Triple

T592797
Position Surface form Disambiguated ID Type / Status
Subject M25 motorway E17312 entity
Predicate passesNear P416 FINISHED
Object Buckinghamshire E10606 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: Buckinghamshire | Statement: [M25 motorway, passesNear, Buckinghamshire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buckinghamshire
Context triple: [M25 motorway, passesNear, Buckinghamshire]
  • A. Buckinghamshire chosen
    Buckinghamshire is a ceremonial and non-metropolitan county in South East England, known for its historic towns, Chiltern Hills countryside, and proximity to London.
  • B. Hertfordshire
    Hertfordshire is a county in southern England known for its historic market towns, countryside, and proximity to London.
  • C. Bedfordshire
    Bedfordshire is a ceremonial and non-metropolitan county in the East of England, known for its mix of rural countryside, market towns, and the large town of Luton.
  • D. Oxfordshire
    Oxfordshire is a historic county in South East England known for the city of Oxford and its prestigious university, as well as its stately homes and rural landscapes.
  • E. Northamptonshire
    Northamptonshire is a historic, landlocked county in the East Midlands of England known for its market towns, rural landscapes, and long association with the footwear and leather industries.
  • 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_69a49379d09c8190ac7e00b24e2810b1 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49bbcaf5c81908de4e27096d3da13 completed March 1, 2026, 8:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad292fbfe88190816bc5d4f0e56e9f completed March 8, 2026, 7:45 a.m.
Created at: March 1, 2026, 7:33 p.m.