Triple

T1159611
Position Surface form Disambiguated ID Type / Status
Subject Manufacturing Belt E24463 entity
Predicate includesCity P3207 FINISHED
Object Toledo E25661 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: Toledo | Statement: [Manufacturing Belt, includesCity, Toledo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toledo
Context triple: [Manufacturing Belt, includesCity, Toledo]
  • A. Toledo
    Toledo is a historic Spanish city renowned for its medieval architecture, cultural heritage, and role as a major political and religious center in Spain’s history.
  • B. Toledo chosen
    Toledo is a major city in northwestern Ohio, known as an industrial and transportation hub on the western end of Lake Erie.
  • C. Columbus, Ohio
    Columbus, Ohio is the capital and largest city of Ohio, known for its diverse economy, major universities, and role as a cultural and political center in the region.
  • D. Canton
    Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
  • E. Dayton
    Dayton is an unincorporated community and census-designated place located within South Brunswick Township in Middlesex County, New Jersey.
  • 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_69ac5ebbf80881909010e1e1e59212d4 completed March 7, 2026, 5:22 p.m.
Created at: March 1, 2026, 7:45 p.m.