Triple
T34614548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pingualuit Crater Lake |
E888826
|
entity |
| Predicate | humanPopulationNearby |
P151802
|
FINISHED |
| Object | very sparse |
—
|
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: very sparse | Statement: [Pingualuit Crater Lake, humanPopulationNearby, very sparse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: humanPopulationNearby Context triple: [Pingualuit Crater Lake, humanPopulationNearby, very sparse]
-
A.
hasNearbySettlementDensity
chosen
Indicates that an entity is associated with a concentration of settlements located within a nearby surrounding area.
-
B.
nearbySettlements
Indicates that one settlement is located close to another settlement in geographic space.
-
C.
residesNear
Indicates that one entity lives or is located in close physical proximity to another entity.
-
D.
nearestInhabitedTerritory
Indicates that one territory is the closest inhabited territory to another specified location or territory.
-
E.
nearbySettlementRegion
Indicates that a settlement is located close to or within the surrounding area of a specified region.
- 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_69f349d584e08190b40b9f6281ad50c4 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7234bcaa48190ac970759d34e254a |
completed | May 3, 2026, 10:28 a.m. |
| PD | Predicate disambiguation | batch_69f72155c48881909bd40b9aa3febd5a |
completed | May 3, 2026, 10:20 a.m. |
Created at: May 1, 2026, 2:03 a.m.