Triple
T28813126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gero |
E727569
|
entity |
| Predicate | onsenWaterType |
P851
|
FINISHED |
| Object | alkaline simple hot spring |
—
|
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: alkaline simple hot spring | Statement: [Gero, onsenWaterType, alkaline simple hot spring]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: onsenWaterType Context triple: [Gero, onsenWaterType, alkaline simple hot spring]
-
A.
waterType
chosen
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
B.
boilerType
Indicates the specific kind or category of boiler associated with an entity, such as its design, fuel source, or operating characteristics.
-
C.
onsenCategory
Indicates that one entity is classified as belonging to a particular category or type within the context of hot springs (onsen).
-
D.
waterServiceType
Indicates the specific kind or category of water service provided or associated with an entity.
-
E.
onsenArea
Indicates that a location or region is designated as an onsen (hot spring) area.
- F. None of above.
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_69f0319c38948190bca746ad60fd25ba |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f658f163a88190b1dd222eaa0f93ea |
completed | May 2, 2026, 8:05 p.m. |
| PD | Predicate disambiguation | batch_69f65760fd3081908ffe014a5e2bf069 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 28, 2026, 6:31 a.m.