Triple

T9940483
Position Surface form Disambiguated ID Type / Status
Subject Warner Oland E194066 entity
Predicate name P16 FINISHED
Object Warner Oland E194066 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: Warner Oland | Statement: [Warner Oland, name, Warner Oland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Warner Oland
Context triple: [Warner Oland, name, Warner Oland]
  • A. Warner Oland chosen
    Warner Oland was a Swedish-American actor best known for portraying the detective Charlie Chan in a popular series of 1930s films.
  • B. Harry Davenport
    Harry Davenport was an American character actor best known for his numerous supporting roles in classic Hollywood films of the 1930s and 1940s.
  • C. Warren William
    Warren William was an American stage and film actor of the 1930s, best known for his suave, often morally ambiguous leading and supporting roles in Hollywood pre-Code dramas and mysteries.
  • D. Otto Hunte
    Otto Hunte was a prominent German film art director and production designer best known for his influential work on classic Weimar-era films, including Fritz Lang’s Metropolis.
  • E. Jack Basehart
    Jack Basehart is the son of American actor Richard Basehart, known for his work in film and television during the mid-20th century.
  • 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_69ca82e409348190a393777356b80a2a completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb60f4ffc8190bfe916bb4a7bf5c5 completed April 2, 2026, 12:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b5d295908190a064fb72d65b6e24 completed April 5, 2026, 7:19 p.m.
Created at: March 30, 2026, 8:44 p.m.