Triple

T14577869
Position Surface form Disambiguated ID Type / Status
Subject Kyotanabe Campus E342103 entity
Predicate locatedIn P40 FINISHED
Object Kyōtanabe NE NERFINISHED

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: Kyōtanabe | Statement: [Kyotanabe Campus, locatedIn, Kyōtanabe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kyōtanabe
Context triple: [Kyotanabe Campus, locatedIn, Kyōtanabe]
  • A. Kyotanabe chosen
    Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
  • B. Kitanagoya
    Kitanagoya is a city in central Japan known as a residential and commercial suburb within the Nagoya metropolitan area.
  • C. Koshigaya
    Koshigaya is a suburban city in Japan known for its large shopping complexes and residential communities within the Greater Tokyo metropolitan area.
  • D. Shiraoi
    Shiraoi is a coastal town in Hokkaido, Japan, known for its Ainu cultural heritage and natural hot springs.
  • E. Higashikawa
    Higashikawa is a town in Hokkaido, Japan, known as a gateway to the Daisetsuzan mountain range and for its scenic natural landscapes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d822dcc6248190bed689984bceb0e2 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb3f5ec448190b2ef887fdf7b633e completed April 14, 2026, 9:39 p.m.
Created at: April 10, 2026, 1:24 a.m.