Triple

T2658099
Position Surface form Disambiguated ID Type / Status
Subject The Aviator E54662 entity
Predicate producer P490 FINISHED
Object Michael Mann E57782 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: Michael Mann | Statement: [The Aviator, producer, Michael Mann]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Mann
Context triple: [The Aviator, producer, Michael Mann]
  • A. Michael Mann chosen
    Michael Mann is an American filmmaker renowned for his stylish, atmospheric crime dramas and thrillers such as "Heat," "Collateral," and "Manhunter."
  • B. Michael Mann
    Michael Mann was a German sociologist and political scientist, son of writer Thomas Mann, known for his work on the sociology of power and social structures.
  • C. Michael Cimino
    Michael Cimino was an American film director and screenwriter best known for his ambitious, visually striking dramas and his Oscar-winning work on the Vietnam War epic "The Deer Hunter."
  • D. Robert Stevens
    Robert Stevens was an American television and film director best known for his work on classic anthology series such as Alfred Hitchcock Presents.
  • E. Richard LaGravenese
    Richard LaGravenese is an American screenwriter and director known for his character-driven dramas and adaptations, including films like "The Fisher King," "The Bridges of Madison County," and "P.S. I Love You."
  • 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_69ab49e028948190b97e01d73548b1d9 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd94c61a08190bdf5e1caeff3e788 completed March 7, 2026, 7:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98d535388190979a549dc2ce5f2f completed March 10, 2026, 4:06 a.m.
Created at: March 6, 2026, 9:53 p.m.