task_id stringclasses 53
values | query stringclasses 53
values | answer stringlengths 1 139 | artifact_type stringclasses 6
values | artifact_scope stringclasses 4
values | query_cols listlengths 1 5 | artifact_reasoning_cols listlengths 0 8 | table dict | num_rows int64 10 1.13k | num_cols int64 5 20 | recovered_tables_transform_spec dict | base_data_num_tokens int64 1.94k 16.1k | base_data_token_bucket int64 2k 16k | perturbation_note stringclasses 257
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.52 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"8.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 56 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "8.0",
"row": 0
},
{
"col": "rank",
"new_value": "8.0",
"row": 41
},
{
"col": "rank",
"new_value": "6.0",
"row": 52
}
... | 3,977 | 4,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#NUM!",
"row": 6
},
{
"col": "rank",
"new_value": "#RANK",
"row": 16
}
]
]
} | 1,977 | 2,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.0 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 37 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "age",
"new_value": "31 years old",
"row": 5
},
{
"col": "age",
"new_value": "43 years old",
"row": 13
},
{
"col": "age",
"new_value": "40 years old",
... | 4,040 | 4,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.48 | missingness | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 145 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": null,
"row": 9
},
{
"col": "rank",
"new_value": null,
"row": 16
},
{
"col": "rank",
"new_value": null,
"row": 20
},
... | 15,952 | 16,000 | Introduced missingness in rank column. You can recover the missing values by looking at the time or time_in_seconds column. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.13 | inconsistent-formatting | single-column | [
"race_year_id",
"rank",
"age"
] | [
"rank"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 113 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "age",
"new_value": "43 years old",
"row": 1
},
{
"col": "age",
"new_value": "31 years old",
"row": 5
},
{
"col": "rank",
"new_value": "RANK: ---5th Place",
... | 7,995 | 8,000 | Introduced formatting inconsistencies in rank and age columns |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.43 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 225 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "9.0",
"row": 16
},
{
"col": "rank",
"new_value": "18.0",
"row": 29
},
{
"col": "rank",
"new_value": "9.0",
"row": 35
},
... | 15,991 | 16,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 38.5 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 18 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 1,977 | 2,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.59 | inconsistent-commonsense-logic | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 73 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "8.0",
"row": 0
},
{
"col": "rank",
"new_value": "9.0",
"row": 35
},
{
"col": "rank",
"new_value": "12.0",
"row": 65
}
... | 7,994 | 8,000 | Introduced an inconsistency in the rank column. Can be recovered by looking at time or time_in_seconds column of other rows. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.88 | outliers | single-column | [
"race_year_id",
"rank",
"age"
] | [
"age"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 145 | 20 | {
"drop_rows": [
[
16,
35
]
],
"overwrite_cells": [
[]
]
} | 15,952 | 16,000 | Introduced a obvious outliers in age column. Should be removed. |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 41.0 | clean | clean | [
"race_year_id",
"rank",
"age"
] | [] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance",
"nationality",
"participants",
"start_time",
"participation",
"elevation_gain",
"city",
"elevation_loss",
"country",
"aid_... | 37 | 20 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[]
]
} | 4,040 | 4,000 | |
ultra-trail-races-rank | We have a dataset of ultra trail running race results.
What is the average age of all top 5 finishers (i.e., any result with a rank of 1-5) in this dataset?
Return the result rounded to 2 decimal places. | 40.13 | bad-values | connected-multi-column | [
"race_year_id",
"rank",
"age"
] | [
"rank",
"time",
"time_in_seconds"
] | {
"headers": [
"race_year_id",
"race",
"rank",
"runner",
"age",
"time",
"time_in_seconds",
"date",
"event",
"distance"
],
"rows": [
[
"68140",
"Millstone 100",
"1.0",
"VERHEUL Jasper",
"30",
"26H 35M 25S",
"95725.0",
"2021... | 113 | 10 | {
"drop_rows": [
[]
],
"overwrite_cells": [
[
{
"col": "rank",
"new_value": "#NUM!",
"row": 5
},
{
"col": "rank",
"new_value": "#RANK",
"row": 17
},
{
"col": "rank",
"new_value": "#NUM!",
"row": 20
... | 7,995 | 8,000 | Introduced bad values in rank column. You can recover the missing values by looking at the other column. |
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