment Advance (25% / 12 = 2.0833%)
August 2016 Payment
July 2016 Payment
label: ProviderID
DY5 Target Funding
UPP Carve-out (25%)
Adjusted DY5 Target (Less UPP Carve-out)
Stage 1 and 2 Target (25%)
Monthly Payment Advance (25% / 12 = 2.0833%)
July 2016 Payment
August 2016 Payment
September 2016 Payment
November 2016 Payment
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   |
|--------:|:-------------|--------:|:---------------|--------:|--------:|:-----------|--------:|:--------|--------:|:---------|
|    2012 | Second Round |       7 | Gonzaga        |      66 |       2 | Ohio State |      73 | Loser   |       2 | Lower    |
|    2012 | First Round  |       2 | Duke           |      70 |      15 | Lehigh     |      75 | Loser   |      15 | Lower    |
|    2012 | Elite Eight  |       1 | North Carolina |      67 |       2 | Kansas     |      80 | Loser   |       2 | Lower    |
|    2012 | First Round  |       4 | Louisville     |      69 |      13 | Davidson   |      62 | Winner  |       4 | Higher   |
|    2012 | Sweet 16     |       3 | Marquette      |      58 |       7 | Florida    |      68 | Loser   |       7 | Lower    |

Candidate column headers:
Visit Team Seed
Home Team
Winning Seed
Home Team Seed
Winning Seed Relative
Visit Team
Home Team Score
Year
Result
Round
Visit Team Score



headers: Visit Team Seed
Home Team
Winning Seed
Home Team Seed
Winning Seed Relative
Visit Team
Home Team Score
Year
Result
Round
Visit Team Score
label: Year
Round
Home Team Seed
Home Team
Home Team Score
Visit Team Seed
Visit Team
Visit Team Score
Result
Winning Seed
Winning Seed Relative
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   |
|--------:|:---------------|:--------------|:----------|:-----------|--------:|--------:|:--------|
|      11 | C001B001C21593 |               | HISAR     | HARYANA    |  125005 |    1662 | Y       |
|       8 | C001B004C0334  |               | AMRITSAR  | PUNJAB     |  143001 |     183 | Y       |
|      15 | C001B016C0109  | THIRUVOTTIYUR | CHENNAI   | TAMIL NADU |  600019 |      44 | Y       |
|      49 | C001B007C0857  |               | AHMEDABAD | GUJARAT    |  380009 |      79 | Y       |
|      27 | C001B001C22308 |               | HISAR     | HARYANA    |  125001 |    1662 | Y       |

Candidate column headers:
State
Std. Code
City
Live
Sno
Hospital Code
Address2
Pincode



headers: State
Std. Code
City
Live
Sno
Hospital Code
Address2
Pincode
label: Sno
Hospital Code
Address2
City
State
Pincode
Std. Code
Live
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 |   Col_18 |   Col_19 |
|--------:|:---------|:--------|:-----------|--------:|--------:|--------:|--------:|--------:|--------:|---------:|:---------|---------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
|    2009 | Denver   | -0.23   | ALL Payers |    1849 |     406 |     499 |     122 |     482 |      77 |      386 | #        |     1950 |      830 |     2159 |     3992 |     1731 |     1006 |     1555 |     3671 |
|    2013 | Colorado |         | ALL Payers |    3365 |     611 |     940 |     221 |    1017 |     128 |      668 | 31%      |     3618 |     1878 |     3672 |     5385 |     3075 |     2192 |     2656 |     4408 |
|    2009 | Conejos  | 0.1     | ALL Payers |    2070 |     384 |     586 |     123 |     471 |     182 |      447 | #        |     2283 |     1085 |     3111 |     5012 |     1816 |     1274 |     2201 |     3530 |
|    2009 | Hinsdale | 0.22    | ALL Payers |    1936 |     386 |     678 |     211 |     496 |      30 |      345 | #        |     1707 |      286 |      140 |     3919 |     2168 |     1475 |      513 |     3227 |
|    2009 | Baca     | -0.36   | ALL Payers |    1935 |     310 |     643 |      91 |     383 |     108 |      491 | #        |     1661 |      555 |     2108 |     3533 |     2209 |     2644 |      968 |     2235 |

