Triple
T3910484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold W. Kuhn |
E87307
|
entity |
| Predicate | notableConcept |
P201
|
FINISHED |
| Object | Kuhn–Tucker conditions |
E83405
|
NE FINISHED |
How this triple was built (2 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: Kuhn–Tucker conditions | Statement: [Harold W. Kuhn, notableConcept, Kuhn–Tucker conditions]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kuhn–Tucker conditions Context triple: [Harold W. Kuhn, notableConcept, Kuhn–Tucker conditions]
-
A.
Karush–Kuhn–Tucker conditions
chosen
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.
Lagrange multipliers
Lagrange multipliers are a mathematical optimization technique used to find the extrema of functions subject to equality constraints.
-
D.
Nonlinear programming
Nonlinear programming is a branch of mathematical optimization focused on finding optimal solutions to problems where the objective function or constraints are nonlinear.
-
E.
Hamilton’s maximum principle
Hamilton’s maximum principle is a fundamental analytical tool in geometric analysis that extends the classical maximum principle to tensor-valued quantities, playing a key role in studying the behavior of solutions to the Ricci flow and related geometric evolution equations.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeed3408f881908c3cffc5dbfe3950 |
completed | March 9, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5285851248190a18cea371aacbc49 |
completed | March 14, 2026, 9:20 a.m. |
Created at: March 9, 2026, 3:22 p.m.