MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| column83     |
|:-------------|
| nov 10, 2021 |
| nov 12, 2021 |
| nov 11, 2021 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| region                           |
|:---------------------------------|
| central and southern asia        |
| eastern and south-eastern asia   |
| europe and northern america      |
| latin america and the caribbean  |
| northern africa and western asia |
| sub-saharan africa               |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| product   |
|:----------|
| superglue |
| fastcar   |
| rapidzoo  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| f2_user_id   |
|:-------------|
| nherta01     |
| mkaba001     |
| rruss517     |
| jchon001     |
| swagn504     |
| lbrock02     |
| skong501     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a   |
|:----|
| hr  |
| n/x |
| tmp |
| dpt |
| cld |
| wdr |
| wsp |
| p06 |
| p12 |
| q06 |
| q12 |
| t06 |
| t12 |
| poz |
| pos |
| typ |
| snw |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| نتیجه آزمایش تشخیصی رایت   |
|:---------------------------|
| 1/640                      |
| 1/80                       |
| 1/160                      |
| 1/320                      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| outcome                |
|:-----------------------|
| moderately worse       |
| slightly worse         |
| moderately improved    |
| slightly improved      |
| no change              |
| significantly worse    |
| significantly improved |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|   provider spoken - russian  |
|-----------------------------:|
|                        0.007 |
|                        0.002 |
|                        0.013 |
|                        0.019 |
|                        0.012 |
|                        0.001 |
|                        0.028 |
|                        0.098 |
|                        0.029 |
|                        0.005 |
|                        0.003 |
|                        0.081 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| hovedområde                                      |
|:-------------------------------------------------|
| naturvidenskabelige uddannelser                  |
| tekniske uddannelser                             |
| samfundsvidenskabelige og merkantile uddannelser |
| humanistiske uddannelser                         |
| sundhedsuddannelser                              |
| øvrige uddannelser                               |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| trans type   |
|:-------------|
| sys          |
| ach          |
| wir          |
| wth          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0                                 |
|:-------------------------------------------|
| energy star - holding cabinet - full size  |
| energy star - multi-tank conv - high temp  |
| energy star - multi-tank conv - low temp   |
| energy star - single tank conv - high temp |
| energy star fryer                          |
| energy star griddle                        |
| energy star oven - combo                   |
| energy star oven - convection              |
| energy star steamer - 3 pan                |
| energy star steamer - 4 pan                |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| sheet tab               |
|:------------------------|
| hardware                |
| software                |
| application development |
| hosting                 |
| travel                  |
| training                |
| other                   |
| optional products       |
| optional services       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| responsible  party   |
|:---------------------|
| ilt / teachers       |
| ilt                  |
| ethan / katie        |
| math plc             |
| jenn                 |
| ilt / math plc       |
| ethan/katie/ilt      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|   unknown % |
|------------:|
| 0           |
| 0.000673854 |
| 0.0023671   |
| 0.000956688 |
| 0.00194398  |
| 0.0028348   |
| 0.0056338   |
| 8.15927e-05 |
| 0.000259235 |
| 0.0579978   |
| 0.124548    |
| 0.0736842   |
| 0.0831555   |
| 0.00952381  |
| 0.000694927 |
| 0.000541419 |
| 0.00101317  |
| 0.000156116 |
| 0.000700525 |
| 0.00811688  |
| 0.00347499  |
