Triple

T2963982
Position Surface form Disambiguated ID Type / Status
Subject Taiyuan E80116 entity
Predicate sisterCity P1072 FINISHED
Object Kawagoe E279257 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: Kawagoe | Statement: [Taiyuan, sisterCity, Kawagoe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kawagoe
Context triple: [Taiyuan, sisterCity, Kawagoe]
  • A. Kawagoe chosen
    Kawagoe is a historic Japanese city in Saitama Prefecture, often called "Little Edo" for its well-preserved Edo-period streetscapes and traditional warehouses.
  • B. Kameoka
    Kameoka is a city in Kyoto Prefecture, Japan, known for its rural landscapes, historical sites, and proximity to Kyoto.
  • C. Kyotanabe
    Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
  • D. Koshigaya
    Koshigaya is a suburban city in Japan known for its large shopping complexes and residential communities within the Greater Tokyo metropolitan area.
  • E. Kumagaya
    Kumagaya is a city in northern Saitama Prefecture, Japan, known for its hot summer temperatures and role as a regional commercial and transportation hub.
  • 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_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9958b1e48190a77f37bf63333c5b completed March 8, 2026, 3:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69bef7d61c988190ae814cef9e726ae6 completed March 21, 2026, 7:56 p.m.
Created at: March 8, 2026, 2:58 p.m.