Triple

T19982353
Position Surface form Disambiguated ID Type / Status
Subject Law & Order: UK E493846 entity
Predicate stars P1956 FINISHED
Object Peter Davison 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: Peter Davison | Statement: [Law & Order: UK, stars, Peter Davison]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Davison
Context triple: [Law & Order: UK, stars, Peter Davison]
  • A. Peter Davison chosen
    Peter Davison is a British actor best known for playing the Fifth Doctor in the long-running science fiction television series Doctor Who.
  • B. Sylvester McCoy
    Sylvester McCoy is a Scottish actor best known for playing the Seventh Doctor in the long-running British science fiction series "Doctor Who."
  • C. Tom Baker
    Tom Baker is a British actor best known for his iconic portrayal of the Fourth Doctor in the long-running science fiction television series "Doctor Who."
  • D. Tom Baker
    Tom Baker is the harried but loving father of twelve children in the family comedy film "Cheaper by the Dozen."
  • E. Jon Pertwee
    Jon Pertwee was a British actor best known for playing the Third Doctor in the long-running science fiction television series Doctor Who.
  • 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_69da626a67648190af9653832a3aeced completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e65d13a8a88190bf5f4f697793f4c9 completed April 20, 2026, 5:06 p.m.
Created at: April 11, 2026, 3:28 p.m.