Triple
T6078464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kinosaki Onsen |
E135461
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Toyooka |
E180128
|
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: Toyooka | Statement: [Kinosaki Onsen, locatedIn, Toyooka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toyooka Context triple: [Kinosaki Onsen, locatedIn, Toyooka]
-
A.
Toyooka
chosen
Toyooka is a city in northern Hyogo Prefecture, Japan, known for its stork conservation efforts, hot spring resort Kinosaki Onsen, and scenic coastal and rural landscapes.
-
B.
Takanami
Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
-
C.
Takeharu
Takeharu is a Japanese given name commonly used for males.
-
D.
Itagaki
Itagaki is a Japanese surname associated with several notable historical and contemporary figures in Japan.
-
E.
Takaichi
Takaichi is a Japanese surname most prominently associated with conservative politician Sanae Takaichi.
- 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057706d9881909b52093282593886 |
completed | March 22, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d48f0508190991453dc17c53b89 |
completed | March 23, 2026, 11 a.m. |
Created at: March 22, 2026, 4:11 p.m.