Triple
T9919713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shizuoka |
E185961
|
entity |
| Predicate | hasCulturalAttraction |
P3114
|
FINISHED |
| Object | Nihondaira |
E704314
|
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: Nihondaira | Statement: [Shizuoka, hasCulturalAttraction, Nihondaira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nihondaira Context triple: [Shizuoka, hasCulturalAttraction, Nihondaira]
-
A.
Nihondaira
chosen
Nihondaira is a scenic plateau in Shizuoka Prefecture, Japan, famed for its panoramic views of Mount Fuji, Suruga Bay, and the surrounding tea fields.
-
B.
Hakutaka
Hakutaka is a high-speed train service operating on Japan’s Hokuriku Shinkansen line, connecting Tokyo with cities along the Sea of Japan coast.
-
C.
Nippani
Nippani is a town in the northern part of Karnataka, India, known for its agriculture, small-scale industries, and location near the Maharashtra border.
-
D.
Iwakura
Iwakura is a Japanese surname most famously associated with Iwakura Tomomi, a key statesman of the Meiji Restoration.
-
E.
Settsu
Settsu is a city in Osaka Prefecture, Japan, known as part of the Osaka metropolitan area.
- 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_69ca829b45f481909040f7b99a1976ed |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb5699bc48190961e036d1131fef0 |
completed | April 2, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d20df77f208190b550b888bf7b55ea |
completed | April 5, 2026, 7:23 a.m. |
Created at: March 30, 2026, 8:42 p.m.