Triple
T22602966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshinoyama area |
E574878
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Kami Senbon |
—
|
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: Kami Senbon | Statement: [Yoshinoyama area, hasPart, Kami Senbon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kami Senbon Context triple: [Yoshinoyama area, hasPart, Kami Senbon]
-
A.
Kami Senbon
chosen
Kami Senbon is the upper cherry-blossom viewing area on Mount Yoshino in Nara Prefecture, Japan, renowned for its dense sakura trees and panoramic mountain vistas.
-
B.
Oku Senbon
Oku Senbon is the upper, more remote area of Mount Yoshino in Nara Prefecture, Japan, renowned for its dense, late-blooming cherry blossoms and scenic mountain vistas.
-
C.
Naka Senbon
Naka Senbon is a central cherry-blossom viewing area on Mount Yoshino in Nara Prefecture, Japan, known for its dense groves of sakura trees and scenic springtime landscapes.
-
D.
Shimo Senbon
Shimo Senbon is the lower area of Mount Yoshino famed for its dense groves of cherry trees that create spectacular spring blossom views.
-
E.
Kinboku-san
Kinboku-san is an alternative name for Kinpoku Mountain, a notable peak in Japan.
- 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_69e245bc11308190b69d794d5d1e0bb6 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1626eb178819096866d03a78f82fc |
completed | April 29, 2026, 1:44 a.m. |
Created at: April 17, 2026, 2:50 p.m.