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.