Triple

T1310730
Position Surface form Disambiguated ID Type / Status
Subject Tokyo Prefecture E27983 entity
Predicate contains P35 FINISHED
Object Tama area E38268 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: Tama area | Statement: [Tokyo Prefecture, contains, Tama area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tama area
Context triple: [Tokyo Prefecture, contains, Tama area]
  • A. Geita Region
    Geita Region is an administrative region in northwestern Tanzania, known for its significant gold mining activities and proximity to Lake Victoria.
  • B. Los Pelambres area
    The Los Pelambres area is a major Chilean copper-mining district in the Andean highlands of Choapa Province, known for hosting one of the country’s largest open-pit copper mines.
  • C. Tama chosen
    Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
  • D. Nam District
    Nam District is an administrative district (gu) of the metropolitan city of Busan in South Korea, known for its coastal location and urban residential areas.
  • E. Kuse District
    Kuse District is a rural administrative district located in Kyoto Prefecture, Japan, known for its small towns and agricultural landscapes.
  • 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_69a496d7d83481908f83085854e51328 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c15490a88190872c3d2698a8f9c9 completed March 1, 2026, 10:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69acb30b45708190aaf8c977fef2500c completed March 7, 2026, 11:21 p.m.
Created at: March 1, 2026, 7:51 p.m.