Triple
T13064588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gotemba |
E329286
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Hakone |
E255274
|
NE FINISHED |
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: Hakone | Statement: [Gotemba, locatedNear, Hakone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hakone Context triple: [Gotemba, locatedNear, Hakone]
-
A.
Hakone
chosen
Hakone is a popular hot spring resort town in Japan known for its views of Mount Fuji, scenic lakes, and traditional ryokan inns.
-
B.
Kamikochi
Kamikochi is a scenic highland valley in Japan’s Northern Alps renowned for its pristine river, mountain views, and popular hiking trails.
-
C.
Nikko
Nikko is a historic Japanese city in Tochigi Prefecture renowned for its ornate UNESCO-listed shrines, temples, and scenic mountainous landscapes.
-
D.
Nikko
Nikko was a principal disciple and successor of the Japanese Buddhist reformer Nichiren, known for helping to establish and spread Nichiren Buddhism.
-
E.
Fujikawaguchiko
Fujikawaguchiko is a Japanese resort town in Yamanashi Prefecture known for its views of Mount Fuji and Lake Kawaguchi, hot springs, and access to Fuji Five Lakes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980e9bdfc81908eb90fb50597df64 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d6016984819097c999772db1e831 |
completed | May 3, 2026, 4:58 a.m. |
Created at: April 9, 2026, 8:59 p.m.