Triple

T22828946
Position Surface form Disambiguated ID Type / Status
Subject Peter Pan (2003 film) E565743 entity
Predicate editedBy P1954 FINISHED
Object Michael Kahn 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: Michael Kahn | Statement: [Peter Pan (2003 film), editedBy, Michael Kahn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Kahn
Context triple: [Peter Pan (2003 film), editedBy, Michael Kahn]
  • A. Michael Kahn chosen
    Michael Kahn is an acclaimed American film editor best known for his long-time collaboration with director Steven Spielberg on numerous major films.
  • B. Tom Kahn
    Tom Kahn was an American social democrat and civil rights activist known for his work with the AFL-CIO and his role in organizing the 1963 March on Washington.
  • C. Mitch Kertzman
    Mitch Kertzman is an American technology executive and entrepreneur best known for his leadership roles in the software and semiconductor industries, including at companies like LSI Logic and Sybase.
  • D. Phil Rubinstein
    Phil Rubinstein is a fictional character portrayed by actor Andrew Robinson, likely appearing in a film or television production.
  • E. Paul Stekler
    Paul Stekler is an American documentary filmmaker and political scientist known for his films on U.S. politics and history.
  • 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_69e24585ab1c81909b2b5065d15805d5 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17e2a0e308190941064965346f890 completed April 29, 2026, 3:42 a.m.
Created at: April 17, 2026, 3:34 p.m.