Triple

T5013186
Position Surface form Disambiguated ID Type / Status
Subject John Jones E112675 entity
Predicate usedBy P260 FINISHED
Object John Darwin E421559 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 Darwin | Statement: [John Jones, usedBy, John Darwin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Darwin
Context triple: [John Jones, usedBy, John Darwin]
  • A. John Darwin chosen
    John Darwin is a British former teacher and prison officer who infamously faked his own death in a canoeing accident in 2002 as part of an insurance fraud scheme.
  • B. John Darwin
    John Darwin is a British historian and academic known for his influential work on the history of empires, particularly the British Empire and global imperialism.
  • C. Ian Darwin
    Ian Darwin is a software developer and author best known for his contributions to Unix, Java, and open source programming resources.
  • D. Geoffrey Simpson
    Geoffrey Simpson is an Australian cinematographer known for his work on feature films including the 1994 adaptation of "Little Women."
  • E. Mark Darwin
    Mark Darwin is one of the two sons of British former teacher and convicted fraudster John Darwin, who became widely known due to his father's infamous "canoe man" disappearance and insurance scam.
  • 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_69bd4434acb8819086679dbeccc2fe54 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd730f12a481908a27c15dc73987c6 completed March 20, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69be9c5b09688190a0bc602396042e3f completed March 21, 2026, 1:25 p.m.
Created at: March 20, 2026, 1:35 p.m.