Triple
T8337495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uraga Channel |
E195824
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Yokosuka |
E281970
|
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: Yokosuka | Statement: [Uraga Channel, nearCity, Yokosuka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yokosuka Context triple: [Uraga Channel, nearCity, Yokosuka]
-
A.
Yokosuka
chosen
Yokosuka is a coastal city in Kanagawa Prefecture, Japan, known for its major naval base and strategic location at the mouth of Tokyo Bay.
-
B.
Tachikawa
Tachikawa is a major city in western Tokyo, Japan, known as a key commercial and transportation hub of the Tama region.
-
C.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
-
D.
Kawanishi
Kawanishi is a city in Hyōgo Prefecture, Japan, known as a residential and commuter town within the Osaka metropolitan area.
-
E.
Daigo
Daigo was the era name (nengō) in Japanese history corresponding to the reign of Emperor Daigo in the early 10th century.
- 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_69ca82ecbdc481908a55cad8ca062d88 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fd5027c81909724f25aa30bbe58 |
completed | March 31, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdb70fbf881909daefb3162ba803c |
completed | April 3, 2026, 3:23 p.m. |
Created at: March 30, 2026, 5:57 p.m.