Triple

T588887
Position Surface form Disambiguated ID Type / Status
Subject Third Council of Toledo E17222 entity
Predicate place P373 FINISHED
Object Toledo E12295 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: [Third Council of Toledo, place, Toledo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toledo
Context triple: [Third Council of Toledo, place, Toledo]
  • A. Toledo chosen
    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
    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_69a49379d09c8190ac7e00b24e2810b1 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49b9d1e68819096a9b5e7b2e83d6e completed March 1, 2026, 8:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5103be4b881908fcd20c4c781c0a0 completed March 2, 2026, 4:21 a.m.
Created at: March 1, 2026, 7:33 p.m.