Triple

T13265479
Position Surface form Disambiguated ID Type / Status
Subject Nansei Islands E315911 entity
Predicate hasMajorCity P316 FINISHED
Object Naha E13218 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: Naha | Statement: [Nansei Islands, hasMajorCity, Naha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Naha
Context triple: [Nansei Islands, hasMajorCity, Naha]
  • A. Naha chosen
    Naha is the capital and largest city of Okinawa Prefecture in Japan, known as a major political, economic, and cultural center of the Ryukyu Islands.
  • B. Nago
    Nago is a coastal city in northern Okinawa, Japan, known for its beaches, subtropical climate, and role as a regional commercial and cultural center.
  • C. Hanam
    Hanam is a city in South Korea known for its rapid urban development and large shopping and residential complexes, located just east of Seoul in Gyeonggi Province.
  • D. Hatta
    Hatta is an Indonesian surname most prominently associated with Mohammad Hatta, the country’s first vice president and a leading figure in the struggle for independence.
  • E. Kihoku
    Kihoku is a town in Mie Prefecture, Japan, known for its coastal scenery and fishing industry along the Kumano Sea.
  • 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_69d806b1d9ac8190852c5571d5bd5f0f completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9901e44bc8190966f87ae219d6bf4 completed April 11, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69f70a4ad79c8190b1304942dc48c0ff completed May 3, 2026, 8:41 a.m.
Created at: April 9, 2026, 9:25 p.m.