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.