Triple
T34343985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee County, Tennessee |
E881377
|
entity |
| Predicate | waterAreaPercentage |
P19315
|
FINISHED |
| Object | about 1.4 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 1.4 percent | Statement: [Coffee County, Tennessee, waterAreaPercentage, about 1.4 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: waterAreaPercentage Context triple: [Coffee County, Tennessee, waterAreaPercentage, about 1.4 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.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
D.
waterAreaNowKnownAs
Indicates that a body of water previously known by another name or designation is currently recognized or referred to by a specified new name.
-
E.
lakeArea
Indicates the surface area measurement of a lake.
- 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_69f349bc55e881908c8e338ef76b0043 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fd7e364a648190a1e9e1d9fc76e99e |
completed | May 8, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69fd7bb547608190a3b04dddbca6b8bc |
completed | May 8, 2026, 5:59 a.m. |
Created at: May 1, 2026, 1:58 a.m.