Triple
T34901085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East Feliciana Parish |
E1006588
|
entity |
| Predicate | hasRuralPopulationDistribution |
P47416
|
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: [East Feliciana Parish, hasRuralPopulationDistribution, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRuralPopulationDistribution Context triple: [East Feliciana Parish, hasRuralPopulationDistribution, yes]
-
A.
hasRuralDistricts
Indicates that an entity is associated with, contains, or is administratively linked to one or more rural districts.
-
B.
hasRuralAreaShare
Indicates the proportion of an entity’s total area or population that is classified as rural.
-
C.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
D.
isPredominantlyRural
chosen
Indicates that a place or region is characterized mainly by rural features, such as low population density and extensive non-urban land use.
-
E.
hasLongRuralSectionsIn
Indicates that something (such as a route or infrastructure) contains extended stretches that pass through rural areas within a specified region or location.
- 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_69f76dbfe5788190ad8b64f241f470c8 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782c98fa08190870b68de2c1ff26a |
completed | May 3, 2026, 5:15 p.m. |
| PD | Predicate disambiguation | batch_69f781020cc4819088c40cb8589504e4 |
completed | May 3, 2026, 5:08 p.m. |
Created at: May 3, 2026, 4 p.m.