Triple
T6816682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sakai City |
E156778
|
entity |
| Predicate | hasJapaneseName |
P9882
|
FINISHED |
| Object | 堺市 |
E416540
|
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: 堺市 | Statement: [Sakai City, hasJapaneseName, 堺市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 堺市 Context triple: [Sakai City, hasJapaneseName, 堺市]
-
A.
Wakayama City
Wakayama City is a coastal city in Japan known for its historic Wakayama Castle, scenic views over Wakayama Bay, and role as a regional commercial and cultural center in the Kansai area.
-
B.
Higashiōsaka
Higashiōsaka is an industrial and residential city in Japan known for its manufacturing base and location within the Osaka metropolitan area.
-
C.
Sakai, Osaka
chosen
Sakai, Osaka is a historic port city in Japan’s Osaka Prefecture, known for its ancient burial mounds, traditional craftsmanship, and role as a major commercial center.
-
D.
枚方市
枚方市は、大阪府北東部に位置し、淀川沿いに広がる住宅都市兼商業都市として発展している市です。
-
E.
Suita, Osaka
Suita, Osaka is a city in northern Osaka Prefecture, Japan, known as a major suburban and educational hub that hosts the main campus of Osaka University.
- 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_69c688298a288190af3f285d57f76bbe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d354177481908ab3cf5437c095e2 |
completed | March 27, 2026, 6:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c723e0c62c8190b3b3b092ea48d4c5 |
completed | March 28, 2026, 12:42 a.m. |
Created at: March 27, 2026, 2:17 p.m.