Triple

T15484315
Position Surface form Disambiguated ID Type / Status
Subject Ishiteji E377003 entity
Predicate locatedIn P40 FINISHED
Object Matsuyama E202029 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: Matsuyama | Statement: [Ishiteji, locatedIn, Matsuyama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matsuyama
Context triple: [Ishiteji, locatedIn, Matsuyama]
  • A. Matsuyama chosen
    Matsuyama is a major city on Japan’s Shikoku Island, known for its historic Dōgo Onsen hot spring and Matsuyama Castle.
  • B. Aioi
    Aioi is a city in Hyōgo Prefecture, Japan, known for its coastal location along the Seto Inland Sea and its traditional fishing and maritime industries.
  • C. Fujinomiya
    Fujinomiya is a city in Shizuoka Prefecture, Japan, known as a major gateway to Mount Fuji and for its scenic views of the iconic volcano.
  • D. Ichinoseki
    Ichinoseki is a city in northeastern Japan known as a gateway to the scenic and historic sites of southern Iwate Prefecture.
  • E. Maebashi
    Maebashi is the capital city of Gunma Prefecture in Japan, known as a regional administrative and commercial center on the Kantō Plain.
  • 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_69d85cd21dcc81908646251b1c26ea00 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f8e6ff08190b130b3a38f4190e7 completed April 16, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0084a6d6308190ad57a51b380171a2 completed May 10, 2026, 1:14 p.m.
Created at: April 10, 2026, 3:45 a.m.