Triple
T13366397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Norikura |
E318948
|
entity |
| Predicate | nearestCity |
P350
|
FINISHED |
| Object | Takayama |
—
|
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: Takayama | Statement: [Mount Norikura, nearestCity, Takayama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Takayama Context triple: [Mount Norikura, nearestCity, Takayama]
-
A.
Takayama
chosen
Takayama is a historic mountain city in Japan’s Gifu Prefecture, known for its well-preserved Edo-period streets, traditional wooden houses, and proximity to the Japanese Alps.
-
B.
Matsumoto
Matsumoto is a historic city in central Japan best known for its well-preserved Matsumoto Castle and as a gateway to the scenic Japanese Alps.
-
C.
Kōka
Kōka is a city in Shiga Prefecture, Japan, historically famous as the home of the Kōga ninja tradition.
-
D.
Kusatsu
Kusatsu is a Japanese city in Shiga Prefecture known as a regional commercial hub and transportation crossroads near Lake Biwa.
-
E.
Kitanagoya
Kitanagoya is a city in central Japan known as a residential and commercial suburb within the Nagoya metropolitan area.
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadcd652d48190a782fd1f57f34b6a |
completed | April 11, 2026, 11:44 p.m. |
Created at: April 9, 2026, 9:32 p.m.