Triple
T13035702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monge problem in optimal transport |
E326555
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | problem in optimal transport theory |
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: problem in optimal transport theory Context triple: [Monge problem in optimal transport, instanceOf, problem in optimal transport theory]
-
A.
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.
-
B.
object in optimal stopping theory
An object in optimal stopping theory is an abstract entity (such as a stochastic process, payoff function, or stopping rule) whose evolution or evaluation over time determines when it is best to stop observing and take an action to maximize expected reward or minimize expected cost.
-
C.
equation in the calculus of variations
An equation in the calculus of variations is a mathematical relation, typically an Euler–Lagrange equation, that characterizes the functions making a given functional stationary (usually minimizing or maximizing its value).
-
D.
optimality conditions
Optimality conditions are mathematical criteria that must be satisfied by a candidate solution to ensure it is a local or global optimum of an optimization problem.
-
E.
result in convex analysis
In convex analysis, a result is a formally stated and proven fact—such as a theorem, lemma, or proposition—that characterizes properties or relationships of convex sets, convex functions, or related optimization structures.
- F. None of above. chosen
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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
Created at: April 9, 2026, 8:55 p.m.