Triple
T11309791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 淀川 |
E267806
|
entity |
| Predicate | 通過都市 |
P98431
|
FINISHED |
| Object | 大阪市 |
E486
|
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: 大阪市 | Statement: [淀川, 通過都市, 大阪市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 大阪市 Context triple: [淀川, 通過都市, 大阪市]
-
A.
Osaka
chosen
Osaka is Japan's third-largest city and a major economic, cultural, and historical hub known for its vibrant street food, bustling nightlife, and role as a commercial center in the Kansai region.
-
B.
Settsu, Osaka
Settsu, Osaka is a suburban city in northern Osaka Prefecture, Japan, known for its residential neighborhoods and convenient access to the Osaka metropolitan area.
-
C.
神戸市
神戸市は、兵庫県の県庁所在地であり、国際貿易港や異国情緒あふれる街並みで知られる日本有数の港湾都市です。
-
D.
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.
-
E.
Higashiōsaka
Higashiōsaka is an industrial and residential city in Japan known for its manufacturing base and location within the Osaka metropolitan area.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9c0b3b88190ac0e3d6a5ad3b9bc |
completed | April 9, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e603bbb84c81908a29fcef32d3cf31 |
completed | April 20, 2026, 10:45 a.m. |
Created at: April 8, 2026, 9:32 p.m.