Triple
T22604161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zushi Beach |
E574910
|
entity |
| Predicate | hasViewOf |
P854
|
FINISHED |
| Object | Mount Fuji |
—
|
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: Mount Fuji | Statement: [Zushi Beach, hasViewOf, Mount Fuji]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mount Fuji Context triple: [Zushi Beach, hasViewOf, Mount Fuji]
-
A.
Mount Fuji
chosen
Mount Fuji is Japan’s iconic, snow-capped stratovolcano and highest peak, renowned for its nearly symmetrical cone and cultural significance.
-
B.
Mount Akan Fuji
Mount Akan Fuji is a picturesque, conical volcano in Hokkaido, Japan, known for its resemblance to Mount Fuji and its location within Akan-Mashu National Park.
-
C.
Fujiyama
Fujiyama is a famous steel roller coaster in Japan known for its great height, speed, and record-breaking status when it opened.
-
D.
Mount Hakone
Mount Hakone is an active volcanic complex in Japan’s Kanagawa Prefecture, famous for its hot springs, scenic crater lake, and views of nearby Mount Fuji.
-
E.
Mount Yamashiro
Mount Yamashiro is a Japanese mountain whose name was historically significant enough to be used for the Imperial Japanese Navy battleship Yamashiro.
- 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_69f1626fad6881909895cc8c0af62f0d |
completed | April 29, 2026, 1:44 a.m. |
Created at: April 17, 2026, 2:51 p.m.