Triple

T14572011
Position Surface form Disambiguated ID Type / Status
Subject judicial district of Fukuoka High Court E341939 entity
Predicate hasBranchSeatCity P21613 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: [judicial district of Fukuoka High Court, hasBranchSeatCity, Naha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Naha
Context triple: [judicial district of Fukuoka High Court, hasBranchSeatCity, 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_69d822dcc6248190bed689984bceb0e2 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb3f33b1c8190bb447788bfd28d51 completed April 14, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8aca591081908db149ec517a999b completed May 8, 2026, 7:03 a.m.
Created at: April 10, 2026, 1:24 a.m.