Triple

T18078337
Position Surface form Disambiguated ID Type / Status
Subject Academy Award for Best Film Editing E432616 entity
Predicate winner P354 FINISHED
Object Paul Rogers 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: Paul Rogers | Statement: [Academy Award for Best Film Editing, winner, Paul Rogers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paul Rogers
Context triple: [Academy Award for Best Film Editing, winner, Paul Rogers]
  • A. Paul Rogers chosen
    Paul Rogers is an American film editor best known for his Academy Award–winning work on the multiverse film "Everything Everywhere All at Once."
  • B. Peter Rogers
    Peter Rogers was a British film producer best known for overseeing the long-running and popular "Carry On" comedy film series.
  • C. Phil Rogers
    Phil Rogers is an American sportswriter and baseball analyst known for his extensive coverage of Major League Baseball and authorship of several books on the sport.
  • D. Graham Rogers
    Graham Rogers is an American actor known for his roles in television series such as "The Kominsky Method," "Quantico," and "Atypical."
  • E. Paul Eldridge
    Paul Eldridge was an American writer, poet, and educator best known for his philosophical and speculative fiction, often co-authored with George Sylvester Viereck.
  • 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_69d8b9070cac81909fa9473fb1c3f1c7 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4d9f6a85481909894c39c8be98d5d completed April 19, 2026, 1:34 p.m.
Created at: April 10, 2026, 10:27 a.m.