Triple
T13282034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autauga County, Alabama |
E316344
|
entity |
| Predicate | hasAreaWaterPercentage |
P19315
|
FINISHED |
| Object | about 1.5 percent |
—
|
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: about 1.5 percent | Statement: [Autauga County, Alabama, hasAreaWaterPercentage, about 1.5 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaWaterPercentage Context triple: [Autauga County, Alabama, hasAreaWaterPercentage, about 1.5 percent]
-
A.
areaWaterPercentage
chosen
Indicates the proportion of an entity’s total area that is covered by water, typically expressed as a percentage.
-
B.
inlandWaterPercentage
Indicates the proportion of a geographic area’s total surface that is covered by inland water bodies such as lakes, rivers, and reservoirs.
-
C.
hasAreaWaterBody
Indicates that an entity includes, contains, or is associated with a body of water within its area or boundaries.
-
D.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
E.
hasWatersOf
Indicates that a geographic or physical entity contains, is traversed by, or is otherwise characterized by specific bodies or types of water.
- 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_69d806b349908190a9a61dd9323bf153 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6535688190a5a4549b7be2d611 |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:27 p.m.