Triple

T4336657
Position Surface form Disambiguated ID Type / Status
Subject Lake Ōnuma E97478 entity
Predicate nearestCity P350 FINISHED
Object Maebashi E289472 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: Maebashi | Statement: [Lake Ōnuma, nearestCity, Maebashi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maebashi
Context triple: [Lake Ōnuma, nearestCity, Maebashi]
  • A. Maebashi chosen
    Maebashi is the capital city of Gunma Prefecture in Japan, known as a regional administrative and commercial center on the Kantō Plain.
  • B. Takasaki
    Takasaki is a city in Japan’s Gunma Prefecture known for its Daruma doll production and as a regional commercial and transportation hub.
  • C. Akishima
    Akishima is a city in western Tokyo, Japan, known as part of the Tama area and characterized by its residential neighborhoods and light industry.
  • D. Tokorozawa
    Tokorozawa is a commuter city in the Greater Tokyo area of Japan, known for its residential neighborhoods, aviation history, and role as a transport hub in southern Saitama.
  • E. Utsunomiya
    Utsunomiya is a city in Tochigi Prefecture, Japan, known as a regional commercial center and for its specialty gyoza (dumplings).
  • 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_69b3454662a481908fbcd0bbfaa3a0a4 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3516af43081908393fd0dad3d9382 completed March 12, 2026, 11:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c9a9ca03ec8190859d9728fef39d24 completed March 29, 2026, 10:38 p.m.
Created at: March 12, 2026, 11:14 p.m.