Triple
T215712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allier department |
E4815
|
entity |
| Predicate | landscapeType |
P9701
|
FINISHED |
| Object | rural |
—
|
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: rural | Statement: [Allier department, landscapeType, rural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landscapeType Context triple: [Allier department, landscapeType, rural]
-
A.
landscapeStyle
Indicates the design style or aesthetic approach applied to a landscape or outdoor environment.
-
B.
vegetationType
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
C.
terrainFeature
Indicates a relationship where one entity is a natural or constructed landform or surface characteristic associated with a given location or area.
-
D.
vegetation
Indicates that an area or object is covered with, contains, or is characterized by plant life.
-
E.
hasNature
Indicates that something possesses, exhibits, or is characterized by a particular inherent quality, essence, or fundamental type.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b52190481908f299d26122bafd2 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25dcba5148190ab80fd14c7cf4bb4 |
completed | Feb. 28, 2026, 3:15 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.