Candidate column headers:
M 35-64 Cost
% Generic Rx Cost
M 0-17 Cost
Payer Type
M 35-64 C2E
County
Total Cost of Care
F 0-17 Cost
Year
Rx Cost
M 18-34 Cost
Inpatient Facility Cost
F 18-34 Cost
Physician Cost
Outpatient Facility Cost
F 35-64 Cost
Ancillary Cost
ER Facility Cost (Subset of Outpatient Cost)
Male Cost
Female Cost



headers: M 35-64 Cost
% Generic Rx Cost
M 0-17 Cost
Payer Type
M 35-64 C2E
County
Total Cost of Care
F 0-17 Cost
Year
Rx Cost
M 18-34 Cost
Inpatient Facility Cost
F 18-34 Cost
Physician Cost
Outpatient Facility Cost
F 35-64 Cost
Ancillary Cost
ER Facility Cost (Subset of Outpatient Cost)
Male Cost
Female Cost
label: Year
County
M 35-64 C2E
Payer Type
Total Cost of Care
Inpatient Facility Cost
Outpatient Facility Cost
ER Facility Cost (Subset of Outpatient Cost)
Physician Cost
Ancillary Cost
Rx Cost
% Generic Rx Cost
Female Cost
F 0-17 Cost
F 18-34 Cost
F 35-64 Cost
Male Cost
M 0-17 Cost
M 18-34 Cost
M 35-64 Cost
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   |
|--------:|--------:|:--------|--------:|:-----------------|--------:|:---------------------|--------:|--------:|:---------|:---------------------------|:---------|:---------|
|   43278 |    1267 | Success |   20607 | 06230/0015/0/000 |       1 | Improvement Required |   43108 |     790 | Stirling | http://www.stirling.gov.uk | FHIS     | False    |
|   43278 |    1267 | Success |  133742 | 66666/0027/6/666 |    4613 | Pass                 |   42347 |     790 | Stirling | http://www.stirling.gov.uk | FHIS     | False    |
|   43278 |    1267 | Success |   28377 | 08920/2000/4/000 |    7843 | Pass                 |   41583 |     790 | Stirling | http://www.stirling.gov.uk | FHIS     | False    |
|   43278 |    1267 | Success |   22662 | 07/00264/CHUC    |    7841 | Pass                 |   41771 |     790 | Stirling | http://www.stirling.gov.uk | FHIS     | False    |
|   43278 |    1267 | Success |    1581 | 00290/0028/0/000 |       1 | Pass                 |   42739 |     790 | Stirling | http://www.stirling.gov.uk | FHIS     | False    |

Candidate column headers:
RatingDate
ItemCount
LocalAuthorityCode
LocalAuthorityWebSite
NewRatingPending
SchemeType
ExtractDate
LocalAuthorityName
ReturnCode
FHRSID
RatingValue
LocalAuthorityBusinessID
BusinessTypeID



headers: RatingDate
ItemCount
LocalAuthorityCode
LocalAuthorityWebSite
NewRatingPending
SchemeType
ExtractDate
LocalAuthorityName
ReturnCode
FHRSID
RatingValue
LocalAuthorityBusinessID
BusinessTypeID
label: ExtractDate
ItemCount
ReturnCode
FHRSID
LocalAuthorityBusinessID
BusinessTypeID
RatingValue
RatingDate
LocalAuthorityCode
LocalAuthorityName
LocalAuthorityWebSite
SchemeType
NewRatingPending
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 |
|:----------------|:--------|--------:|--------:|--------:|--------:|--------:|---------:|--------:|
| Vietnam         | VNM     | 79.6979 | 2313.3  | 87.7818 | 98.1459 | 87.1951 |  66.5174 | 58.8493 |
| Costa Rica      | CRI     | 89.0679 | 9672.83 | 76.9684 | 97.7423 | 90.1017 |  90.2275 | 90.2994 |
| Thailand        | THA     | 85.6846 | 6954.42 | 76.6771 | 85.5951 | 94.2595 |  79.5602 | 92.3312 |
| Tunisia         | TUN     | 88.3344 | 7492.73 | 92.8013 | 86.4459 | 81.4309 |  89.37   | 91.6237 |
| Kyrgyz Republic | KGZ     | 93.2015 | 1755.78 | 99.7566 | 81.3106 | 96.5912 | 100      | 88.349  |

