Triple

T20858030
Position Surface form Disambiguated ID Type / Status
Subject Takasaki E513533 entity
Predicate hasLandmark P105 FINISHED
Object Takasaki City Hall 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: Takasaki City Hall | Statement: [Takasaki, hasLandmark, Takasaki City Hall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Takasaki City Hall
Context triple: [Takasaki, hasLandmark, Takasaki City Hall]
  • A. Takasaki City Hall chosen
    Takasaki City Hall is the main municipal government building and administrative center serving the city of Takasaki in Gunma Prefecture, Japan.
  • B. Tsuru City Hall
    Tsuru City Hall is the main municipal government building and administrative center serving the city of Tsuru in Japan.
  • C. Izumo City Hall
    Izumo City Hall is the main administrative building and local government headquarters serving the city of Izumo in Shimane Prefecture, Japan.
  • D. Kawaguchi City Hall
    Kawaguchi City Hall is the main municipal government building and administrative center serving the city of Kawaguchi in Saitama Prefecture, Japan.
  • E. Matsumoto City Hall
    Matsumoto City Hall is the main municipal government building and administrative center responsible for managing public services and local affairs in Matsumoto, Japan.
  • 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_69e0b4f5b01081909452f654d2fc3f50 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c3a9fd7881908ae4c5c63f64efc0 completed April 21, 2026, 12:24 a.m.
Created at: April 16, 2026, 12:44 p.m.