Triple

T1121791
Position Surface form Disambiguated ID Type / Status
Subject Pacific Islands E24627 entity
Predicate hasPart P35 FINISHED
Object Japan E174 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: Japan | Statement: [Pacific Islands, hasPart, Japan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Japan
Context triple: [Pacific Islands, hasPart, Japan]
  • A. Japan chosen
    Japan is an East Asian island nation in the Pacific Ocean known for its advanced technology, rich cultural heritage, and major cities such as Tokyo, Osaka, and Kyoto.
  • B. Honshu
    Honshu is the largest and most populous island of Japan, home to major cities such as Tokyo, Osaka, and Kyoto.
  • C. South Korea
    South Korea is an East Asian nation on the southern half of the Korean Peninsula, known for its advanced technology, vibrant pop culture, and rapid economic development.
  • D. Fujinomiya, Japan
    Fujinomiya, Japan is a city in Shizuoka Prefecture known as a gateway to Mount Fuji and for its scenic views, shrines, and local cuisine.
  • E. Oppama, Japan
    Oppama, Japan is an industrial coastal district in Yokosuka, Kanagawa Prefecture, best known for its major Nissan automobile manufacturing plant.
  • 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_69a4940712c88190aa244f3fc6070a65 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bbbf71188190b82c8fff9d5ac01a completed March 1, 2026, 10:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac538a5d248190bb44c6b27d2c714c completed March 7, 2026, 4:34 p.m.
Created at: March 1, 2026, 7:44 p.m.