| 0.00104384  |
| 0.0120615   |
| 0.00292241  |
| 0.00769823  |
| 0.00346821  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| domains in tweet   |
|:-------------------|
| twitter.com        |
| autchat.com        |
| youtube.com        |
| morizot.net        |
| wordpress.com      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|            q |
|-------------:|
| -0.0034225   |
| -0.00462363  |
| -0.00211436  |
| -0.00288281  |
|  0.00393818  |
|  0.00819122  |
|  0.00726591  |
|  0.00652175  |
|  0.00651394  |
|  0.0113076   |
|  0.0177541   |
|  0.018632    |
|  0.0180316   |
|  0.0230913   |
|  0.0339422   |
|  0.0319837   |
|  0.0437823   |
|  0.0361931   |
|  0.0328516   |
|  0.0210166   |
|  0.0187783   |
|  0.00486204  |
| -0.00167494  |
| -0.0019289   |
| -0.00497041  |
| -0.00807192  |
| -0.0131648   |
| -0.015694    |
| -0.0169231   |
| -0.00781189  |
| -0.00448147  |
| -0.00596455  |
| -0.0036526   |
| -0.00101564  |
|  0.00380718  |
|  0.0104046   |
|  0.00959264  |
|  0.0158269   |
|  0.0179181   |
|  0.0254922   |
|  0.022455    |
|  0.017404    |
|  0.0147019   |
|  0.012203    |
|  0.0101519   |
|  0.00647294  |
|  0.00210641  |
| -0.0137957   |
| -0.0163659   |
| -0.0269712   |
| -0.0553481   |
| -0.0737923   |
| -0.0829159   |
| -0.0848892   |
| -0.0776398   |
| -0.0778162   |
| -0.0702411   |
| -0.0662628   |
| -0.0614787   |
| -0.0637618   |
| -0.055129    |
| -0.0539151   |
| -0.0461886   |
| -0.0375741   |
| -0.0345466   |
| -0.0332006   |
| -0.0322633   |
| -0.0246433   |
| -0.0262774   |
| -0.0196557   |
| -0.0136893   |
| -0.0227246   |
| -0.0138931   |
| -0.00757651  |
| -0.0110865   |
| -0.013441    |
| -0.0102813   |
| -0.0135357   |
| -0.0231247   |
| -0.0276148   |
| -0.0246947   |
| -0.0243801   |
| -0.0233888   |
| -0.0218316   |
| -0.0217078   |
| -0.0175538   |
| -0.0113528   |
| -0.00893273  |
| -0.0015678   |
| -0.000444321 |
| -0.00352916  |
| -0.00354957  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| place id         |
|:-----------------|
| 26b0db32cfda0432 |
| 94965b2c45386f87 |
| 0004c3fb58780d2a |
| 133e56cabbc75719 |
| b49b3053b5c25bf5 |
| 3bbb998ddd2b0308 |
| df4875cc2df893ed |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| certification body                      |
|:----------------------------------------|
| tüv rheinland                           |
| tüv süd                                 |
| 2nd cert: tüv rheinland                 |
| 1st cert:tüv süd2nd cert: tüv rheinland |
| cip                                     |
| tuv sud                                 |
| aja registrars                          |
| cipi                                    |
| xxx                                     |
| tuv rheinland                           |
| sgs                                     |
| certification international             |
| cipi (1st)aja registrars (2nd)          |
| cipi (1st)tüv rheinland (2nd & 3rd)     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| 2° to 8° c    |
|:--------------|
| yes           |
| no            |
| **            |
| yes*          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a                                         |
|:------------------------------------------|
| commercial renovation costs               |
| cpg residential repair & remodeling costs |
| concrete & masonry costs                  |
| electrical costs                          |
| electrical change order costs             |
| facilities construction costs             |
| facilities maintenance & repair costs     |
| green building costs                      |
| heavy construction costs                  |
| interior costs                            |
| labor rates for the construction industry |
| light commercial costs                    |
| mechanical costs                          |
| open shop building construction costs     |
| plumbing costs                            |
| residential costs                         |
| site work & landscape costs               |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| b                     |
|:----------------------|
| sapna                 |
| ipf 4-9               |
| pant pea-13           |
| pusa prabhat (ddr-23) |
| ambika                |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a          |
|:-----------|
| robbery    |
