Triple

T17851901
Position Surface form Disambiguated ID Type / Status
Subject Ichikawa E445827 entity
Predicate borderedBy P224 FINISHED
Object Funabashi NE NERFINISHED

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: Funabashi | Statement: [Ichikawa, borderedBy, Funabashi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Funabashi
Context triple: [Ichikawa, borderedBy, Funabashi]
  • A. Funabashi chosen
    Funabashi is a major city in Chiba Prefecture, Japan, known as a residential and commercial hub within the Greater Tokyo metropolitan area.
  • B. Moriguchi
    Moriguchi is a city in Japan’s Kansai region that forms part of the Osaka metropolitan area and serves as a residential and commercial hub.
  • C. Yodoyabashi
    Yodoyabashi is a major business and commercial district in central Osaka, known for its financial institutions, offices, and convenient subway and rail connections.
  • D. Shiodome
    Shiodome is a modern high-rise business and commercial district in Tokyo, Japan, known for housing major corporate headquarters, upscale hotels, and shopping complexes.
  • E. Kagurazaka
    Kagurazaka is a historic neighborhood in central Tokyo known for its narrow cobblestone streets, traditional ryotei restaurants, and blend of old geisha district charm with modern boutiques and cafes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f26f18819089c9e43250bee6ae completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48fff6c288190a2b5e60b66c03ddc completed April 19, 2026, 8:19 a.m.
Created at: April 10, 2026, 10:17 a.m.