Triple
T1360921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Okinawan Japanese |
E29095
|
entity |
| Predicate | region |
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: [Okinawan Japanese, region, Naha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naha Context triple: [Okinawan Japanese, region, 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.
Kutaisi
Kutaisi is one of Georgia’s major cities, historically significant and formerly a capital, located in the western part of the country.
-
C.
Yanam
Yanam is a coastal town and district enclave of the Union Territory of Puducherry in India, historically influenced by French colonial rule and culturally linked to the Telugu-speaking region of Andhra Pradesh.
-
D.
Taif
Taif is a city in western Saudi Arabia known for its cool climate, rose cultivation, and historical significance as a summer resort and cultural center.
-
E.
Lapa
Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2b156b081909c99ada70a969fc0 |
completed | March 1, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acce725fec819085f6de8e6e368aa4 |
completed | March 8, 2026, 1:18 a.m. |
Created at: March 1, 2026, 7:56 p.m.