Triple
T2719517
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee County, Alabama |
E60047
|
entity |
| Predicate | ruralArea |
P2460
|
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: [Coffee County, Alabama, ruralArea, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ruralArea Context triple: [Coffee County, Alabama, ruralArea, true]
-
A.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
B.
isRural
chosen
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
C.
hasRuralCommunes
Indicates that an entity possesses, includes, or is associated with one or more rural communes.
-
D.
locatedInAgriculturalRegion
Indicates that an entity is situated within a region primarily characterized by agricultural activities or land use.
-
E.
spokenInRuralAreasOf
Indicates that something (typically a language, dialect, or speech variety) is used or spoken primarily in the rural areas of a specified region or country.
- 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_69ab4b746d248190958e052045c09255 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdaaee104819085966bc54d5da9c0 |
completed | March 7, 2026, 7:58 a.m. |
| PD | Predicate disambiguation | batch_69abd8240920819087a812d816a55edb |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:55 p.m.