Triple
T6833687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cramér–Rao bound |
E157397
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | lower bound on variance |
C21321
|
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: lower bound on variance Context triple: [Cramér–Rao bound, instanceOf, lower bound on variance]
-
A.
moment inequality
A moment inequality is a constraint that specifies that an expected value (moment) of a random variable or function of data must lie above or below a certain bound, rather than being exactly equal to it.
-
B.
norm inequality
A norm inequality is a mathematical statement that compares the sizes (norms) of vectors or functions, often establishing bounds or relationships between different norms in a vector space.
-
C.
equation in the calculus of variations
An equation in the calculus of variations is a mathematical relation, typically an Euler–Lagrange equation, that characterizes the functions making a given functional stationary (usually minimizing or maximizing its value).
-
D.
approximation
An approximation is a value, representation, or solution that is close to, but not exactly equal to, a true or ideal quantity, used when exactness is unnecessary or unattainable.
-
E.
quantitative central limit theorem
The quantitative central limit theorem provides explicit bounds on how quickly the distribution of normalized sums of random variables converges to the normal distribution, typically in terms of metrics like the Kolmogorov or Wasserstein distance.
- 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_69c6882c53608190b99aebef079b23bd |
completed | March 27, 2026, 1:37 p.m. |
Created at: March 27, 2026, 2:18 p.m.