Datasets:

flatcircle
/

sp500-lexical-pivots-v1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
ticker: string
company: string
finding: string
prior_event: string
prior_event_id: int64
prior_event_date: string
prior_excerpt: string
latest_event: string
latest_event_id: int64
latest_event_date: string
latest_excerpt: string
company_row: int64
finding_row: int64
name: string
version: string
primary_source: string
analysis: struct<min_finding_score: double, material_deflection_delta: double, material_guidance_answer_drop:  (... 256 chars omitted)
  child 0, min_finding_score: double
  child 1, material_deflection_delta: double
  child 2, material_guidance_answer_drop: int64
  child 3, topic_context_window_sentences: int64
  child 4, lexical_pivots: list<item: struct<topic: string, from: list<item: string>, to: list<item: string>, interpretation: s (... 7 chars omitted)
      child 0, item: struct<topic: string, from: list<item: string>, to: list<item: string>, interpretation: string>
          child 0, topic: string
          child 1, from: list<item: string>
              child 0, item: string
          child 2, to: list<item: string>
              child 0, item: string
          child 3, interpretation: string
  child 5, deflection_markers: list<item: string>
      child 0, item: string
  child 6, forward_guidance_markers: list<item: string>
      child 0, item: string
outputs: struct<universe_file: string, raw_transcript_bundle: string, company_level_file: string, finding_lev (... 16 chars omitted)
  child 0, universe_file: string
  child 1, raw_transcript_bundle: string
  child 2, company_level_file: string
  child 3, finding_level_file: string
quartr: struct<base_url: string, timeout_seconds: int64, max_retries: int64, request_pause_seconds: double>
  child 0, base_url: string
  child 1, timeout_seconds: int64
  child 2, max_retries: int64
  child 3, request_pause_seconds: double
scope: struct<language: string, event_type_ids: list<item: int64>, events_per_company: int64, comparison: s (... 96 chars omitted)
  child 0, language: string
  child 1, event_type_ids: list<item: int64>
      child 0, item: int64
  child 2, events_per_company: int64
  child 3, comparison: string
  child 4, minimum_transcripts_per_company: int64
  child 5, topics: list<item: string>
      child 0, item: string
  child 6, dataset_rows: string
universe: struct<index: string, source_url: string, ticker_column: string, company_column: string, gics_sector (... 91 chars omitted)
  child 0, index: string
  child 1, source_url: string
  child 2, ticker_column: string
  child 3, company_column: string
  child 4, gics_sector_column: string
  child 5, gics_sub_industry_column: string
  child 6, user_agent: string
  child 7, output_file: string
to
{'name': Value('string'), 'version': Value('string'), 'primary_source': Value('string'), 'universe': {'index': Value('string'), 'source_url': Value('string'), 'ticker_column': Value('string'), 'company_column': Value('string'), 'gics_sector_column': Value('string'), 'gics_sub_industry_column': Value('string'), 'user_agent': Value('string'), 'output_file': Value('string')}, 'scope': {'language': Value('string'), 'event_type_ids': List(Value('int64')), 'events_per_company': Value('int64'), 'comparison': Value('string'), 'minimum_transcripts_per_company': Value('int64'), 'topics': List(Value('string')), 'dataset_rows': Value('string')}, 'quartr': {'base_url': Value('string'), 'timeout_seconds': Value('int64'), 'max_retries': Value('int64'), 'request_pause_seconds': Value('float64')}, 'analysis': {'min_finding_score': Value('float64'), 'material_deflection_delta': Value('float64'), 'material_guidance_answer_drop': Value('int64'), 'topic_context_window_sentences': Value('int64'), 'lexical_pivots': List({'topic': Value('string'), 'from': List(Value('string')), 'to': List(Value('string')), 'interpretation': Value('string')}), 'deflection_markers': List(Value('string')), 'forward_guidance_markers': List(Value('string'))}, 'outputs': {'universe_file': Value('string'), 'raw_transcript_bundle': Value('string'), 'company_level_file': Value('string'), 'finding_level_file': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              ticker: string
              company: string
              finding: string
              prior_event: string
              prior_event_id: int64
              prior_event_date: string
              prior_excerpt: string
              latest_event: string
              latest_event_id: int64
              latest_event_date: string
              latest_excerpt: string
              company_row: int64
              finding_row: int64
              name: string
              version: string
              primary_source: string
              analysis: struct<min_finding_score: double, material_deflection_delta: double, material_guidance_answer_drop:  (... 256 chars omitted)
                child 0, min_finding_score: double
                child 1, material_deflection_delta: double
                child 2, material_guidance_answer_drop: int64
                child 3, topic_context_window_sentences: int64
                child 4, lexical_pivots: list<item: struct<topic: string, from: list<item: string>, to: list<item: string>, interpretation: s (... 7 chars omitted)
                    child 0, item: struct<topic: string, from: list<item: string>, to: list<item: string>, interpretation: string>
                        child 0, topic: string
                        child 1, from: list<item: string>
                            child 0, item: string
                        child 2, to: list<item: string>
                            child 0, item: string
                        child 3, interpretation: string
                child 5, deflection_markers: list<item: string>
                    child 0, item: string
                child 6, forward_guidance_markers: list<item: string>
                    child 0, item: string
              outputs: struct<universe_file: string, raw_transcript_bundle: string, company_level_file: string, finding_lev (... 16 chars omitted)
                child 0, universe_file: string
                child 1, raw_transcript_bundle: string
                child 2, company_level_file: string
                child 3, finding_level_file: string
              quartr: struct<base_url: string, timeout_seconds: int64, max_retries: int64, request_pause_seconds: double>
                child 0, base_url: string
                child 1, timeout_seconds: int64
                child 2, max_retries: int64
                child 3, request_pause_seconds: double
              scope: struct<language: string, event_type_ids: list<item: int64>, events_per_company: int64, comparison: s (... 96 chars omitted)
                child 0, language: string
                child 1, event_type_ids: list<item: int64>
                    child 0, item: int64
                child 2, events_per_company: int64
                child 3, comparison: string
                child 4, minimum_transcripts_per_company: int64
                child 5, topics: list<item: string>
                    child 0, item: string
                child 6, dataset_rows: string
              universe: struct<index: string, source_url: string, ticker_column: string, company_column: string, gics_sector (... 91 chars omitted)
                child 0, index: string
                child 1, source_url: string
                child 2, ticker_column: string
                child 3, company_column: string
                child 4, gics_sector_column: string
                child 5, gics_sub_industry_column: string
                child 6, user_agent: string
                child 7, output_file: string
              to
              {'name': Value('string'), 'version': Value('string'), 'primary_source': Value('string'), 'universe': {'index': Value('string'), 'source_url': Value('string'), 'ticker_column': Value('string'), 'company_column': Value('string'), 'gics_sector_column': Value('string'), 'gics_sub_industry_column': Value('string'), 'user_agent': Value('string'), 'output_file': Value('string')}, 'scope': {'language': Value('string'), 'event_type_ids': List(Value('int64')), 'events_per_company': Value('int64'), 'comparison': Value('string'), 'minimum_transcripts_per_company': Value('int64'), 'topics': List(Value('string')), 'dataset_rows': Value('string')}, 'quartr': {'base_url': Value('string'), 'timeout_seconds': Value('int64'), 'max_retries': Value('int64'), 'request_pause_seconds': Value('float64')}, 'analysis': {'min_finding_score': Value('float64'), 'material_deflection_delta': Value('float64'), 'material_guidance_answer_drop': Value('int64'), 'topic_context_window_sentences': Value('int64'), 'lexical_pivots': List({'topic': Value('string'), 'from': List(Value('string')), 'to': List(Value('string')), 'interpretation': Value('string')}), 'deflection_markers': List(Value('string')), 'forward_guidance_markers': List(Value('string'))}, 'outputs': {'universe_file': Value('string'), 'raw_transcript_bundle': Value('string'), 'company_level_file': Value('string'), 'finding_level_file': Value('string')}}
              because column names don't match

