Triple
T18024969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akagi mountain lakes |
E431221
|
entity |
| Predicate | accessFrom |
P1985
|
FINISHED |
| Object | Numata |
—
|
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: Numata | Statement: [Akagi mountain lakes, accessFrom, Numata]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Numata Context triple: [Akagi mountain lakes, accessFrom, Numata]
-
A.
Numata
chosen
Numata is a city in Gunma Prefecture, Japan, known as a gateway to the Mount Akagi and Oze National Park areas.
-
B.
Numata
Numata is a small town located in Kamikawa Subprefecture on Japan’s northern island of Hokkaido.
-
C.
Yanaoca
Yanaoca is a small Andean town in southern Peru that serves as the administrative and commercial center of Canas Province in the Cusco Region.
-
D.
Ubajara
Ubajara is a small Brazilian municipality in the state of Ceará, known for its location in the Serra da Ibiapaba highlands and for the nearby Ubajara National Park with its caves and waterfalls.
-
E.
Okitipupa
Okitipupa is a prominent town in southwestern Nigeria known as a commercial and administrative center within Ondo State.
- 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_69d8b9050fb48190890155145deb0a66 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9c554348190bd0df06d0cfe188e |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 10:24 a.m.