Triple

T13035732
Position Surface form Disambiguated ID Type / Status
Subject Monge problem in optimal transport E326555 entity
Predicate hasRelaxation P107554 FINISHED
Object Kantorovich formulation of optimal transport E1020368 NE FINISHED

How this triple was built (3 steps)

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.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Kantorovich formulation of optimal transport | Statement: [Monge problem in optimal transport, hasRelaxation, Kantorovich formulation of optimal transport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kantorovich formulation of optimal transport
Context triple: [Monge problem in optimal transport, hasRelaxation, Kantorovich formulation of optimal transport]
  • A. Kantorovich problem in optimal transport chosen
    The Kantorovich problem in optimal transport is a relaxed, linear-programming formulation of transporting mass between probability distributions that allows splitting mass and guarantees existence of optimal transport plans.
  • B. Optimal Transport: Old and New
    "Optimal Transport: Old and New" is a comprehensive monograph by Cédric Villani that develops the theory of optimal transport and its applications across analysis, geometry, and probability.
  • C. Monge problem in optimal transport
    The Monge problem in optimal transport is a foundational mathematical formulation that seeks the most efficient way to move mass from one distribution to another, minimizing a given transportation cost.
  • D. Kantorovich duality
    Kantorovich duality is a fundamental result in optimal transport theory that characterizes the optimal transport cost as the supremum of a dual variational problem over suitable test functions.
  • E. Brenier map
    The Brenier map is the unique gradient of a convex function that provides the optimal transport between probability measures under a quadratic cost, playing a central role in modern optimal transport theory.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasRelaxation
Context triple: [Monge problem in optimal transport, hasRelaxation, Kantorovich formulation of optimal transport]
  • A. hasRelief
    Indicates that one entity features or exhibits a raised or sculpted surface design (relief) in relation to another entity or context.
  • B. ageRelaxationAvailableFor
    Indicates that there is an allowance or reduction in the standard age requirement applicable to a specified entity or group.
  • C. hasReliefOf
    Indicates that one entity features, bears, or is adorned with a sculpted or carved relief representation of another entity.
  • D. hasReliefRange
    Indicates a relationship where an entity is associated with a specified range or interval of relief (e.g., elevation difference or surface variation) values.
  • E. relievedBy
    Indicates that one entity eases, reduces, or removes the burden, pain, stress, or responsibility experienced by another entity.
  • F. None of above. chosen

Provenance (5 batches)

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.
NER Named-entity recognition batch_69d97f2a71a0819098bb6cf8a4b2208a completed April 10, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e269c18481908e0b46c298a946ca completed May 3, 2026, 5:51 a.m.
PD Predicate disambiguation batch_69d97dc39a0881908119c62e31bf6182 completed April 10, 2026, 10:46 p.m.
PDg Predicate description generation batch_69d97e3df2288190a7f27d31d248bb7f completed April 10, 2026, 10:48 p.m.
Created at: April 9, 2026, 8:55 p.m.