Triple
T5993911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arima Onsen |
E133418
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Kobe City |
E499546
|
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: Kobe City | Statement: [Arima Onsen, partOf, Kobe City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kobe City Context triple: [Arima Onsen, partOf, Kobe City]
-
A.
Kobe
Kobe is a major port city in Japan’s Kansai region, known for its scenic harbor setting, cosmopolitan atmosphere, and famous Kobe beef.
-
B.
Osaka
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.
-
C.
Kobe, Hyogo, Japan
chosen
Kobe, Hyogo, Japan is a major port city in western Japan known for its international trade, scenic harbor setting between mountains and sea, and famous Kobe beef.
-
D.
Sakai, Osaka
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.
-
E.
Yokohama
Yokohama is Japan’s second-largest city and a major international port located just south of Tokyo.
- 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_69c00870ddbc81909880fa3864f4f38d |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04e92e1448190bbf961a8243082ee |
completed | March 22, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11cd900a48190b5aa83c28b3dfc1a |
completed | March 23, 2026, 10:58 a.m. |
Created at: March 22, 2026, 4:05 p.m.