| a. assault |
| burglary   |
| larceny    |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| group name              |
|:------------------------|
| michael_ ciane_ carmen  |
| calista_ julia_ mckenna |
| abigail_ madison        |
| colin_ evan_ david      |
| josh_ alex_ kai         |
| adrian_ gavin_ jayden   |
| julia_ emily_ iyanna    |
| mean (avg) distance     |
| median distance         |
| maximum distance        |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| cancer sites                                          |
|:------------------------------------------------------|
| all solid tumors                                      |
| oral cavity and pharynx                               |
| gum and other mouth                                   |
| digestive system                                      |
| stomach                                               |
| small intestine                                       |
| colon                                                 |
| rectum                                                |
| hepatobiliary                                         |
| liver                                                 |
| pancreas                                              |
| respiratory system                                    |
| lung and bronchus                                     |
| soft tissue including heart                           |
| skin excluding basal and squamous                     |
| melanoma of the skin                                  |
| female breast                                         |
| female genital system                                 |
| cervix uteri                                          |
| corpus and uterus_ nos                                |
| ovary                                                 |
| male genital system                                   |
| prostate                                              |
| urinary system                                        |
| urinary bladder                                       |
| kidney                                                |
| endocrine system                                      |
| thyroid                                               |
| all hematological and lymphatic except acute leukemia |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a                          |
|:---------------------------|
| hot cups (e.g. coffee cup) |
| mail                       |
| paper mailing pouches      |
| kraft bags                 |
| magazines                  |
| newspaper                  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| f                                                                                                           |
|:------------------------------------------------------------------------------------------------------------|
| http://www.installationpartssupply.com/category/stainless-steel-female-flare-swivel-nut.html#.vdap8_lviko   |
| http://www.installationpartssupply.com/category/oetiker-stepless-clamps.html#.vdanqplvikp                   |
| http://www.installationpartssupply.com/category/fit-john-guest-barb.html#.vdes-flviko                       |
| http://www.installationpartssupply.com/mm5/merchant.mvc?screen=prod&store_code=ips&product_code=g66-66      |
| http://www.installationpartssupply.com/category/female-flare-swivel-elbow-stainless-steel.html#.vihwhgtuxye |
| http://www.installationpartssupply.com/category/stainless-steel-female-flare-swivel-nut.html#.vihwimtuxye   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| t                    |
|:---------------------|
| 1024 kbps            |
| 12.5 khz             |
| 50 khz wireless      |
| 25 khz wireless      |
| 12.5 khz wireless    |
| 25/12.5 khz wireless |
| 12.5/25 khz wireless |
| varies               |
| 25 khz               |
| various              |
| 5 mhz                |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| r   |
|:----|
| d   |
| c   |
| b   |
| aa  |
| a   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| c           |
|:------------|
| spotvent    |
| mayfair     |
| sdf         |
| curzon      |
| sapphire    |
| dvf         |
| vitalis     |
| svc         |
| kitchenaire |
| gla         |
| classic     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| type    |
|:--------|
