Triple

T3014403
Position Surface form Disambiguated ID Type / Status
Subject Tone River E82300 entity
Predicate nearCity 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: [Tone River, nearCity, Maebashi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maebashi
Context triple: [Tone River, nearCity, 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_69ad8b1eb53481908c39bbcd1ec104b2 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9a69e8148190a97507740c9d26a8 completed March 8, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7daed5d188190b499151e636d206d completed March 28, 2026, 1:43 p.m.
Created at: March 8, 2026, 3 p.m.