Triple

T8941554
Position Surface form Disambiguated ID Type / Status
Subject Ruby Keeler E212910 entity
Predicate workedWith P398 FINISHED
Object Dick Powell E166970 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: Dick Powell | Statement: [Ruby Keeler, workedWith, Dick Powell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dick Powell
Context triple: [Ruby Keeler, workedWith, Dick Powell]
  • A. Dick Powell chosen
    Dick Powell was an American actor, singer, and later film director and producer, known for his transition from light musical roles to hard-boiled film noir leads in the 1930s and 1940s.
  • B. Zachary Scott
    Zachary Scott was an American actor best known for his suave yet often villainous roles in 1940s and 1950s Hollywood films.
  • C. Robert Cummings
    Robert Cummings was an American film and television actor best known for his roles in comedies and thrillers during Hollywood’s Golden Age.
  • D. Victor Mature
    Victor Mature was an American film actor known for his rugged leading-man roles in 1940s and 1950s Hollywood epics and adventure films.
  • E. Glenn Ford
    Glenn Ford was a Canadian-American film actor renowned for his versatile performances in classic Hollywood movies such as "Gilda," "The Big Heat," and "Blackboard Jungle."
  • 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_69ca839694c88190b324ffeb43d23b08 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66b9c14c8190b80c3df0cdba2747 completed April 1, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05436de048190a884b7158f9281f7 completed April 3, 2026, 11:58 p.m.
Created at: March 30, 2026, 6:58 p.m.