Triple
T17520751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lloyd’s algorithm |
E426673
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | iterative optimization method |
C21314
|
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: iterative optimization method Context triple: [Lloyd’s algorithm, instanceOf, iterative optimization method]
-
A.
stationary iterative method
chosen
A stationary iterative method is a numerical algorithm for solving linear systems that repeatedly updates an approximate solution using a fixed iteration matrix and rule that do not change between iterations.
-
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.
adaptive learning rate method
An adaptive learning rate method is an optimization technique that automatically adjusts the step size for each parameter during training based on past gradient information to improve convergence speed and stability.
-
D.
numerical integration method for ordinary differential equations
A numerical integration method for ordinary differential equations is an algorithmic procedure that approximates the solution of an ODE over discrete steps by iteratively updating the dependent variable using information about its derivative.
-
E.
algorithm
An algorithm is a finite, well-defined sequence of computational steps or rules designed to solve a specific problem or perform a particular task.
- F. None of above.
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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
Created at: April 10, 2026, 5:49 a.m.