Triple

T2500261
Position Surface form Disambiguated ID Type / Status
Subject Karen Verne E52446 entity
Predicate workedIn P1527 FINISHED
Object Hollywood film industry E247 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: Hollywood film industry | Statement: [Karen Verne, workedIn, Hollywood film industry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hollywood film industry
Context triple: [Karen Verne, workedIn, Hollywood film industry]
  • A. Hollywood studios
    Hollywood studios are major American film production companies based in Hollywood that dominate the global movie industry through large-scale financing, production, and distribution of films.
  • B. Hollywood
    Hollywood is a residential neighborhood in the city of College Park, Maryland, known for its suburban character and proximity to the University of Maryland.
  • C. Hollywood chosen
    Hollywood is a famous Los Angeles neighborhood internationally recognized as the historic center of the American film and entertainment industry.
  • D. Hollywood
    Hollywood is a residential neighborhood in Homewood, Alabama, known for its historic homes and suburban character just outside Birmingham.
  • E. Hollywood
    Hollywood is a coastal city in southeastern Florida known for its beaches, boardwalk, and proximity to Miami.
  • 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_69ab4957b3a88190adf968ae0c1b931c completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd1afd86c81909181c4b45d4f8bc5 completed March 7, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1fa0e26481908e383a6d44b3f3d5 completed March 9, 2026, 7:29 p.m.
Created at: March 6, 2026, 9:46 p.m.