Triple
T29618288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bad Füssing |
E754922
|
entity |
| Predicate | thermalWaterType |
P167515
|
FINISHED |
| Object | sulfurous thermal mineral water |
—
|
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: sulfurous thermal mineral water | Statement: [Bad Füssing, thermalWaterType, sulfurous thermal mineral water]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: thermalWaterType Context triple: [Bad Füssing, thermalWaterType, sulfurous thermal mineral water]
-
A.
waterType
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
B.
typeOfWaterfall
Indicates that one entity is a specific kind or category of waterfall in relation to another entity.
-
C.
waterSourceType
Indicates the kind or category of source from which water is obtained.
-
D.
waterHeadType
Indicates the type or classification of a water head (e.g., source or headwater) associated with a water-related feature.
-
E.
reservoirType
Indicates the specific kind or classification of a reservoir associated with an 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_69f0ef86b6ec8190a87fff07fd983b1e |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69f66e227eec8190a5e5a8de8359875b |
completed | May 2, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69f6659d36208190b01412600a4ed57d |
completed | May 2, 2026, 8:59 p.m. |
| PDg | Predicate description generation | batch_69f6691da93081909deaf680614fc900 |
completed | May 2, 2026, 9:14 p.m. |
Created at: April 28, 2026, 6:33 p.m.