Triple

T7379560
Position Surface form Disambiguated ID Type / Status
Subject The Marrying Man E170211 entity
Predicate starredActor P5563 FINISHED
Object Armand Assante E70556 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: Armand Assante | Statement: [The Marrying Man, starredActor, Armand Assante]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Armand Assante
Context triple: [The Marrying Man, starredActor, Armand Assante]
  • A. Armand Assante chosen
    Armand Assante is an American actor known for his intense screen presence and roles in crime dramas and historical films.
  • B. Charles Bronson
    Charles Bronson was an American film actor best known for his tough-guy roles in action and Western movies, including the "Death Wish" series.
  • C. Lawrence Tierney
    Lawrence Tierney was an American film and television actor best known for his tough-guy roles in classic crime films and later for his intimidating presence in movies like Quentin Tarantino's "Reservoir Dogs."
  • D. Michael Madsen
    Michael Madsen is an American actor known for his tough-guy roles in films such as Reservoir Dogs, Kill Bill, and other Quentin Tarantino movies.
  • E. Miguel Ferrer
    Miguel Ferrer was an American character actor known for his intense, often villainous roles in film and television, including notable performances in projects like "RoboCop," "Twin Peaks," and "NCIS: Los Angeles."
  • 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_69c68a5d0ed08190b6d361e68f813330 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f1c61484819087874d4e7f9fd791 completed March 27, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8276ec3b88190b720354787f7a735 completed March 28, 2026, 7:09 p.m.
Created at: March 27, 2026, 3:08 p.m.