Triple

T14592853
Position Surface form Disambiguated ID Type / Status
Subject Back Award E342486 entity
Predicate hasRecipient P108 FINISHED
Object Peter Haggett E1108947 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: Peter Haggett | Statement: [Back Award, hasRecipient, Peter Haggett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Haggett
Context triple: [Back Award, hasRecipient, Peter Haggett]
  • A. Peter Haggett chosen
    Peter Haggett is a prominent British geographer known for his influential work in human and medical geography and for helping to shape modern spatial analysis in the discipline.
  • B. Geoffrey Hutchings
    Geoffrey Hutchings was a British character actor known for his work in film, television, and theatre, often playing sharp-tongued or eccentric supporting roles.
  • C. Steven Hoggett
    Steven Hoggett is a renowned British movement director and choreographer known for his innovative work in theatre and musical productions on the West End and Broadway.
  • D. Stephen Hague
    Stephen Hague is an American record producer and songwriter best known for his work on influential synth-pop and alternative rock albums from the 1980s and 1990s.
  • E. Peter Pugh
    Peter Pugh is a British author and publisher best known for writing corporate and business histories, including works on major companies and institutions.
  • 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_69d822ddc0f081909cd8163c7de298cd completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb43480d8819084a707e56da2c237 completed April 14, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fda918542c819099646943be59dd04 completed May 8, 2026, 9:12 a.m.
Created at: April 10, 2026, 1:24 a.m.