Triple
T9048292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 伊丹市 |
E216813
|
entity |
| Predicate | borderWith |
P224
|
FINISHED |
| Object | 兵庫県川西市 |
E216816
|
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: 兵庫県川西市 | Statement: [伊丹市, borderWith, 兵庫県川西市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 兵庫県川西市 Context triple: [伊丹市, borderWith, 兵庫県川西市]
-
A.
Ichinomiya, Hyōgo
Ichinomiya, Hyōgo was a former town in Hyōgo Prefecture, Japan, that later became part of the city of Awaji through municipal consolidation.
-
B.
Kawanishi, Hyōgo Prefecture
chosen
Kawanishi is a suburban city in Hyōgo Prefecture, Japan, known as a residential and commuter town within the Osaka metropolitan area.
-
C.
Habikino, Osaka
Habikino is a city in Osaka Prefecture, Japan, historically notable for its large kofun burial mounds and ancient imperial tombs.
-
D.
Naniwa-ku, Osaka
Naniwa-ku, Osaka is a central ward of Osaka City known for its busy commercial districts, entertainment areas, and major transport hubs such as Namba.
-
E.
Kōka, Shiga Prefecture
Kōka, Shiga Prefecture is a rural city in Japan’s Kansai region known for its historic ninja heritage and scenic mountain landscapes.
- 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_69ca83d362e88190ae44b4e4dc194209 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6b51aa708190a37feecfd8deed2f |
completed | April 1, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfebc0fd648190b0dd6cf62605b98f |
completed | April 3, 2026, 4:33 p.m. |
Created at: March 30, 2026, 7:09 p.m.