Triple
T35332720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kantorovich problem in optimal transport |
E1020368
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | relaxed optimal transport formulation |
C32375
|
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: relaxed optimal transport formulation Context triple: [Kantorovich problem in optimal transport, instanceOf, relaxed optimal transport formulation]
-
A.
optimal transport map
An optimal transport map is a function that rearranges one probability distribution into another in a way that minimizes a specified cost, typically the total "effort" of moving mass from source to target.
-
B.
problem in optimal transport theory
chosen
A problem in optimal transport theory seeks the most efficient way to move mass from one probability distribution to another while minimizing a given cost function.
-
C.
normalizing flow model
A normalizing flow model is a generative model that transforms a simple base distribution into a complex target distribution through a sequence of invertible, differentiable mappings with tractable Jacobian determinants.
-
D.
necessary conditions for optimality
Necessary conditions for optimality are criteria that any candidate solution must satisfy in order to be considered a potential optimizer (such as a minimum, maximum, or saddle point) of a given objective function under specified constraints.
-
E.
transport theory
Transport theory is the conceptual framework that describes how particles, energy, or quantities such as mass and charge move and are distributed within physical systems under the influence of processes like diffusion, convection, and external forces.
- 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_69f76deacf4481908e7735a5a7715b0a |
completed | May 3, 2026, 3:46 p.m. |
Created at: May 3, 2026, 4:03 p.m.