Triple
T38260610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kantorovich duality |
E1017918
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | result in optimal transport theory |
C15240
|
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: result in optimal transport theory Context triple: [Kantorovich duality, instanceOf, result in optimal transport theory]
-
A.
problem in optimal transport theory
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.
-
B.
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.
-
C.
result in convex analysis
chosen
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.
-
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.
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.
- 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_69f76de33e4481909099fa812709bd42 |
completed | May 3, 2026, 3:46 p.m. |
Created at: May 3, 2026, 4:30 p.m.