Triple

T13510318
Position Surface form Disambiguated ID Type / Status
Subject Mount Ōminakami E321121 entity
Predicate municipality P852 FINISHED
Object Minakami, Gunma E555451 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: Minakami, Gunma | Statement: [Mount Ōminakami, municipality, Minakami, Gunma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Minakami, Gunma
Context triple: [Mount Ōminakami, municipality, Minakami, Gunma]
  • A. Numata, Gunma
    Numata, Gunma is a city in Gunma Prefecture, Japan, known for its mountainous scenery, hot springs, and proximity to natural attractions such as lakes and ski areas.
  • B. Minakami chosen
    Minakami is a mountainous town in Gunma Prefecture, Japan, known for its hot springs, outdoor sports, and scenic natural landscapes.
  • C. Hanamaki
    Hanamaki is a city in northeastern Japan known for its hot springs, rural landscapes, and as the birthplace of writer Kenji Miyazawa.
  • D. Kitakami, Iwate
    Kitakami, Iwate is a city in Iwate Prefecture, Japan, known for its scenic Kitakami River, springtime cherry blossoms, and historical cultural sites.
  • E. Tokamachi, Niigata
    Tokamachi, Niigata is a city in Niigata Prefecture, Japan, known for its heavy snowfall, traditional Echigo-jofu textiles, and scenic rural landscapes.
  • 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_69d807629d6c8190998f1b9bb12d2ed0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaf86a6208190be8c18f7a0158f23 completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75490291c8190b5985d8c90ef1af6 completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:43 p.m.