Candidate column headers:
Country
GDP per capita (2005 PPP$) in 2006
Core Country Right to Housing Index Value 2006
Core Country Right to Education Index Value 2006
Core Country Right to Health Index Value 2006
Core Country Right to Food Index Value 2006
Core Country  Right to Work Index Value 2006
Core Country SERF Index Value 2006
Country Code



headers: Country
GDP per capita (2005 PPP$) in 2006
Core Country Right to Housing Index Value 2006
Core Country Right to Education Index Value 2006
Core Country Right to Health Index Value 2006
Core Country Right to Food Index Value 2006
Core Country  Right to Work Index Value 2006
Core Country SERF Index Value 2006
Country Code
label: Country
Country Code
Core Country SERF Index Value 2006
GDP per capita (2005 PPP$) in 2006
Core Country Right to Education Index Value 2006
Core Country Right to Health Index Value 2006
Core Country Right to Housing Index Value 2006
Core Country Right to Food Index Value 2006
Core Country  Right to Work Index Value 2006
Task: Arithmetic-Relationship (source dataset: Auto-Relate)

You are given a table with numerical values in multiple columns. Your task is to analyze the data and identify semantic relationships, expressed in arithmetic relationships, that exist between columns.

These relationships may involve any combination of the following operations: addition (+), subtraction (−), multiplication (*), and division (/). The expressions can involve two or more columns and may be nested (e.g., B = (A + C) / D).

Please only identify arithmetic relationships that hold semantically based on the data, and do not return relationships that are likely spurious, e.g., (A = B * 0), where A is an all 0 column. Please only return minimal atomic relationships, e.g., if A = B + C and D = E + F, then there is no need to return A = B + C + E + F - D.

No explanation, return all the formula(s) in JSON format: {"Arithmetic-Relationship": [<list-of-formulas>]}. If no relationship is found, return {"Arithmetic-Relationship": []}.

Input Table:
|   A |   B |    C |        D |
|----:|----:|-----:|---------:|
|   1 | 5.6 |  9.3 | 0.602151 |
|   2 | 6   | 12.3 | 0.487805 |
|   3 | 5   | 11.6 | 0.431034 |
|   4 | 4.2 | 12   | 0.35     |
|   5 | 3.9 | 14.1 | 0.276596 |
|   6 | 5.1 | 10.6 | 0.481132 |
|   7 | 6.4 | 11.2 | 0.571429 |
|   8 | 4.9 | 13.7 | 0.357664 |
|   9 | 6.5 | 12.4 | 0.524194 |
|  10 | 5.4 | 13.6 | 0.397059 |
|  11 | 3.2 | 10.4 | 0.307692 |
|  12 | 4.7 |  8.9 | 0.52809  |
|  13 | 5.5 | 10   | 0.55     |
|  14 | 5.9 | 14.3 | 0.412587 |
|  15 | 6.7 | 13.1 | 0.51145  |
|  16 | 2.8 |  8.9 | 0.314607 |
|  17 | 3.7 |  9.7 | 0.381443 |
|  18 | 6.1 | 11.5 | 0.530435 |
|  19 | 4.7 | 11.8 | 0.398305 |
|  20 | 4.2 | 12.4 | 0.33871  |
|  21 | 4.5 | 14.5 | 0.310345 |
|  22 | 3.8 |  7.9 | 0.481013 |
|  23 | 1.8 | 12.6 | 0.142857 |
|  24 | 5.4 | 11   | 0.490909 |
|  25 | 4.1 | 14.9 | 0.275168 |
|  26 | 3.3 | 12.2 | 0.270492 |
|  27 | 3.9 |  9.7 | 0.402062 |
|  28 | 5   | 12.8 | 0.390625 |
|  29 | 4.8 | 10.5 | 0.457143 |
|  30 | 4.8 | 10.3 | 0.466019 |
|  31 | 3.7 |  8.6 | 0.430233 |
|  32 | 6.2 | 11.1 | 0.558559 |
|  33 | 5.1 | 12.7 | 0.401575 |
|  34 | 2.8 | 11.3 | 0.247788 |
|  35 | 3.7 | 13.2 | 0.280303 |
|  36 | 3   | 12.3 | 0.243902 |
|  37 | 4.2 | 11.7 | 0.358974 |
|  38 | 2.2 | 11.1 | 0.198198 |
|  39 | 5.1 | 14.3 | 0.356643 |
|  40 | 4.8 | 13.7 | 0.350365 |
|  41 | 5.9 | 11.9 | 0.495798 |
|  42 | 1.3 | 11.2 | 0.116071 |
|  43 | 4.5 | 13.9 | 0.323741 |
|  44 | 3.7 |  9.2 | 0.402174 |
|  45 | 4.5 | 10.4 | 0.432692 |
|  46 | 5.2 | 12.8 | 0.40625  |
|  47 | 6.9 | 11.7 | 0.589744 |
|  48 | 3.7 | 11.2 | 0.330357 |
|  49 | 2.5 | 11.5 | 0.217391 |
|  50 | 4   | 14.3 | 0.27972  |



label: D=B/C
labeled_formulas: D=B/C
output: {"Arithmetic-Relationship": ["D=B/C"]}
source: $MMTU_HOME/resources/Relationship-finding/AR/table_261684___xlsx_8c6a7e62616647779ca46f6894a539c1cf0521c5.xlsx___line_507
Task: Column-type-annotation (source dataset: SemTab2019)

