Triple

T22032497
Position Surface form Disambiguated ID Type / Status
Subject Hannibal Lecter novels E544120 entity
Predicate mainCharacter P1183 FINISHED
Object Hannibal Lecter 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: Hannibal Lecter | Statement: [Hannibal Lecter novels, mainCharacter, Hannibal Lecter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hannibal Lecter
Context triple: [Hannibal Lecter novels, mainCharacter, Hannibal Lecter]
  • A. Hannibal Lecter chosen
    Hannibal Lecter is a brilliant but psychopathic cannibalistic serial killer and psychiatrist, best known as the chilling antagonist in Thomas Harris’s novels and their film adaptations.
  • B. Dick Lecter
    Dick Lecter is a supporting character in the satirical comedy film "Pootie Tang," known for his role in the movie’s offbeat, absurdist humor.
  • C. Colonel Woodrow Dolarhyde
    Colonel Woodrow Dolarhyde is a tough, authoritarian cattle baron and former Civil War officer who becomes a key leader in the fight against an alien invasion in the film "Cowboys & Aliens."
  • D. Francis Dolarhyde
    Francis Dolarhyde is the fictional serial killer known as "The Tooth Fairy" in Thomas Harris's novel *Red Dragon* and its screen adaptations.
  • E. Michael Ripps
    Michael Ripps is a film editor known for his work on the movie "Stakeout."
  • 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_69e11e2f98c8819083e11eab90942a78 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f127ee84848190a22c24bf14498520 completed April 28, 2026, 9:34 p.m.
Created at: April 16, 2026, 8:24 p.m.