Triple
T3984205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bluewell, West Virginia |
E86830
|
entity |
| Predicate | hasLandAreaSquareKilometers |
P53183
|
FINISHED |
| Object | 10.6 |
—
|
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: 10.6 | Statement: [Bluewell, West Virginia, hasLandAreaSquareKilometers, 10.6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandAreaSquareKilometers Context triple: [Bluewell, West Virginia, hasLandAreaSquareKilometers, 10.6]
-
A.
areaTotalSquareKilometers
Indicates the total size of something measured in square kilometers.
-
B.
landArea
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
C.
landAreaSquareMiles
Indicates the size of a geographic area measured in square miles.
-
D.
areaApproxKm2
Indicates that one entity has an approximate area, measured in square kilometers, given by the other entity.
-
E.
areaLandSquareMiles
Indicates the amount of land area an entity occupies, measured in square miles.
- F. None of above. chosen
Provenance (4 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_69aed93fd9d4819085d3b2137d2346cb |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa3ef7ac8190abe02f440ff83c43 |
completed | March 9, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69aef8f492ac819089dbb9436dbcdd2b |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aefa3cf4048190837f9ec5fa8e95e3 |
completed | March 9, 2026, 4:50 p.m. |
Created at: March 9, 2026, 3:33 p.m.