Triple
T25960237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AMPL |
E645517
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | mathematical optimization modeling language |
C50159
|
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: mathematical optimization modeling language Context triple: [AMPL, instanceOf, mathematical optimization modeling language]
-
A.
mathematical program
A mathematical program is an optimization model that seeks to minimize or maximize an objective function subject to a set of mathematical constraints.
-
B.
optimization paradigm
An optimization paradigm is a conceptual framework that defines how to formulate, search for, and evaluate solutions to a problem in order to find the best (or sufficiently good) outcome under given constraints and objectives.
-
C.
polyhedral optimizer
A polyhedral optimizer is a compiler component that analyzes and transforms loop nests using polyhedral models to improve performance through advanced loop restructuring, parallelization, and locality optimization.
-
D.
combinatorial optimization problem
A combinatorial optimization problem is a mathematical task of finding an optimal object (such as a subset, sequence, or arrangement) from a finite but typically large set of discrete possibilities, subject to given constraints.
-
E.
neural network modeling language
A neural network modeling language is a specialized formalism or syntax used to define, configure, and connect neural network components and architectures in a clear, structured, and often platform-agnostic way.
- 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_69e77e85efc08190997da7fcf98bd300 |
completed | April 21, 2026, 1:41 p.m. |
Created at: April 22, 2026, 8:47 a.m.