Triple

T1781763
Position Surface form Disambiguated ID Type / Status
Subject The Pit and the Pendulum E39304 entity
Predicate filmAdaptationStar P795 FINISHED
Object Vincent Price E144309 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: Vincent Price | Statement: [The Pit and the Pendulum, filmAdaptationStar, Vincent Price]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vincent Price
Context triple: [The Pit and the Pendulum, filmAdaptationStar, Vincent Price]
  • A. Vincent Price chosen
    Vincent Price was an American actor renowned for his distinctive voice and charismatic presence, particularly in classic horror films and gothic dramas.
  • B. Vincent E. Price
    Vincent E. Price is an American political communication scholar and academic leader who serves as the president of Duke University.
  • C. Henry Hull
    Henry Hull was an American character actor best known for his prolific work in early 20th-century stage and film, including notable roles in classic Hollywood productions.
  • D. Bela Lugosi
    Bela Lugosi was a Hungarian-American actor best known for his iconic portrayal of Count Dracula in early horror cinema.
  • E. John Carradine
    John Carradine was a prolific American character actor known for his distinctive voice and extensive work in classic Hollywood films, including numerous Westerns and horror movies.
  • 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_69a88630519c8190a17addd83c4a3ef4 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64e34fe881908aa75f2b4141b87b completed March 6, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada99f52a08190854109d152c22be0 completed March 8, 2026, 4:53 p.m.
Created at: March 4, 2026, 7:31 p.m.