Triple

T16461752
Position Surface form Disambiguated ID Type / Status
Subject Jon Sopel E399822 entity
Predicate name P16 FINISHED
Object Jon Sopel E399822 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: Jon Sopel | Statement: [Jon Sopel, name, Jon Sopel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jon Sopel
Context triple: [Jon Sopel, name, Jon Sopel]
  • A. Jon Sopel chosen
    Jon Sopel is a British journalist and broadcaster best known for his work as a BBC correspondent and news presenter.
  • B. Luke Harding
    Luke Harding is a British journalist and author known for his investigative reporting on Russia, espionage, and international affairs, including works that inspired the film "The Fifth Estate."
  • C. Nick Davies
    Nick Davies is a British investigative journalist best known for exposing the News of the World phone-hacking scandal and his extensive work on media ethics and corruption.
  • D. Mark Bazeley
    Mark Bazeley is a British actor known for his work in film, television, and theatre, including roles in political dramas and high-profile UK series.
  • E. Phil Beauman
    Phil Beauman is an American comedy writer and producer best known for his work on parody films and television, including contributions to the hit spoof franchise "Scary Movie."
  • 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_69d87f2dac988190b74d6e185fa88ba4 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32d819d548190bc76a0ec2e223437 completed April 18, 2026, 7:06 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00679ecf4c819096e7f698b81fe25a completed May 10, 2026, 11:10 a.m.
Created at: April 10, 2026, 5:10 a.m.