Triple

T405696
Position Surface form Disambiguated ID Type / Status
Subject Takatsuki E9377 entity
Predicate hasSisterCity P919 FINISHED
Object Toledo, Ohio 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, Ohio | Statement: [Takatsuki, hasSisterCity, Toledo, Ohio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toledo, Ohio
Context triple: [Takatsuki, hasSisterCity, Toledo, Ohio]
  • 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. Norwalk, Ohio
    Norwalk, Ohio is a small city in northern Ohio that serves as the county seat of Huron County and a regional hub for the surrounding rural communities.
  • E. Akron
    Akron is an industrial city in northeastern Ohio known historically for its rubber and tire manufacturing industry.
  • 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_69a2e8004cb88190b92ed1add6abf41a completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ecbc00508190bbb602179273f29c completed Feb. 28, 2026, 1:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69a44cb531548190b378444ccf7a6283 completed March 1, 2026, 2:27 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.