Triple

T12877265
Position Surface form Disambiguated ID Type / Status
Subject Showgirls E308000 entity
Predicate screenwriter P2831 FINISHED
Object Joe Eszterhas E329583 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: Joe Eszterhas | Statement: [Showgirls, screenwriter, Joe Eszterhas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joe Eszterhas
Context triple: [Showgirls, screenwriter, Joe Eszterhas]
  • A. Joe Eszterhas chosen
    Joe Eszterhas is a Hungarian-American screenwriter best known for his provocative, high-profile Hollywood thrillers of the late 20th century.
  • B. Lawrence Dobkin
    Lawrence Dobkin was an American character actor, director, and narrator known for his prolific work in film, television, and radio from the 1940s through the 1980s.
  • C. Gene Corman
    Gene Corman was an American film producer and talent agent known for his work in low-budget genre films and for collaborating with major studios on commercially successful projects.
  • D. Peter Hyams
    Peter Hyams is an American filmmaker known for directing and often writing and shooting genre films such as science fiction thrillers and action movies.
  • E. Jon Spaihts
    Jon Spaihts is an American screenwriter and producer best known for his work on major science fiction and blockbuster films such as "Prometheus," "Doctor Strange," and "Dune."
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970fa8474819086a8af3c90f3ca84 completed April 10, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69bb83bac8190838f7537b806317c completed May 3, 2026, 12:50 a.m.
Created at: April 9, 2026, 5:38 p.m.