Triple
T21172672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bergen school of meteorology cyclone model |
E521729
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | mid-latitude cyclone model |
C14578
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: mid-latitude cyclone model Context triple: [Bergen school of meteorology cyclone model, instanceOf, mid-latitude cyclone model]
-
A.
numerical weather prediction model
chosen
A numerical weather prediction model is a computational system that uses mathematical equations and atmospheric data to simulate and forecast future weather conditions.
-
B.
atmospheric general circulation model
An atmospheric general circulation model is a complex numerical model that simulates the three-dimensional, large-scale movement of air and energy in Earth’s atmosphere to study and predict climate and weather patterns.
-
C.
anticyclonic storm
An anticyclonic storm is a large-scale weather system characterized by high atmospheric pressure at its center and winds that rotate opposite to the typical cyclonic direction for that hemisphere, often bringing clearer and more stable conditions.
-
D.
climate model
A climate model is a computational representation of the Earth’s climate system that simulates interactions among the atmosphere, oceans, land surface, and ice to project past, present, and future climate conditions.
-
E.
radiative–convective model
A radiative–convective model is a simplified atmospheric model that balances radiative energy transfer with convective heat transport to simulate vertical temperature profiles and climate behavior.
- F. None of above.
Provenance (1 batch)
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_69e0b50e30748190b186824a206d39b9 |
completed | April 16, 2026, 10:08 a.m. |
Created at: April 16, 2026, 3 p.m.