Triple

T662729
Position Surface form Disambiguated ID Type / Status
Subject Clyde Geronimi E11791 entity
Predicate notableWork P4 FINISHED
Object Peter Pan E67965 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: Peter Pan | Statement: [Clyde Geronimi, notableWork, Peter Pan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Pan
Context triple: [Clyde Geronimi, notableWork, Peter Pan]
  • A. Peter Pan chosen
    Peter Pan is a classic animated fantasy film produced by Walt Disney that follows the adventures of a boy who never grows up and his friends in the magical world of Neverland.
  • B. Dora
    Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
  • C. The Wizard of Oz
    The Wizard of Oz is a landmark 1939 American musical fantasy film renowned for its Technicolor visuals, iconic songs, and enduring status as a classic of Hollywood cinema.
  • D. Pinocchio
    Pinocchio is a classic animated film and character from Disney’s early feature-length productions, telling the story of a wooden puppet who dreams of becoming a real boy.
  • E. Muff Potter
    Muff Potter is a hapless, kind-hearted but often drunk vagrant and accused murderer in Mark Twain’s novel "The Adventures of Tom Sawyer."
  • 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_69a4932862a0819098be659c814e4981 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fd081e8819097f289961f5eff29 completed March 1, 2026, 8:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c398cc748190ab720263096064ef completed March 2, 2026, 5:06 p.m.
Created at: March 1, 2026, 7:36 p.m.