Your task is to annotate the target entity column in the input table with the DBPedia ontology that can most accurately describe all entities in the column (i.e. use the most fine grained ontology class name that can describe all entities in the column).

No explanation, return the ontology class in JSON format: {"DBpedia ontology class": "<DBpedia ontology class>"}. Please note that DBpedia ontology classes are in the format of "http://dbpedia.org/ontology/<class_name>".

Input Table:
| col0                                            | col1                           | col2             |     col3 |
|:------------------------------------------------|:-------------------------------|:-----------------|---------:|
| Beaver Creek Bridge                             | Texas                          | T. H. Department | 96001104 |
| Balsam Lake Mountain Fire Observation Station   | New York                       | A. Corporation   |  1001038 |
| Bailey Colony Farm                              | Palmer, Alaska                 | D. Williams      | 91000775 |
| B Reactor                                       | Richland, Washington           | DuPont           | 92000245 |
| August and Vera Luedtke Barn                    | Fairfield, Iowa                | L. M. Company    | 99000121 |
| Atka B-24D Liberator                            | Atka, Alaska                   | C. Aircraft      | 79000407 |
| Atalaya and Brookgreen Gardens                  | Murrells Inlet, South Carolina | A. H. Huntington | 84002045 |
| Atalaya Castle                                  | Murrells Inlet, South Carolina | A. M. Huntington | 84002045 |
| Arden                                           | Newburgh                       | C. a. Hastings   | 66000561 |
| Arbor Lodge State Historical Park and Arboretum | Nebraska City, Nebraska        | F. L. Olmsted    | 69000135 |

Target entity column: col0



col_id: 0
label: http://dbpedia.org/ontology/Building
okay_annotation: http://dbpedia.org/ontology/ArchitecturalStructure http://dbpedia.org/ontology/Place http://dbpedia.org/ontology/Location
csv: v18_744
col_name: col0
case_id: 1627
Task: Column-type-annotation (source dataset: SemTab2019)

Your task is to annotate the target entity column in the input table with the DBPedia ontology that can most accurately describe all entities in the column (i.e. use the most fine grained ontology class name that can describe all entities in the column).

No explanation, return the ontology class in JSON format: {"DBpedia ontology class": "<DBpedia ontology class>"}. Please note that DBpedia ontology classes are in the format of "http://dbpedia.org/ontology/<class_name>".

Input Table:
| col0           | col1          | col2                    | col3                                 |
|:---------------|:--------------|:------------------------|:-------------------------------------|
| S. Frangén     | R. Meriläinen | Finnish people          | Simo Juhani Frangén                  |
| R. Pryor       | D. McGuire    | Americans               | Richard Franklin Lennox Thomas Pryor |
| P. Oldring     | S. Erikson    | Canadian                | Peter James Edward Oldring           |
| P. Merton      | C. Quentin    | British people          | Paul James Martin                    |
| M. Kasher      | N. Leggero    | United States           | Mark Moshe Kasher                    |
| M. Sahl        | C. Lee        | Americans               | Morton Lyon Sahl                     |
| M. Mira        | D. Shafrir    | United States           | Matthew Paul Mira                    |
| M. Mull        | W. Haas       | United States           | Martin Eugene Mull                   |
| J. Saunders    | A. Edmondson  | England                 | Jennifer Jane Saunders               |
| H. Lloyd       | M. Davis      | United States           | Harold Clayton Lloyd                 |
| D. Harron      | V. Leith      | Canadians               | Donald Hugh Harron                   |
| C. Mochrie     | D. McGrath    | Canadian                | Colin Andrew Mochrie                 |
| C. Reiner      | E. Reiner     | Americans               | CarltonReiner                        |
| B. Connolly    | P. Stephenson | Scottish people         | William Connolly                     |
| A. Buenafuente | S. Abril      | Spain                   | Andreu Buenafuente Moreno            |
| A. Edmondson   | J. Saunders   | British nationality law | Adrian Charles Edmondson             |

Target entity column: col2



col_id: 2
label: http://dbpedia.org/ontology/EthnicGroup
okay_annotation: nan
csv: v16_66
col_name: col2
case_id: 529
Task: Column-type-annotation (source dataset: SemTab2019)

Your task is to annotate the target entity column in the input table with the DBPedia ontology that can most accurately describe all entities in the column (i.e. use the most fine grained ontology class name that can describe all entities in the column).

