Triple
T10947273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Yamanaka |
E258628
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Gotemba |
E329286
|
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: Gotemba | Statement: [Lake Yamanaka, near, Gotemba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gotemba Context triple: [Lake Yamanaka, near, Gotemba]
-
A.
Gotemba
chosen
Gotemba is a Japanese city in Shizuoka Prefecture known as a gateway to Mount Fuji and a popular base for outdoor activities and outlet shopping.
-
B.
Yamadera
Yamadera is a historic mountainside temple complex in Japan’s Tohoku region, famed for its scenic cliffside halls and panoramic valley views.
-
C.
Takamikura
Takamikura is the ornate imperial throne used in Kyoto for the enthronement ceremonies of Japanese emperors.
-
D.
Shikaoi
Shikaoi is a rural town in Hokkaido, Japan, known for its natural scenery, agriculture, and access to outdoor activities such as hiking and hot springs.
-
E.
Asagumo
Asagumo was a Japanese destroyer of the Imperial Japanese Navy that saw action in World War II, including participation in major Pacific naval engagements.
- 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_69d6aa8769b4819082bfe5e61b9017f0 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d770eaaea08190b06e508600d8a305 |
completed | April 9, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e6fd11b08190b587bebf9586f754 |
completed | April 18, 2026, 8:18 p.m. |
Created at: April 8, 2026, 9:23 p.m.