Triple

T10355495
Position Surface form Disambiguated ID Type / Status
Subject The Thin Red Line E243988 entity
Predicate stars P1956 FINISHED
Object John Cusack E139225 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: John Cusack | Statement: [The Thin Red Line, stars, John Cusack]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Cusack
Context triple: [The Thin Red Line, stars, John Cusack]
  • A. John Cusack chosen
    John Cusack is an American actor, screenwriter, and producer known for his roles in films like "Say Anything..." and "High Fidelity" and for his outspoken political activism.
  • B. John C. Reilly
    John C. Reilly is an American actor known for his versatile performances in both dramatic films and broad comedies, including roles in movies like "Chicago," "Boogie Nights," and "Step Brothers."
  • C. Paul Cusack
    Paul Cusack is one of the children of renowned Irish actor Cyril Cusack, belonging to a prominent family in theatre and film.
  • D. Rob Lowe
    Rob Lowe is an American actor known for his roles in films like "St. Elmo's Fire" and TV series such as "Parks and Recreation" and "9-1-1: Lone Star."
  • E. Matthew Broderick
    Matthew Broderick is an American actor known for his work in film, theater, and television, particularly for iconic roles in movies like "Ferris Bueller's Day Off" and "WarGames."
  • 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_69d381b22b8c8190aaed476be5f872a9 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e953d4888190b7ca0ac932349dbf completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7fb827cd4819094bead4304795c33 completed April 9, 2026, 7:18 p.m.
Created at: April 6, 2026, 11:58 a.m.