Triple
T6078476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kinosaki Onsen |
E135461
|
entity |
| Predicate | hasNumberOfPublicBathhouses |
P68522
|
FINISHED |
| Object | 7 |
—
|
LITERAL 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: 7 | Statement: [Kinosaki Onsen, hasNumberOfPublicBathhouses, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfPublicBathhouses Context triple: [Kinosaki Onsen, hasNumberOfPublicBathhouses, 7]
-
A.
hasRestrooms
Indicates that a place or facility provides access to restroom or toilet amenities.
-
B.
hasThermalBathsSince
Indicates that an entity has had thermal baths available or in operation continuously since a specified point in time.
-
C.
hasPublicSpaces
Indicates that an entity includes or provides areas that are accessible and usable by the general public.
-
D.
hasPublicHouse
Indicates that one entity possesses, operates, or is associated with a public house (such as a bar or pub) as part of its facilities or holdings.
-
E.
hasNumberOfHotSprings
Indicates the quantity of hot springs associated with a given entity.
- F. None of above. chosen
Provenance (4 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. |
| PD | Predicate disambiguation | batch_69c049f21fe08190995df3c5c05fb8ea |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8d4a148190bd8f95caae978e1b |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:11 p.m.