| alus    |
| ex      |
| pfsp/cu |
| pfsp/al |
| tfsp/al |
| tfxp/al |
| amka    |
| feral   |
| al      |
| cu      |
| amcmk   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| channel    |
|:-----------|
| mail order |
| dealer     |
| web site   |
| in store   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0   |
|:-------------|
| rf4          |
| rff          |
| r1h          |
| rc9          |
| rq3          |
| rxl          |
| rae          |
| rxq          |
| rwy          |
| rgt          |
| rfs          |
| rjr          |
| rxp          |
| rn7          |
| rbd          |
| rwh          |
| rvv          |
| rxr          |
| rde          |
| rxc          |
| rvr          |
| rdu          |
| rr7          |
| rlt          |
| rte          |
| rn3          |
| rj1          |
| rn5          |
| rcd          |
| rwa          |
| ryj          |
| r1f          |
| rgp          |
| rnq          |
| rjz          |
| rax          |
| rxn          |
| rr8          |
| rj2          |
| rem          |
| rwf          |
| r0a          |
| rpa          |
| raj          |
| rbt          |
| rxf          |
| rd8          |
| rm1          |
| rap          |
| rvw          |
| rgn          |
| rns          |
| rbz          |
| rjl          |
| rtf          |
| rx1          |
| rth          |
| rhu          |
| rhw          |
| ref          |
| rh8          |
| ral          |
| ra2          |
| rd1          |
| rm3          |
| rnz          |
| rhq          |
| rk5          |
| rh5          |
| rtr          |
| r0b          |
| rjc          |
| rj7          |
| rtp          |
| rbv          |
| ren          |
| rna          |
| ras          |
| rtd          |
| rqw          |
| rcx          |
| rfr          |
| rpy          |
| rl4          |
| rxw          |
| ra9          |
| rwd          |
| rrv          |
| rhm          |
| rrk          |
| ra7          |
| rkb          |
| r0d          |
| rtg          |
| rwe          |
| rtx          |
| rje          |
| rk9          |
| ryr          |
| rbk          |
| rww          |
| rwg          |
| rgr          |
| rbl          |
| rwp          |
| rrf          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a    |
|:-----|
| iii  |
| iv   |
| i/15 |
| ii   |
| i/16 |
| i/17 |
| i/18 |
| i/19 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| e                  |
|:-------------------|
| womens sandals     |
| womens shoes       |
| mens sandals       |
| mens shoes         |
| kids boys sandals  |
| kids girls sandals |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| sampling period start date *(§63.655(h)(8)(iv))   |
|:--------------------------------------------------|
| e.g.: 1/1/18                                      |
| e.g.: 1/14/18                                     |
| e.g.: 1/27/18                                     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| telephone   |
|:------------|
| 72 50 77 64 |
| 72 49 45 61 |
| 75 63 66 97 |
| 72 84 19 85 |
| 72 53 58 72 |
| 72 69 80 23 |
| 75 09 15 26 |
| 75 10 99 59 |
| 75 15 10 98 |
| 75 71 98 67 |
| 72 73 37 16 |
| 75 64 30 38 |
| 70 55 28 30 |
| 72 71 09 61 |
| 72 45 85 41 |
| 70 55 28 27 |
| 70 55 28 66 |
| 72 18 75 82 |
| 70 55 60 66 |
| 75 29 60 70 |
| 72 53 01 79 |
| 72 52 43 91 |
| 72 04 86 93 |
| 77 77 81 32 |
| 72 62 83 39 |
| 72 49 46 01 |
| 75 91 19 67 |
| 75 43 30 51 |
| 72 79 39 89 |
| 75 56 65 75 |
| 75 21 86 11 |
| 70 70 90 00 |
| 72 68 01 41 |
| 75 40 42 31 |
| 72 23 91 42 |
| 72 11 03 24 |
| 75 18 46 45 |
| 75 22 00 54 |
| 75 50 23 80 |
| 70 80 58 70 |
| 75 36 86 38 |
| 72 31 69 63 |
| 72 69 63 60 |
| 72 17 66 16 |
| 72 17 64 39 |
| 72 01 00 64 |
| 72 17 57 09 |
| 72 69 63 65 |
| 70 73 81 97 |
| 70 46 46 64 |
| 70 17 74 21 |
| 70 85 74 44 |
| 72 30 04 61 |
| 72 51 52 68 |
| 72 10 56 70 |
| 72 65 66 49 |
| 75 32 50 51 |
| 72 17 73 93 |
| 72 30 05 34 |
| 72 23 97 55 |
| 72 22 46 46 |
| 70 55 60 10 |
| 70 07 57 79 |
| 70 07 60 42 |
| 75 86 20 24 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| a                 |
|:------------------|
| christine farnish |
| martin lawrence   |
| alison munro      |
| catherine waddams |
| tim waggott       |
| rachel fletcher   |
| aileen armstrong  |
| david black       |
| jenny block       |
| lisa commane      |
| claire forbes     |
| emma kelso        |
| alena kozakova    |