Triple
T13509476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KKT conditions |
E321097
|
entity |
| Predicate | relatedConcept |
P37
|
FINISHED |
| Object |
Slater’s condition
Slater’s condition is a regularity condition in convex optimization that guarantees strong duality and the validity of the Karush–Kuhn–Tucker optimality conditions by requiring the existence of a strictly feasible point.
|
E1044001
|
NE FINISHED |
How this triple was built (4 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: Slater’s condition | Statement: [KKT conditions, relatedConcept, Slater’s condition]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Slater’s condition Context triple: [KKT conditions, relatedConcept, Slater’s condition]
-
A.
Karush–Kuhn–Tucker conditions
The Karush–Kuhn–Tucker conditions are fundamental optimality criteria in nonlinear programming that generalize Lagrange multipliers to handle inequality constraints.
-
B.
KKT conditions
KKT conditions are a set of necessary (and under certain conditions, sufficient) optimality conditions used in nonlinear programming to characterize solutions of constrained optimization problems.
-
C.
Convex Optimization
Convex Optimization is a widely used graduate-level textbook that systematically develops the theory, algorithms, and applications of convex optimization problems in engineering, statistics, and applied mathematics.
-
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.
Lagrange multipliers
Lagrange multipliers are a mathematical optimization technique used to find the extrema of functions subject to equality constraints.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Slater’s condition Triple: [KKT conditions, relatedConcept, Slater’s condition]
Generated description
Slater’s condition is a regularity condition in convex optimization that guarantees strong duality and the validity of the Karush–Kuhn–Tucker optimality conditions by requiring the existence of a strictly feasible point.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Slater’s condition Target entity description: Slater’s condition is a regularity condition in convex optimization that guarantees strong duality and the validity of the Karush–Kuhn–Tucker optimality conditions by requiring the existence of a strictly feasible point.
-
A.
Karush–Kuhn–Tucker conditions
The Karush–Kuhn–Tucker conditions are fundamental optimality criteria in nonlinear programming that generalize Lagrange multipliers to handle inequality constraints.
-
B.
KKT conditions
KKT conditions are a set of necessary (and under certain conditions, sufficient) optimality conditions used in nonlinear programming to characterize solutions of constrained optimization problems.
-
C.
Convex Optimization
Convex Optimization is a widely used graduate-level textbook that systematically develops the theory, algorithms, and applications of convex optimization problems in engineering, statistics, and applied mathematics.
-
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.
Lagrange multipliers
Lagrange multipliers are a mathematical optimization technique used to find the extrema of functions subject to equality constraints.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf85a74081909eb08751fc55ce8f |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75490291c8190b5985d8c90ef1af6 |
completed | May 3, 2026, 1:58 p.m. |
| NEDg | Description generation | batch_69f7556329648190be791afdd197b08c |
completed | May 3, 2026, 2:02 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f755c52c648190a4912725ce65ff04 |
completed | May 3, 2026, 2:03 p.m. |
Created at: April 9, 2026, 9:43 p.m.