Triple

T6313995
Position Surface form Disambiguated ID Type / Status
Subject Utsunomiya Station E141570 entity
Predicate locatedIn P40 FINISHED
Object Utsunomiya E598240 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: Utsunomiya | Statement: [Utsunomiya Station, locatedIn, Utsunomiya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Utsunomiya
Context triple: [Utsunomiya Station, locatedIn, Utsunomiya]
  • A. Utsunomiya chosen
    Utsunomiya is a city in Tochigi Prefecture, Japan, known as a regional commercial center and for its specialty gyoza (dumplings).
  • B. Omiya
    Omiya is a major commercial and transportation hub in Saitama Prefecture, Japan, known for its busy railway station and urban center.
  • C. Takasaki
    Takasaki is a city in Japan’s Gunma Prefecture known for its Daruma doll production and as a regional commercial and transportation hub.
  • D. 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.
  • 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_69c008d00efc8190a36c05b4b4a3bf4b completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c064a075dc8190acf7ec010cb4b00c completed March 22, 2026, 9:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1d583f75c819083cb0dcdf265d735 completed April 5, 2026, 3:22 a.m.
Created at: March 22, 2026, 4:28 p.m.