Triple
T22565352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uffelte |
E557933
|
entity |
| Predicate | hasLowUrbanDensity |
P26438
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Uffelte, hasLowUrbanDensity, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLowUrbanDensity Context triple: [Uffelte, hasLowUrbanDensity, true]
-
A.
hasLowPopulationDensity
chosen
Indicates that the number of individuals or entities per unit area in a given region is relatively small compared to typical or expected levels.
-
B.
isLessUrbanizedThan
Indicates that one place has a lower degree of urban development or urban characteristics compared to another place.
-
C.
hasSuburbanAreas
Indicates that a place includes or is associated with surrounding residential suburban districts or neighborhoods.
-
D.
hasHousingDensity
Indicates the relationship between an area and the concentration of housing units within that area, typically measured as units per unit of land.
-
E.
isPredominantlyRural
Indicates that a place or region is characterized mainly by rural features, such as low population density and extensive non-urban land use.
- 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_69e11e5ae4ac8190b1f503457603d969 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15fa9ebc8819098d74fb41e14bd7e |
completed | April 29, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69ee626e6bb08190ada4dd8b48cc0c43 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 16, 2026, 8:52 p.m.