Triple

T22395915
Position Surface form Disambiguated ID Type / Status
Subject Love and Monsters E553628 entity
Predicate screenwriter P2831 FINISHED
Object Brian Duffield NE NERFINISHED

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: Brian Duffield | Statement: [Love and Monsters, screenwriter, Brian Duffield]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brian Duffield
Context triple: [Love and Monsters, screenwriter, Brian Duffield]
  • A. Brian Duffield chosen
    Brian Duffield is an American screenwriter and director known for his work on genre films such as "Underwater," "Love and Monsters," and "Spontaneous."
  • B. David Duffield
    David Duffield is an American billionaire entrepreneur and philanthropist best known for founding the enterprise software companies PeopleSoft and Workday.
  • C. Michael Boughen
    Michael Boughen is a film producer known for his work on action and thriller movies, including the Jason Statham–starring film "Killer Elite."
  • D. Troy Duffy
    Troy Duffy is an American filmmaker and musician best known for writing and directing the cult crime film "The Boondock Saints."
  • E. Michael Healey
    Michael Healey is a Canadian playwright and actor best known for his acclaimed play "The Drawer Boy" and his contributions to contemporary Canadian theatre.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e4da7048190b4387d422a9a0de5 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1585e84b081908c95ed3e0d987ed8 completed April 29, 2026, 1:01 a.m.
Created at: April 16, 2026, 8:45 p.m.