Triple

T4626164
Position Surface form Disambiguated ID Type / Status
Subject Wild Things E101101 entity
Predicate starring P1507 FINISHED
Object Robert Wagner E224464 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: Robert Wagner | Statement: [Wild Things, starring, Robert Wagner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robert Wagner
Context triple: [Wild Things, starring, Robert Wagner]
  • A. Robert Wagner chosen
    Robert Wagner is an American film and television actor best known for his roles in series like "Hart to Hart" and films such as the "Austin Powers" franchise.
  • B. Richard Conte
    Richard Conte was an American film and television actor best known for his roles in classic crime dramas and film noir, including "The Godfather" and "Thieves' Highway."
  • C. Frank Giustra
    Frank Giustra is a Canadian businessman and philanthropist best known as the founder of Lionsgate Entertainment and for his extensive work in the mining and film industries.
  • D. Efrem Zimbalist
    Efrem Zimbalist was a renowned Russian-American violinist, composer, and influential pedagogue of the 20th century.
  • E. John Phillip Law
    John Phillip Law was an American film actor known for his roles in 1960s and 1970s movies, including notable performances in both comedies and cult classics.
  • 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_69bd43d0497c8190ac23c65c5804846a completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5a0a7b588190bc6552ee5babb198 completed March 20, 2026, 2:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfaab30508190881828adab92ba22 completed March 21, 2026, 1:55 a.m.
Created at: March 20, 2026, 1:13 p.m.