Triple

T12106607
Position Surface form Disambiguated ID Type / Status
Subject Hickey E288317 entity
Predicate notableAdaptationPortrayalBy P20085 FINISHED
Object Kevin Spacey E90872 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: Kevin Spacey | Statement: [Hickey, notableAdaptationPortrayalBy, Kevin Spacey]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kevin Spacey
Context triple: [Hickey, notableAdaptationPortrayalBy, Kevin Spacey]
  • A. Kevin Spacey chosen
    Kevin Spacey is an American actor known for his intense, often villainous roles in film and television, including acclaimed performances in "The Usual Suspects," "American Beauty," and "House of Cards."
  • B. Brian Michael Cox
    Brian Michael Cox is a Grammy-winning American songwriter and record producer known for his work on numerous R&B and pop hits.
  • C. Michael York
    Michael York is an English actor known for his roles in films such as "Cabaret," "Logan's Run," and the "Austin Powers" series.
  • D. Ed Harris
    Ed Harris is an American actor and filmmaker known for his intense, authoritative performances in films such as "The Truman Show," "Apollo 13," and "Pollock."
  • E. Sam Waterston
    Sam Waterston is an American actor known for his distinguished film, television, and stage career, including prominent roles in productions such as Law & Order and The Killing Fields.
  • 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_69d6ab4a5c448190a110d1273314b21a completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d91561eaec819096ba00682d81f41a completed April 10, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f6795bf88190891acf918a432bef completed May 2, 2026, 1:04 p.m.
Created at: April 8, 2026, 9:49 p.m.