Triple

T14075048
Position Surface form Disambiguated ID Type / Status
Subject Texas Carnival E338708 entity
Predicate leadActress P6108 FINISHED
Object Esther Williams E578100 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Esther Williams | Statement: [Texas Carnival, leadActress, Esther Williams]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Esther Williams
Context triple: [Texas Carnival, leadActress, Esther Williams]
  • A. Esther Williams
    Esther Williams was an American competitive swimmer-turned-Hollywood star best known for her aquatic-themed musical films in the 1940s and 1950s.
  • B. Eleanor Powell
    Eleanor Powell was an acclaimed American film and Broadway actress and dancer, celebrated especially for her virtuosic tap dancing in Hollywood musicals of the 1930s and 1940s.
  • C. Cleo Buckman Schwimmer
    Cleo Buckman Schwimmer is the daughter of American actor and director David Schwimmer and photographer Zoë Buckman.
  • D. Carole Landis
    Carole Landis was an American film actress and World War II pin-up star known for her glamorous screen presence in 1940s Hollywood.
  • E. Esther Jane Williams chosen
    Esther Jane Williams was an American competitive swimmer turned Hollywood film star, best known for her elaborate aquatic musical films in the 1940s and 1950s.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5c5bc49881909012b66fa451f495 completed April 14, 2026, 3:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcdefcc5708190beacccaa978a4abd completed May 7, 2026, 6:50 p.m.
Created at: April 9, 2026, 10:21 p.m.