Triple

T1356223
Position Surface form Disambiguated ID Type / Status
Subject Episcopal Diocese of Vermont E28994 entity
Predicate locatedIn P40 FINISHED
Object Vermont E9978 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: Vermont | Statement: [Episcopal Diocese of Vermont, locatedIn, Vermont]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vermont
Context triple: [Episcopal Diocese of Vermont, locatedIn, Vermont]
  • A. Vermont chosen
    Vermont is a small, rural New England state in the northeastern United States, known for its Green Mountains, maple syrup production, and picturesque towns.
  • B. New Hampshire
    New Hampshire is a small New England state in the northeastern United States known for its mountainous landscapes, early presidential primary, and “Live Free or Die” motto.
  • C. Maine
    Maine is a northeastern U.S. state known for its rugged coastline, maritime history, and vast forested interior.
  • D. Maine
    Maine is a historical region in northwestern France that played a significant role in the medieval power struggles between the English and French crowns.
  • E. Warren, Vermont
    Warren, Vermont is a small New England town in the Mad River Valley known for its scenic mountain setting, outdoor recreation, and proximity to Sugarbush Resort.
  • 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_69a498571d248190a0ac9eb02d97097f completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c28c8dd0819082f94c9e7c837c5f completed March 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b333ef7f30819086b538cdc2cefe0f completed March 12, 2026, 9:45 p.m.
Created at: March 1, 2026, 7:56 p.m.