Triple

T17012433
Position Surface form Disambiguated ID Type / Status
Subject Come What May E412732 entity
Predicate associatedCharacter P12208 FINISHED
Object Christian (Moulin Rouge!) E416860 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: Christian (Moulin Rouge!) | Statement: [Come What May, associatedCharacter, Christian (Moulin Rouge!)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christian (Moulin Rouge!)
Context triple: [Come What May, associatedCharacter, Christian (Moulin Rouge!)]
  • A. Christian (Moulin Rouge!) chosen
    Christian is the idealistic young poet and romantic lead in the musical film "Moulin Rouge!" who falls tragically in love with the courtesan Satine.
  • B. Charles Delevingne
    Charles Delevingne is a British property developer best known as the father of model and actress Cara Delevingne.
  • C. Dorian Rigal-Ansous
    Dorian Rigal-Ansous is a film editor known for his work on the movie "Heartbreaker."
  • D. Gabriel Signoret
    Gabriel Signoret was a French stage and film actor active in the early 20th century, known for his character roles in numerous French productions.
  • E. Cyrano Jones
    Cyrano Jones is a roguish, fast-talking trader of tribbles in the Star Trek universe, best known from the original series episode "The Trouble with Tribbles."
  • 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d47cc17c819087f7bd27582bcbfa completed April 18, 2026, 6:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01233311648190b5f8a8e7c209d124 completed May 11, 2026, 12:30 a.m.
Created at: April 10, 2026, 5:33 a.m.