Triple

T21299539
Position Surface form Disambiguated ID Type / Status
Subject Uguisudani Station E525017 entity
Predicate servedArea P82 FINISHED
Object Taito ward 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: Taito ward | Statement: [Uguisudani Station, servedArea, Taito ward]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taito ward
Context triple: [Uguisudani Station, servedArea, Taito ward]
  • A. Taitō ward chosen
    Taitō ward is a central Tokyo district known for its historic neighborhoods, traditional temples, and popular tourist areas such as Asakusa and Ueno.
  • B. Majohe Ward
    Majohe Ward is an administrative ward within Dar es Salaam, Tanzania, known as a residential and semi-urban area of Ilala District.
  • C. Kaiapoi Ward
    Kaiapoi Ward is an electoral subdivision within New Zealand’s Waimakariri District, centered on the town of Kaiapoi and its surrounding communities.
  • D. Kisutu Ward
    Kisutu Ward is an administrative ward and urban neighborhood located within the central area of Dar es Salaam, Tanzania.
  • E. Aoi Ward
    Aoi Ward is a central administrative district of Shizuoka City in Japan, known for encompassing the city’s downtown and governmental areas.
  • 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_69e0b517e6748190850d6f6ddf323d69 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e7385b1c548190b940ded0163ee3ca completed April 21, 2026, 8:42 a.m.
Created at: April 16, 2026, 4:05 p.m.