Triple

T17531570
Position Surface form Disambiguated ID Type / Status
Subject River Churnet E426946 entity
Predicate nearbySettlement P350 FINISHED
Object Leek NE NERFINISHED

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: Leek | Statement: [River Churnet, nearbySettlement, Leek]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leek
Context triple: [River Churnet, nearbySettlement, Leek]
  • A. Leek chosen
    Leek is a historic market town in Staffordshire, England, known for its textile heritage and picturesque location near the Peak District.
  • B. Leek
    Leek is a former municipality in the Dutch province of Groningen, known for its rural character and historic estates, that was incorporated into the municipality of Westerkwartier.
  • C. Onions
    Onions is an English surname most notably associated with Charles Talbut Onions, a prominent lexicographer and editor of the Oxford English Dictionary.
  • D. Kale
    Kale is a town in Myanmar’s Sagaing Region, known as a local commercial and transport hub in the country’s northwest.
  • E. Kale
    Kale is a district-level administrative area within Turkey, known for its location in Malatya Province in the Eastern Anatolia region.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889de677081909b22d2657b1f0292 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e453688950819098162d853cd2674e completed April 19, 2026, 4 a.m.
Created at: April 10, 2026, 5:49 a.m.