Triple

T5187716
Position Surface form Disambiguated ID Type / Status
Subject Wanted (2008 film) E117072 entity
Predicate editedBy P1954 FINISHED
Object David Brenner E236325 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: David Brenner | Statement: [Wanted (2008 film), editedBy, David Brenner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: David Brenner
Context triple: [Wanted (2008 film), editedBy, David Brenner]
  • A. David Brenner chosen
    David Brenner was an American film editor known for his work on major Hollywood blockbusters, including several of director Zack Snyder’s films.
  • B. Phil Bronstein
    Phil Bronstein is an American journalist and editor best known for his long tenure at the San Francisco Chronicle and his marriage to actress Sharon Stone.
  • C. Larry Brezner
    Larry Brezner was an American film producer and talent manager known for producing popular comedies such as "Good Morning, Vietnam," "The 'Burbs," and "Ride Along."
  • D. Len Blum
    Len Blum is a Canadian screenwriter known for his work on numerous comedy films, including the 2006 reboot of The Pink Panther.
  • E. Greg Grunberg
    Greg Grunberg is an American actor best known for his roles in television series such as "Heroes," "Alias," and "Felicity," as well as appearances in major film franchises.
  • 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_69bd44620ff48190bcac01782107a397 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd79c56280819085926316f7b520bc completed March 20, 2026, 4:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfd23af5988190a66bdbec54654970 completed March 22, 2026, 11:27 a.m.
Created at: March 20, 2026, 1:46 p.m.