Triple
T10077647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fredonia, Alabama |
E213809
|
entity |
| Predicate | populationDensityPerSquareKilometer |
P728
|
FINISHED |
| Object | 13.0 |
—
|
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: 13.0 | Statement: [Fredonia, Alabama, populationDensityPerSquareKilometer, 13.0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationDensityPerSquareKilometer Context triple: [Fredonia, Alabama, populationDensityPerSquareKilometer, 13.0]
-
A.
populationDensity
Indicates the number of individuals or entities occupying a unit area within a given region.
-
B.
hasPopulationDensity
chosen
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
C.
hasPopulationCenterDensity
Indicates the density of population centers within a given area or region.
-
D.
hasPopulationDensityUnit
Indicates the unit of measurement used to express a population density value for a given entity.
-
E.
hasPopulationDensityType
Indicates the classification of an area based on how densely populated it is (e.g., urban, suburban, rural).
- 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.