Triple

T15336101
Position Surface form Disambiguated ID Type / Status
Subject Bunkyō City E366668 entity
Predicate contains P35 FINISHED
Object Nezu district 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: Nezu district | Statement: [Bunkyō City, contains, Nezu district]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nezu district
Context triple: [Bunkyō City, contains, Nezu district]
  • A. Nezu district chosen
    Nezu district is a historic neighborhood in Tokyo, Japan, known for its traditional atmosphere, old temples and shrines, and preserved shitamachi (downtown) charm.
  • B. Ichigaya district
    Ichigaya district is a central Tokyo neighborhood known for its government and military institutions, educational facilities, and proximity to the Imperial Palace area.
  • C. Tsuzuki District
    Tsuzuki District is a former administrative district that once existed within Kyoto Prefecture in Japan.
  • D. Otsuka district
    Otsuka district is a neighborhood in Tokyo, Japan, known for its mix of residential streets, local shopping areas, and convenient rail access.
  • E. Ebisu district
    Ebisu district is a fashionable neighborhood in Tokyo known for its upscale dining, shopping, and nightlife, as well as its convenient access from central Shibuya.
  • 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_69d85a1355608190a6673ddb67231d54 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e03c5f081908e4d14dbdbc7f7a6 completed April 16, 2026, 1:40 a.m.
Created at: April 10, 2026, 3:17 a.m.