Triple

T8112269
Position Surface form Disambiguated ID Type / Status
Subject Michael Wilding E189383 entity
Predicate name P16 FINISHED
Object Michael Wilding E189383 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: Michael Wilding | Statement: [Michael Wilding, name, Michael Wilding]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Wilding
Context triple: [Michael Wilding, name, Michael Wilding]
  • A. Michael Wilding chosen
    Michael Wilding was a British film and stage actor best known for his roles in 1940s–1950s British cinema and for his high-profile marriage to actress Elizabeth Taylor.
  • B. Trevor Howard
    Trevor Howard was a distinguished English film and stage actor best known for his roles in classic films such as "Brief Encounter" and "The Third Man."
  • C. Colin Clive
    Colin Clive was a British actor best known for his iconic portrayal of Dr. Henry Frankenstein in the classic 1930s horror films "Frankenstein" and "Bride of Frankenstein."
  • D. George Brent
    George Brent was an Irish-American leading man of 1930s and 1940s Hollywood cinema, known for his suave screen presence opposite stars like Bette Davis.
  • E. Jack Hawkins
    Jack Hawkins was a distinguished British actor known for his commanding presence in mid-20th-century war and historical films.
  • 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_69ca82baad008190ab2859712b9b1607 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb432bcb648190b5ddbcc2a3dbc9b1 completed March 31, 2026, 3:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc9433a5848190aac09a2589061b53 completed April 1, 2026, 3:42 a.m.
Created at: March 30, 2026, 5:32 p.m.