Triple

T3342631
Position Surface form Disambiguated ID Type / Status
Subject All the Right Moves E70293 entity
Predicate storyBy P1955 FINISHED
Object Michael Kane E357839 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 Kane | Statement: [All the Right Moves, storyBy, Michael Kane]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Kane
Context triple: [All the Right Moves, storyBy, Michael Kane]
  • A. Michael Kane chosen
    Michael Kane is a screenwriter best known for writing the 1983 American sports drama film "All the Right Moves."
  • B. Michael Jace
    Michael Jace is an American actor best known for his role as LAPD Officer Julien Lowe on the television series "The Shield."
  • C. Seymour Cassel
    Seymour Cassel was an American character actor known for his longtime collaboration with director John Cassavetes and his roles in numerous independent and mainstream films.
  • D. Robert Parrish
    Robert Parrish was an American film editor and director, as well as a former child actor, known for his work on several classic Hollywood films.
  • E. Michael Ironside
    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."
  • 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_69ad85a405e48190b6e68de7cf9f319e completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1f06f8c8190a6b7c56ac3f5ff07 completed March 8, 2026, 5:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69b360a07dec819094b0645d0e2a91da completed March 13, 2026, 12:56 a.m.
Created at: March 8, 2026, 3:12 p.m.