Triple

T14933506
Position Surface form Disambiguated ID Type / Status
Subject Susukino district E372330 entity
Predicate governingBody P46 FINISHED
Object City of Sapporo E40366 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: City of Sapporo | Statement: [Susukino district, governingBody, City of Sapporo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Sapporo
Context triple: [Susukino district, governingBody, City of Sapporo]
  • A. Teine-ku, Sapporo
    Teine-ku, Sapporo is one of the western wards of Sapporo, Japan, known for its residential areas and the nearby Mount Teine ski and recreation facilities.
  • B. Sapporo chosen
    Sapporo is the capital and largest city of Japan’s northern Hokkaido prefecture, known for its annual snow festival, beer, and ski resorts.
  • C. Kiyota-ku, Sapporo
    Kiyota-ku, Sapporo is one of the administrative wards of the city of Sapporo in Hokkaido, Japan, known primarily as a residential area with parks and suburban-style neighborhoods.
  • D. Muroran, Hokkaido
    Muroran, Hokkaido is an industrial and port city in southern Hokkaido, Japan, known for its steel industry, coastal scenery, and role as a key maritime hub.
  • E. Fuji City
    Fuji City is an industrial city in Shizuoka Prefecture, Japan, known for its paper manufacturing industry and views of nearby Mount Fuji.
  • 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_69d85cc9da0c81908d583ca3f63a3908 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded646a0808190ba5c0c91bde011c5 completed April 15, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff133571008190b7e7867208095b90 completed May 9, 2026, 10:57 a.m.
Created at: April 10, 2026, 2:37 a.m.