No explanation, return the ontology class in JSON format: {"DBpedia ontology class": "<DBpedia ontology class>"}. Please note that DBpedia ontology classes are in the format of "http://dbpedia.org/ontology/<class_name>".

Input Table:
| col0                       | col1                       | col2                       | col3                       | col4                       |
|:---------------------------|:---------------------------|:---------------------------|:---------------------------|:---------------------------|
| Castle of Alvor            | Faro                       | Faro                       | Faro                       | Faro                       |
| Castle of Alva             | Bragança                   | Bragança                   | Bragança                   | Bragança                   |
| Castle of Almourol         | Lisbon                     | Lisbon                     | Lisbon                     | Lisbon                     |
| Castle of Aljezur          | Faro                       | Faro                       | Faro                       | Faro                       |
| Castle of Algoso           | Bragança                   | Bragança                   | Bragança                   | Bragança                   |
| Castle of Alcoutim         | Faro                       | Faro                       | Faro                       | Faro                       |
| Castle of Alcoutim         | Faro                       | Faro                       | Faro                       | Faro                       |
| Castle Fortress of Almeida | Guarda                     | Guarda                     | Guarda                     | Guarda                     |
| Castle Balfour             | County Fermanagh           | County Fermanagh           | County Fermanagh           | County Fermanagh           |
| Bezděz Castle              | Česká Lípa District        | Česká Lípa District        | Česká Lípa District        | Česká Lípa District        |
| Altenburg                  | Bamberg                    | Bamberg                    | Bamberg                    | Bamberg                    |
| Špilberk Castle            | Brno                       | Brno                       | Brno                       | Brno                       |
| Červená Lhota Castle       | Jindřichův Hradec District | Jindřichův Hradec District | Jindřichův Hradec District | Jindřichův Hradec District |

Target entity column: col4



col_id: 4
label: http://dbpedia.org/ontology/Settlement
okay_annotation: http://dbpedia.org/ontology/PopulatedPlace http://dbpedia.org/ontology/Place http://dbpedia.org/ontology/Location
csv: v15_728
col_name: col4
case_id: 413
Task: Column-type-annotation (source dataset: SemTab2019)

Your task is to annotate the target entity column in the input table with the DBPedia ontology that can most accurately describe all entities in the column (i.e. use the most fine grained ontology class name that can describe all entities in the column).

No explanation, return the ontology class in JSON format: {"DBpedia ontology class": "<DBpedia ontology class>"}. Please note that DBpedia ontology classes are in the format of "http://dbpedia.org/ontology/<class_name>".

Input Table:
| col0                                | col1                           | col2       | col3       |
|:------------------------------------|:-------------------------------|:-----------|:-----------|
| S. W. Walker                        | D. Davis                       | 1879-11-09 | 1814-09-04 |
| R. Wilson                           | N. a. birth                    | 2005-07-15 | 1922-08-23 |
| R. Freisler                         | M. Freisler                    | 1945-02-03 | 1893-10-30 |
| P. Morton                           | Sarah Wentworth Apthorp Morton | 1837-10-14 | 1751-11-13 |
| J. N. Mitchell                      | M. B. Mitchell                 | 1988-11-09 | 1913-09-15 |
| J. Iqbal                            | N. Iqbal                       | 2015-10-03 | 1924-10-05 |
| Gordon Slynn, Baron Slynn of Hadley | O. Slynn                       | 2009-04-07 | 1930-02-17 |
| E. Fadeley                          | N. Fadeley                     | 2015-08-30 | 1929-12-13 |
| E. Coke                             | E. Hatton                      | 1634-09-03 | 1552-02-01 |
| D. Ward                             | A. Ward                        | 1913-08-30 | 1827-07-09 |
| C. Caesar                           | E. Barkham                     | 1642-12-06 | 1590-02-27 |
| C. D. Glassman                      | H. P. Glassman                 | 2013-07-10 | 1922-9-13  |
| B. O'Keefe                          | N. a. birth                    | 2014-04-24 | 1933-05-20 |

Target entity column: col1



col_id: 1
label: http://dbpedia.org/ontology/Person
okay_annotation: nan
csv: v17_895
col_name: col1
case_id: 1341
Task: Column-type-annotation (source dataset: SemTab2019)

Your task is to annotate the target entity column in the input table with the DBPedia ontology that can most accurately describe all entities in the column (i.e. use the most fine grained ontology class name that can describe all entities in the column).