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S&P 500 Lexical Pivots v1

This dataset contains derived outputs from a screen for quarter-over-quarter language shifts in S&P 500 earnings-call transcripts.

The project compares each company's most recent Quartr earnings-call transcript with the immediately prior transcript for the same Quartr company_id, then surfaces candidate pivots in topics such as demand, pricing power, inventory, consumer health, guidance, and Q&A deflection.

Important Data Note

Raw Quartr transcript text is not included in this public dataset package. The raw transcript bundle was used locally to build and validate derived outputs, but redistribution may be restricted by the underlying data license.

Anyone with the same API access can rerun the collection workflow using the scripts and replication guide.

Files

data/sp500_universe.jsonl
data/company_lexical_pivots.jsonl
data/lexical_pivot_findings.jsonl
data/best_excerpt_comparisons.jsonl
config/dataset.json
scripts/
docs/

Data Files

data/sp500_universe.jsonl: current S&P 500 constituent-security universe from the reference run.
data/company_lexical_pivots.jsonl: one row per same-company latest/prior transcript comparison.
data/lexical_pivot_findings.jsonl: one row per detected lexical-pivot or deflection finding.
data/best_excerpt_comparisons.jsonl: curated short prior/latest quote comparisons with event IDs, event names, event dates, and source row references.

Reference Counts

Reference run date: 2026-05-03

503  data/sp500_universe.jsonl
468  data/company_lexical_pivots.jsonl
369  data/lexical_pivot_findings.jsonl
30   data/best_excerpt_comparisons.jsonl

The original local raw transcript bundle contained 967 Quartr transcript rows and is intentionally excluded from this public upload.

Public Artifacts

Findings markdown: docs/FINDINGS_SUMMARY.md
Substack paste version: docs/SUBSTACK_READY.md
Public replication spec: docs/lexical_pivots.md

Caveats

This is a research dataset and candidate-generation screen, not investment advice. Some flags may be false positives or positive-but-caveated. Review the underlying licensed transcripts before making any investment or business conclusion.

The included docs/lexical_pivots.md describes the preferred public rerun workflow: full-universe scan, latest/prior same-company pairing, LLM review for semantic judgments, quote validation, and dynamic top-three ranking. The derived v1 files included here are the auditable artifacts from the local project package.

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Total file size:

1.15 MB