Triple
T10819502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tōkyō-wan |
E255326
|
entity |
| Predicate | borders |
P224
|
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: [Tōkyō-wan, borders, Yokosuka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yokosuka Context triple: [Tōkyō-wan, borders, 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 was a Japanese aircraft manufacturer best known for producing military seaplanes and bombers for the Imperial Japanese Navy during World War II.
-
E.
Kawanishi
Kawanishi is a city in Hyōgo Prefecture, Japan, known as a residential and commuter town 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d734492be88190874ea0ba4d0fa643 |
completed | April 9, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f43ee693048190a8c7ecdf8724d3ec |
completed | May 1, 2026, 5:49 a.m. |
Created at: April 8, 2026, 9:18 p.m.