Triple
T32043993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Milne–Eddington approximation |
E818289
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | stellar atmosphere model |
C26274
|
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: stellar atmosphere model Context triple: [Milne–Eddington approximation, instanceOf, stellar atmosphere model]
-
A.
astrophysical model
chosen
An astrophysical model is a theoretical or computational framework that describes and predicts the physical processes, structures, and evolution of astronomical objects and phenomena in the universe.
-
B.
astrophysical gas structure
An astrophysical gas structure is a large-scale, gravitationally or pressure-bound aggregation of gas in space—such as clouds, filaments, or shells—whose physical properties and dynamics influence and trace key processes like star formation, feedback, and galactic evolution.
-
C.
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.
-
D.
stellar evolution code
A stellar evolution code is a computational tool that simulates the physical processes governing a star’s life cycle, from formation to end stages, by numerically solving the equations of stellar structure and evolution.
-
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_69f348fcfb648190859f6be5e04b7cfe |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 12:19 a.m.