Triple
T21146040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nissan 350Z |
E521056
|
entity |
| Predicate | assemblyLocation |
P40
|
FINISHED |
| Object | Oppama, Japan |
—
|
NE NERFINISHED |
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: Oppama, Japan | Statement: [Nissan 350Z, assemblyLocation, Oppama, Japan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oppama, Japan Context triple: [Nissan 350Z, assemblyLocation, Oppama, Japan]
-
A.
Oppama, Japan
chosen
Oppama, Japan is an industrial coastal district in Yokosuka, Kanagawa Prefecture, best known for its major Nissan automobile manufacturing plant.
-
B.
Kawachinagano, Japan
Kawachinagano is a city in Osaka Prefecture, Japan, known for its scenic mountainous landscapes, historic temples, and role as a residential and commuter community near Osaka.
-
C.
Tokuyama, Japan
Tokuyama, Japan is a coastal industrial city in Yamaguchi Prefecture known historically for its port facilities and petrochemical industry.
-
D.
Fujinomiya, Japan
Fujinomiya, Japan is a city in Shizuoka Prefecture known as a gateway to Mount Fuji and for its scenic views, shrines, and local cuisine.
-
E.
Shinhidaka, Japan
Shinhidaka is a town in Hokkaido, Japan, known for its horse breeding industry and scenic rural landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b50c6a848190a4e525a77a319b8a |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e723fdc25481909d6648e09b069c41 |
completed | April 21, 2026, 7:15 a.m. |
Created at: April 16, 2026, 2:58 p.m.