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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
escalation_types: list<item: string>
  child 0, item: string
amount_threshold: double
note: string
to
{'ticket_id': Value('string'), 'type': Value('string'), 'priority': Value('string'), 'amount': Value('float64'), 'days_open': Value('int64'), 'status': Value('string'), 'customer_name': Value('string'), 'description': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              escalation_types: list<item: string>
                child 0, item: string
              amount_threshold: double
              note: string
              to
              {'ticket_id': Value('string'), 'type': Value('string'), 'priority': Value('string'), 'amount': Value('float64'), 'days_open': Value('int64'), 'status': Value('string'), 'customer_name': Value('string'), 'description': Value('string')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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ticket_id string	type string	priority string	amount float64	days_open int64	status string	customer_name string	description string
TCK-1010	technical	low	45	5	open	Sofia Rossi	Avatar image not loading on profile page
TCK-1002	billing	medium	350	6	open	Diego Torres	Duplicate charge on order
TCK-1007	inquiry	low	0	2	open	Sofia Rossi	Question about loyalty program points
TCK-1001	technical	high	450	3	open	Lucas Meyer	Payment gateway outage blocking all card payments
TCK-1003	billing	low	189.9	15	open	Priya Sharma	Wrong size delivered, customer requests full refund
TCK-1004	billing	low	20	4	open	Diego Torres	Shipping fee refund for delayed order
TCK-1008	technical	low	100	9	open	Lucas Meyer	Upload fails when file size is exactly 100 MB
TCK-1009	billing	medium	80	7	open	Priya Sharma	Partial refund for subscription downgrade
TCK-1006	feature	low	0	1	open	Diego Torres	Request dark mode support
TCK-1005	technical	medium	120.5	2	open	Diego Torres	Order confirmation email not sent
null	null	null	null	null	null	null	null

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Check out the documentation for more information.

Support Ticket Triage Dataset

This dataset contains a set of customer support tickets that must be triaged according to a prioritization policy.

Purpose

An analyst must read the prioritization policy and apply it to every open ticket, deciding whether each ticket should be escalated or resolved based on the rules in the policy. The final decision for each ticket is then recorded in a structured report.

Layout

policy/prioritization_policy.json - the business rules for escalation.
data/tickets.json - the list of tickets to process.

Tickets

The data/tickets.json file is a JSON array. Each ticket object contains:

Field	Type	Description
`ticket_id`	string	Unique identifier for the ticket (e.g. `TCK-1001`).
`type`	string	Category of the ticket (e.g. `billing`, `technical`).
`priority`	string	Priority level (`high`, `medium`, `low`).
`amount`	number	Financial amount associated with the ticket (USD).
`days_open`	number	How many days the ticket has been open.
`status`	string	Current status (all tickets are `open`).
`customer_name`	string	Name of the customer who submitted the ticket.
`description`	string	Short description of the issue.

There are 10 tickets in this dataset.

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