Triple
T13494528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naha Haarii |
E320724
|
entity |
| Predicate | locatedIn |
P40
|
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: [Naha Haarii, locatedIn, Naha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naha Context triple: [Naha Haarii, locatedIn, 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf4da2c88190a867b53529d39545 |
completed | April 12, 2026, 2:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7463d3a948190aab07a25fd903d4e |
completed | May 3, 2026, 12:57 p.m. |
Created at: April 9, 2026, 9:43 p.m.