Triple
T10077645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fredonia, Alabama |
E213809
|
entity |
| Predicate | hasTotalAreaSquareKilometers |
P13411
|
FINISHED |
| Object | 15.29 |
—
|
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: 15.29 | Statement: [Fredonia, Alabama, hasTotalAreaSquareKilometers, 15.29]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTotalAreaSquareKilometers Context triple: [Fredonia, Alabama, hasTotalAreaSquareKilometers, 15.29]
-
A.
hasLandAreaSquareKilometers
Indicates that an entity possesses a land area measured in square kilometers.
-
B.
areaTotalSquareKilometers
chosen
Indicates the total size of something measured in square kilometers.
-
C.
meanSurfaceArea_km2
Indicates the average surface area of an entity measured in square kilometers.
-
D.
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).
-
E.
areaApproxKm2
Indicates that one entity has an approximate area, measured in square kilometers, given by the other entity.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd02f47e08190bfeb641b202beecc |
completed | April 2, 2026, 2:10 a.m. |
| PD | Predicate disambiguation | batch_69cd4b97870481908f7a89df10d58a9e |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 8:59 p.m.