Triple

T4013680
Position Surface form Disambiguated ID Type / Status
Subject John Nathan-Turner E90704 entity
Predicate name P16 FINISHED
Object John Nathan-Turner E90704 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: John Nathan-Turner | Statement: [John Nathan-Turner, name, John Nathan-Turner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Nathan-Turner
Context triple: [John Nathan-Turner, name, John Nathan-Turner]
  • A. John Nathan-Turner chosen
    John Nathan-Turner was a British television producer best known as the longest-serving producer of the classic science fiction series Doctor Who during the 1980s.
  • B. Nicholas Briggs
    Nicholas Briggs is a British actor and voice artist best known for providing the iconic voices of the Daleks and other monsters in the Doctor Who franchise.
  • C. John Paddy Carstairs
    John Paddy Carstairs was a British film director and screenwriter best known for his work on mid-20th-century comedies, including several films starring Norman Wisdom.
  • D. Ray Henderson
    Ray Henderson was a prominent American songwriter and composer of popular music and Broadway tunes during the 1920s and 1930s.
  • E. Nigel Terry
    Nigel Terry was an English actor best known for his intense, character-driven performances in historical and period films and on stage.
  • 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_69aed95e44088190aff7d90a151b1b20 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa8ad6348190b71feaf8c18c90c2 completed March 9, 2026, 4:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b54c73e6048190a59a8d8bc12c907d completed March 14, 2026, 11:54 a.m.
Created at: March 9, 2026, 3:35 p.m.