Triple

T4295752
Position Surface form Disambiguated ID Type / Status
Subject The Leys School E99706 entity
Predicate hasAlumnus P51 FINISHED
Object Sir Richard Dearlove E429688 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: Sir Richard Dearlove | Statement: [The Leys School, hasAlumnus, Sir Richard Dearlove]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sir Richard Dearlove
Context triple: [The Leys School, hasAlumnus, Sir Richard Dearlove]
  • A. Sir Richard Dearlove chosen
    Sir Richard Dearlove is a British intelligence officer best known for serving as Chief of the UK’s Secret Intelligence Service (MI6) from 1999 to 2004.
  • B. Sir John Trevor
    Sir John Trevor was a prominent 17th-century English politician and royal official who held several high offices under the Stuart monarchy.
  • C. Sir Michael Lyons
    Sir Michael Lyons is a British public servant and former chairman of the BBC Trust, known for his leadership roles in local government and public sector organizations.
  • D. Sir Clive Alderton
    Sir Clive Alderton is a senior British courtier and diplomat who serves as a key adviser and top administrative official to the UK monarch.
  • E. Reginald Gardiner
    Reginald Gardiner was a British-born actor and comedian known for his sophisticated comic roles in Hollywood films of the 1930s and 1940s.
  • 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_69b3455175088190aa79c6e03b86647e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35085b864819086cf726285384566 completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d073936c8190b48045f8370ea27f completed March 14, 2026, 9:17 p.m.
Created at: March 12, 2026, 11:08 p.m.