Triple

T7417208
Position Surface form Disambiguated ID Type / Status
Subject Neyagawa E171159 entity
Predicate borderedBy P224 FINISHED
Object Shijonawate E412031 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: Shijonawate | Statement: [Neyagawa, borderedBy, Shijonawate]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shijonawate
Context triple: [Neyagawa, borderedBy, Shijonawate]
  • A. Tatsuno
    Tatsuno is a city in western Japan known for its traditional soy sauce production and historic townscape within Hyogo Prefecture.
  • B. Ishkashimi
    Ishkashimi is a lesser-known Eastern Iranian language spoken by small communities in parts of Afghanistan and Tajikistan.
  • C. Kakogawa
    Kakogawa is an industrial and residential city in central Hyōgo Prefecture, Japan, known for its steel manufacturing and role as a regional transportation hub.
  • D. Osakasayama chosen
    Osakasayama is a suburban city in Osaka Prefecture, Japan, known for its residential character and proximity to the Osaka metropolitan area.
  • E. Kameyama
    Kameyama is a city in Mie Prefecture, Japan, known historically as a post town on the Tōkaidō and for its preserved castle ruins and traditional streetscapes.
  • 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_69c68a618bdc81908d8018edadecd1a4 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f2c7ae0c8190a8348d6223aeeecc completed March 27, 2026, 9:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69cfc9129bec8190b3c1471ac8a131ac completed April 3, 2026, 2:05 p.m.
Created at: March 27, 2026, 3:11 p.m.