Triple

T14002037
Position Surface form Disambiguated ID Type / Status
Subject Lord Lieutenant of Rutland E336849 entity
Predicate appliesToJurisdiction P82 FINISHED
Object Rutland E86840 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: Rutland | Statement: [Lord Lieutenant of Rutland, appliesToJurisdiction, Rutland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rutland
Context triple: [Lord Lieutenant of Rutland, appliesToJurisdiction, Rutland]
  • A. Rutland
    Rutland is an unincorporated community located in Bibb County, Georgia, United States.
  • B. Rutland
    Rutland is a small city in central Vermont known historically as a marble quarrying center and as a regional hub for commerce and outdoor recreation.
  • C. Rutland chosen
    Rutland is a small historic county in the East Midlands of England, known for its rural character and Rutland Water reservoir.
  • D. Rutland
    Rutland is a small town in Worcester County, Massachusetts, known for its rural character and location near the geographic center of the state.
  • E. Berkshire
    Berkshire is a historic county in South East England known for its royal connections, including Windsor Castle, and its mix of affluent towns and rural landscapes.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2ed06a50819093ddc64f55050689 completed April 14, 2026, 12:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbaca180988190bbfc93bd708688d6 completed May 6, 2026, 9:03 p.m.
Created at: April 9, 2026, 10:19 p.m.