Triple

T1159606
Position Surface form Disambiguated ID Type / Status
Subject Manufacturing Belt E24463 entity
Predicate includesCity P3207 FINISHED
Object Buffalo E22106 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: Buffalo | Statement: [Manufacturing Belt, includesCity, Buffalo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buffalo
Context triple: [Manufacturing Belt, includesCity, Buffalo]
  • A. Buffalo chosen
    Buffalo is a major city in western New York State known for its industrial history, proximity to Niagara Falls, and namesake Buffalo-style chicken wings.
  • B. Rochester
    Rochester is a historic cathedral city and former market town in Kent, England, known for its Norman castle, Romanesque cathedral, and strong associations with the novelist Charles Dickens.
  • C. Rochester
    Rochester is a major city in western New York State known historically for its role in industry, photography, and social reform movements.
  • D. Rochester
    Rochester is a small historic town in southeastern Massachusetts known for its rural character and New England charm.
  • E. Rochester
    Rochester is a small borough in western Pennsylvania situated along the Ohio River in Beaver County.
  • 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_69a494060e148190abb42f971242c197 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bcad47a08190895769611798f67f completed March 1, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69ae891551c8819090f6edd70c45ff39 completed March 9, 2026, 8:47 a.m.
Created at: March 1, 2026, 7:45 p.m.