Triple

T4888318
Position Surface form Disambiguated ID Type / Status
Subject St Hugh’s College, Oxford E109494 entity
Predicate hasAlumnus P51 FINISHED
Object Joan Bakewell E93399 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: Joan Bakewell | Statement: [St Hugh’s College, Oxford, hasAlumnus, Joan Bakewell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Joan Bakewell
Context triple: [St Hugh’s College, Oxford, hasAlumnus, Joan Bakewell]
  • A. Joan Bakewell chosen
    Joan Bakewell is a British journalist, broadcaster, and writer renowned for her long career in television and radio and her influential commentary on culture and public affairs.
  • B. Margery Sharp
    Margery Sharp was a British author best known for her children's novels, particularly the series about heroic mice that inspired Disney's animated film "The Rescuers."
  • C. Rosemary Harris
    Rosemary Harris is a British actress acclaimed for her extensive stage and screen career, including a Tony Award win and an Academy Award nomination.
  • D. Doreen Brett
    Doreen Brett was the wife of British comedian and actor Norman Wisdom.
  • E. Rosemary Leith
    Rosemary Leith is a Canadian-born entrepreneur and internet governance leader who co-founded the World Wide Web Foundation and serves on various boards related to technology and public policy.
  • 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_69bd440f71348190b99938e59fb7f9a1 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6e053db8819087828e753c78d341 completed March 20, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69be68126b288190889b2cf6e400ec0b completed March 21, 2026, 9:42 a.m.
Created at: March 20, 2026, 1:28 p.m.