Triple
T37547107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wahba problem in spline smoothing |
E933493
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | statistical optimization problem |
C66381
|
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: statistical optimization problem Context triple: [Wahba problem in spline smoothing, instanceOf, statistical optimization problem]
-
A.
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.
-
B.
geometric optimization problem
A geometric optimization problem is a mathematical task that involves finding the best (e.g., shortest, largest, or most efficient) geometric configuration or measurement under given constraints.
-
C.
object in optimal stopping theory
An object in optimal stopping theory is an abstract entity (such as a stochastic process, payoff function, or stopping rule) whose evolution or evaluation over time determines when it is best to stop observing and take an action to maximize expected reward or minimize expected cost.
-
D.
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.
-
E.
stochastic approximation method
A stochastic approximation method is an iterative algorithmic technique for finding roots or optima of functions when only noisy or sample-based observations are available, updating estimates using random data to converge to the desired solution.
- 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_69f76eca55bc8190acf25741793d5dac |
completed | May 3, 2026, 3:50 p.m. |
Created at: May 3, 2026, 4:17 p.m.