Triple
T24014356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reynolds-averaged Navier–Stokes turbulence modeling |
E594627
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | turbulence modeling approach |
C23145
|
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: turbulence modeling approach Context triple: [Reynolds-averaged Navier–Stokes turbulence modeling, instanceOf, turbulence modeling approach]
-
A.
turbulence model
chosen
A turbulence model is a mathematical framework used in fluid dynamics to approximate the effects of turbulent flow on momentum, energy, and other transported quantities without resolving all turbulent scales directly.
-
B.
Lagrangian turbulence model
A Lagrangian turbulence model is a computational approach that simulates turbulent flows by tracking the trajectories and interactions of individual fluid particles or parcels over time.
-
C.
turbulence theory framework
A turbulence theory framework is a conceptual and mathematical structure that organizes the principles, models, and scaling laws used to describe, analyze, and predict turbulent fluid flows across different regimes and scales.
-
D.
atmospheric dynamics model
An atmospheric dynamics model is a computational representation that simulates the motion, thermodynamics, and interactions of air in the atmosphere to study and predict weather and climate behavior.
-
E.
atmospheric model discretization scheme
An atmospheric model discretization scheme is a numerical framework that approximates the continuous equations governing atmospheric motion and thermodynamics on a finite set of spatial and temporal grid points or elements.
- 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_69e288bc8f608190ac4af29f0bd1c744 |
completed | April 17, 2026, 7:23 p.m. |
Created at: April 17, 2026, 9:42 p.m.