Triple
T5065294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Bear Lake |
E114127
|
entity |
| Predicate | hasLowPopulationDensityAround |
P26438
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Great Bear Lake, hasLowPopulationDensityAround, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLowPopulationDensityAround Context triple: [Great Bear Lake, hasLowPopulationDensityAround, yes]
-
A.
hasLowPopulationDensity
chosen
Indicates that the number of individuals or entities per unit area in a given region is relatively small compared to typical or expected levels.
-
B.
hasVerySmallResidentPopulation
Indicates that the subject location has a resident population that is extremely small in size.
-
C.
isDenselyPopulated
Indicates that a place has a high concentration of inhabitants relative to its area.
-
D.
hasPopulationDensity
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
E.
residesNear
Indicates that one entity lives or is located in close physical proximity to another 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_69bd443c0c8c81908663b77afb28e165 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7478f7988190bc0473e8af055147 |
completed | March 20, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69bd715622b48190a3e8e49a5ef62b4a |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:38 p.m.