Triple
T22308086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koyasan |
E551437
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Koya, Wakayama |
—
|
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: Koya, Wakayama | Statement: [Koyasan, nearCity, Koya, Wakayama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koya, Wakayama Context triple: [Koyasan, nearCity, Koya, Wakayama]
-
A.
Koyasan, Wakayama Prefecture
chosen
Koyasan in Wakayama Prefecture is a major Buddhist monastic complex and sacred mountain plateau that serves as the spiritual center of Shingon Buddhism in Japan.
-
B.
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.
-
C.
Kameoka
Kameoka is a city in Kyoto Prefecture, Japan, known for its rural landscapes, historical sites, and proximity to Kyoto.
-
D.
Teshikaga
Teshikaga is a town in eastern Hokkaido, Japan, known for its volcanic landscapes, including Lake Mashu and Lake Kussharo within Akan-Mashu National Park.
-
E.
Konan, Shiga
Konan, Shiga is a city in Japan known for its rural landscapes, historical temples, and location in the southern part of Shiga Prefecture.
- 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_69e11e46c0188190800181a4233f28fe |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1574c8a248190bf5eef5be78381fd |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.