No explanation, return the ontology class in JSON format: {"DBpedia ontology class": "<DBpedia ontology class>"}. Please note that DBpedia ontology classes are in the format of "http://dbpedia.org/ontology/<class_name>".

Input Table:
| col0                          | col1         | col2          |   col3 |
|:------------------------------|:-------------|:--------------|-------:|
| A Case of Deadly Force        | L. O'Donnell | P. Chihara    |   5700 |
| A Bright Shining Lie          | T. George    | G. Chang      |   7080 |
| 3-2-1 Penguins!               | M. Nawrocki  | M. Nawrocki   |  -1800 |
| 26 Men                        | S. Nibley    | R. Hayden     |   1500 |
| 24: Redemption                | H. Gordon    | S. Callery    |   5340 |
| 240-Robert                    | A. Yerkovich | M. Post       |   2880 |
| 2000 Year Old Man             | M. Brooks    | M. Garson     |   1800 |
| 19-2                          | R. Bossé     | N. Maranda    |   2520 |
| 1864                          | O. Bornedal  | M. Beltrami   |   3600 |
| 12:01                         | R. Lupoff    | P. R. Melnick |   5520 |
| 101 Dalmatians: The Series    | J. Jinkins   | Various       |   1320 |
| 10.5                          | J. Lafia     | L. Holdridge  |   9900 |
| 10.5: Apocalypse              | J. Lafia     | H. Lohner     |  10140 |
| 10,000 Black Men Named George | C. Nowrasteh | S. J. Taylor  |   5700 |

Target entity column: col1



col_id: 1
label: http://dbpedia.org/ontology/Person
okay_annotation: nan
csv: v15_387
col_name: col1
case_id: 254
Task: Column-type-annotation (source dataset: SemTab2019)

Your task is to annotate the target entity column in the input table with the DBPedia ontology that can most accurately describe all entities in the column (i.e. use the most fine grained ontology class name that can describe all entities in the column).

No explanation, return the ontology class in JSON format: {"DBpedia ontology class": "<DBpedia ontology class>"}. Please note that DBpedia ontology classes are in the format of "http://dbpedia.org/ontology/<class_name>".

Input Table:
| col0           | col1                      |   col2 | col3       |
|:---------------|:--------------------------|-------:|:-----------|
| E. Hinton      | California                |   1958 | 1958-10-12 |
| D. Douglas     | Zachary, Louisiana        |   2015 | 2015-01-01 |
| D. Gray        | United Kingdom            |   1978 | 1978-04-07 |
| D. Brooks      | Bridgehampton, New York   |   2005 | 2005-08-01 |
| D. Keefer      | Sherman Oaks, California  |   2014 | 2014-09-07 |
| D. Estelle     | New Zealand               |   2003 | 2003-08-02 |
| D. Durant      | California                |   2005 | 2005-03-15 |
| D. Carter      | Beverly Hills, California |   2010 | 2010-04-10 |
| Divine         | California                |   1988 | 1988-03-07 |
| D. quintuplets | Canada                    |   2001 | 2001-06-23 |
| D. Thakur      | Mumbai                    |   2012 | 2012-09-20 |
| D. Dors        | Berkshire                 |   1984 | 1984-05-04 |
| D. Anand       | Maharashtra               |   2011 | 2011-12-03 |
| D. Jarman      | Kent                      |   1994 | 1994-02-19 |
| D. Patrick     | Hollywood, California     |   2002 | 2002-10-13 |
| D. Biswas      | Kolkata                   |   1980 | 1980-08-18 |
| D. Kelley      | Los Angeles               |   1999 | 1999-06-11 |
| D. Kelly       | Dublin                    |   2012 | 2012-02-12 |
| D. Ward        | Montana                   |   2016 | 2016-08-28 |
| D. L. Rue      | United Kingdom            |   2009 | 2009-05-31 |
| Dalida         | Montmartre                |   1987 | 1987-05-03 |
| C. Moore       | Calabasas, California     |   1999 | 1999-12-28 |

Target entity column: col0



col_id: 0
label: http://dbpedia.org/ontology/Person
okay_annotation: nan
csv: v17_544
col_name: col0
case_id: 1211