Triple

T14008873
Position Surface form Disambiguated ID Type / Status
Subject Ghostbusters (2016 film) E337024 entity
Predicate screenwriter P2831 FINISHED
Object Paul Feig E271298 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: Paul Feig | Statement: [Ghostbusters (2016 film), screenwriter, Paul Feig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paul Feig
Context triple: [Ghostbusters (2016 film), screenwriter, Paul Feig]
  • A. Paul Feig chosen
    Paul Feig is an American filmmaker, producer, and actor best known for directing hit comedies such as "Bridesmaids" and creating the cult TV series "Freaks and Geeks."
  • B. Paul Feigay
    Paul Feigay is a theatrical producer known for his work on the classic Broadway musical "On the Town."
  • C. Carol Holofcener
    Carol Holofcener is the mother of American film director and screenwriter Nicole Holofcener.
  • D. Anna Boden
    Anna Boden is an American filmmaker best known for co-directing the Marvel Studios superhero film "Captain Marvel" and for her long-time creative partnership with Ryan Fleck on independent dramas.
  • E. Charlotte Wells
    Charlotte Wells is a central courtesan character in the British period drama series "Harlots," known for navigating the power struggles and personal conflicts within 18th-century London's sex trade.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2ed44f90819099ad08c09c066b56 completed April 14, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbc32b459c81908b652286f444e940 completed May 6, 2026, 10:39 p.m.
Created at: April 9, 2026, 10:19 p.m.