Triple

T12916827
Position Surface form Disambiguated ID Type / Status
Subject The Karate Kid Part III E309006 entity
Predicate antagonist P4675 FINISHED
Object Mike Barnes E1011347 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: Mike Barnes | Statement: [The Karate Kid Part III, antagonist, Mike Barnes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mike Barnes
Context triple: [The Karate Kid Part III, antagonist, Mike Barnes]
  • A. Mike Barnes chosen
    Mike Barnes is the aggressive karate fighter hired to defeat Daniel LaRusso in the film "The Karate Kid Part III."
  • B. Forest Baskett
    Forest Baskett is an American computer scientist and venture capitalist known for his influential work in computer architecture and his role as a general partner at New Enterprise Associates (NEA).
  • C. Roy Hurley
    Roy Hurley was an American professional basketball player who competed in the Basketball Association of America during the 1940s.
  • D. Ross Barnes
    Ross Barnes was a 19th-century American baseball infielder renowned as one of the first great stars of professional baseball and a key figure in the early National League.
  • E. Scott Barnes
    Scott Barnes is an American college athletics administrator who serves as the athletic director at Oregon State University and has previously held similar roles at several other universities.
  • 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_69d7bdf92b588190acdf2a2291ac4590 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d971a1e8088190af697629baecf59f completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8d101e08190b31d6aaaecf96507 completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 5:41 p.m.