Triple
T18857119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | gendarmerie |
E461201
|
entity |
| Predicate | typicalAreaOfOperation |
P111844
|
FINISHED |
| Object | rural areas |
—
|
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 areas | Statement: [gendarmerie, typicalAreaOfOperation, rural areas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAreaOfOperation Context triple: [gendarmerie, typicalAreaOfOperation, rural areas]
-
A.
fieldOfOperation
Indicates the domain, area, or scope within which an entity operates or carries out its primary activities.
-
B.
area of activity
chosen
Indicates the domain, field, or sphere in which an entity is active or carries out its primary functions or operations.
-
C.
areaOfSupport
Indicates the spatial region or domain within which an entity provides support, assistance, or backing to another.
-
D.
geographicAreaOfSupport
Indicates the geographic region or area within which support, assistance, or services are provided or applicable.
-
E.
aircraftPrimaryOperationalRegion
Indicates the main geographic area or region in which an aircraft is primarily operated or intended to operate.
- 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_69d8dcfb7b9c8190854e7b171b98ea2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c05de3e88190b0bfcbce906daa5e |
completed | April 20, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e48d2166b88190add38de96cedc65c |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:57 a.m.