Triple

T4401612
Position Surface form Disambiguated ID Type / Status
Subject Hart District E93629 entity
Predicate borderedBy P224 FINISHED
Object Bracknell Forest E61129 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: Bracknell Forest | Statement: [Hart District, borderedBy, Bracknell Forest]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bracknell Forest
Context triple: [Hart District, borderedBy, Bracknell Forest]
  • A. Bracknell Forest chosen
    Bracknell Forest is a unitary authority area and borough in Berkshire, South East England, encompassing the town of Bracknell and surrounding communities.
  • B. Bracknell
    Bracknell is a town in the English county of Berkshire, known as a post-war New Town and commercial centre in the Thames Valley.
  • C. Slough
    Slough is a large industrial and commercial town in southern England, known for its diverse population and proximity to London and Heathrow Airport.
  • D. Aylesbury
    Aylesbury is a historic market town in southern England that serves as an important commercial and administrative center in Buckinghamshire.
  • E. Welwyn Garden City
    Welwyn Garden City is a planned English town in Hertfordshire, founded by Ebenezer Howard in the early 20th century as a model community combining the benefits of city and countryside.
  • 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_69b345158c748190a2c040fce2da9980 completed March 12, 2026, 10:58 p.m.
NER Named-entity recognition batch_69b352cf218481908d072fec58361f28 completed March 12, 2026, 11:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6855bd42c8190a9fbebd5176d63fd completed March 27, 2026, 1:25 p.m.
Created at: March 12, 2026, 11:28 p.m.