Triple
T2312807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bourbon-l’Archambault |
E50996
|
entity |
| Predicate | hasThermalSprings |
P32542
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Bourbon-l’Archambault, hasThermalSprings, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasThermalSprings Context triple: [Bourbon-l’Archambault, hasThermalSprings, yes]
-
A.
hasHotSpring
chosen
Indicates that one entity possesses, contains, or is associated with a hot spring.
-
B.
hasFreshwaterSprings
Indicates that the subject contains or is associated with natural sources of freshwater emerging from the ground.
-
C.
hasSpaResort
Indicates that one entity possesses, includes, or is associated with a spa resort as an amenity or feature.
-
D.
hasSpaTown
Indicates that a place is associated with or contains a town known for its spa or therapeutic bathing facilities.
-
E.
hasThermalWaterUse
Indicates that something makes use of thermal water, typically for purposes such as heating, bathing, energy production, or therapeutic applications.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc685f05481909c863b29d1f6bacd |
completed | March 7, 2026, 6:32 a.m. |
| PD | Predicate disambiguation | batch_69abc58e88e481908733fdf79d3f8a15 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.