Triple
T13265481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nansei Islands |
E315911
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Nago |
E210306
|
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: Nago | Statement: [Nansei Islands, hasMajorCity, Nago]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nago Context triple: [Nansei Islands, hasMajorCity, Nago]
-
A.
Nago
chosen
Nago is a coastal city in northern Okinawa, Japan, known for its beaches, subtropical climate, and role as a regional commercial and cultural center.
-
B.
Nagaya
Nagaya is a Japanese surname historically borne by various notable figures, including samurai and aristocrats, and remains in use in modern Japan.
-
C.
Daigo
Daigo was the era name (nengō) in Japanese history corresponding to the reign of Emperor Daigo in the early 10th century.
-
D.
Miyakonojō
Miyakonojō is a city in Miyazaki Prefecture on Japan’s Kyushu island, known for its agriculture and livestock production.
-
E.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
- 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_69f716cac5388190a839ec1dbdcdbf82 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:25 p.m.