Triple
T69732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vichy |
E1394
|
entity |
| Predicate | hasThermalWaterType |
P851
|
FINISHED |
| Object | bicarbonate-rich 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: bicarbonate-rich mineral water | Statement: [Vichy, hasThermalWaterType, bicarbonate-rich mineral water]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasThermalWaterType Context triple: [Vichy, hasThermalWaterType, bicarbonate-rich mineral water]
-
A.
waterType
chosen
Indicates the specific kind or category of water associated with an entity (e.g., fresh, salt, brackish).
-
B.
hasHydrosphere
Indicates that an entity possesses or is characterized by a surrounding layer or system of water, such as oceans, seas, lakes, or other bodies of liquid water.
-
C.
hasFumaroles
Indicates the presence of fumaroles (openings emitting volcanic gases or steam) associated with an entity.
-
D.
hasSalinityRange
Indicates the range of salinity values within which something (such as a substance, environment, or organism) is present, applicable, or able to function.
-
E.
hasRockType
Indicates that an entity is composed of, characterized by, or associated with a specific type of rock.
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24eaa0df88190add55579b2b9fd02 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.