Triple

T21413584
Position Surface form Disambiguated ID Type / Status
Subject A Yank at Eton E528239 entity
Predicate screenwriter P2831 FINISHED
Object George Seaton NE NERFINISHED

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: George Seaton | Statement: [A Yank at Eton, screenwriter, George Seaton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George Seaton
Context triple: [A Yank at Eton, screenwriter, George Seaton]
  • A. George Seaton chosen
    George Seaton was an American screenwriter, director, and producer best known for films such as "Miracle on 34th Street" and "Airport."
  • B. Thomas Mayne
    Thomas Mayne was an Australian food scientist best known for creating the chocolate malted milk drink Milo in the 1930s.
  • C. Edward Kidner
    Edward Kidner is the wealthy, terminally ill industrialist in the science fiction film "Self/less" whose consciousness is transferred into a younger body, setting off the movie’s central ethical and existential conflicts.
  • D. John Seaton
    John Seaton is a fictional character from the 1978 British-American sports drama film "International Velvet."
  • E. Walter Boyd
    Walter Boyd is a former Jamaican international footballer best known for his prolific goal-scoring and charismatic playing style as a forward in the 1990s and early 2000s.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c454c248819093425d1099101c09 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e8b201ce1481908392c77e5ca40f5f completed April 22, 2026, 11:33 a.m.
Created at: April 16, 2026, 5:44 p.m.