Triple

T9723751
Position Surface form Disambiguated ID Type / Status
Subject Shinagawa Station E235547 entity
Predicate near P350 FINISHED
Object Konan district E475686 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: Konan district | Statement: [Shinagawa Station, near, Konan district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Konan district
Context triple: [Shinagawa Station, near, Konan district]
  • A. Kohlu District
    Kohlu District is an administrative district in the Balochistan province of Pakistan, known for its rugged terrain and predominantly Baloch population.
  • B. Miura District chosen
    Miura District is a rural administrative district in Kanagawa Prefecture, Japan, known for its coastal towns and scenic Miura Peninsula landscapes.
  • C. Kaifu District
    Kaifu District is an urban administrative district of Changsha, the capital city of Hunan Province in south-central China.
  • D. Senkawa district
    Senkawa district is a residential neighborhood in Tokyo, Japan, known for its convenient urban location and access to public transportation.
  • E. Ōshima District
    Ōshima District is an administrative district in Kagoshima Prefecture, Japan, encompassing several islands in the Amami archipelago, including Yoronjima.
  • 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_69ca84d0123c819096f9dc3b6abb0881 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9e77096481908ffd315fecb1d5ec completed April 1, 2026, 10:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1bcc45bfc81909b86d10598d9bd39 completed April 5, 2026, 1:37 a.m.
Created at: March 30, 2026, 8:21 p.m.