Triple

T17120633
Position Surface form Disambiguated ID Type / Status
Subject Scanners E415454 entity
Predicate castMember P1668 FINISHED
Object Michael Ironside E131601 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 Ironside | Statement: [Scanners, castMember, Michael Ironside]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Ironside
Context triple: [Scanners, castMember, Michael Ironside]
  • A. Michael Ironside chosen
    Michael Ironside is a Canadian actor known for his intense, often villainous roles in science fiction and action films such as "Total Recall," "Starship Troopers," and "Top Gun."
  • B. John Doman
    John Doman is an American character actor known for his authoritative roles in television series such as "The Wire" and "Gotham" as well as numerous film appearances.
  • C. Michael Lonsdale
    Michael Lonsdale was a distinguished French-English actor known for his versatile performances in European cinema and Hollywood films, including roles in "The Day of the Jackal" and the James Bond film "Moonraker."
  • D. Michael Kane
    Michael Kane is a screenwriter best known for writing the 1983 American sports drama film "All the Right Moves."
  • E. Michael Kane
    Michael Kane is a creator known for developing the character Stefen Djordjevic.
  • 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_69d886d090cc8190a39cb94992586905 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3e8092b548190b45c1695be47edc2 completed April 18, 2026, 8:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a013a1062fc8190b1c4e97f42cf3faa completed May 11, 2026, 2:08 a.m.
Created at: April 10, 2026, 5:36 a.m.