Triple
T5267688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dalton Highway |
E118976
|
entity |
| Predicate | isSparselyPopulatedArea |
P26438
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Dalton Highway, isSparselyPopulatedArea, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSparselyPopulatedArea Context triple: [Dalton Highway, isSparselyPopulatedArea, true]
-
A.
isDenselyPopulated
Indicates that a place has a high concentration of inhabitants relative to its area.
-
B.
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.
-
C.
isPopulousRegionOf
Indicates that a region has a large population and is located within or associated with a specified larger area or entity.
-
D.
isPredominantlyRural
Indicates that a place or region is characterized mainly by rural features, such as low population density and extensive non-urban land use.
-
E.
isMostDenselyPopulatedRegionIn
Indicates that a region has the highest population density compared to all other regions within a specified larger area or context.
- 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_69bd446a42c88190b7ecbef006561d55 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77c71268819094f9f5203eed392d |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:51 p.m.