Triple
T24957216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fano inequality |
E624508
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | bound on error probability |
C41080
|
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: bound on error probability Context triple: [Fano inequality, instanceOf, bound on error probability]
-
A.
statistical bound
A statistical bound is a theoretical limit that constrains how large or small a statistical quantity (such as an estimator’s error, a probability, or a risk) can be under specified assumptions.
-
B.
bound in coding theory
In coding theory, a bound is a theoretical limit that constrains parameters such as code length, dimension, and minimum distance, defining what combinations are possible or optimal for error-correcting codes.
-
C.
lower bound on variance
A lower bound on variance is a theoretical limit that specifies the smallest possible variance any unbiased estimator of a parameter can achieve under given model assumptions.
-
D.
error-correcting code
An error-correcting code is a method of encoding data with redundant information so that errors introduced during transmission or storage can be detected and corrected.
-
E.
inequality in information theory
chosen
An inequality in information theory is a mathematical relation that bounds or compares information-theoretic quantities—such as entropy, mutual information, or divergence—to reveal fundamental limits on data compression, communication, and inference.
- 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_69e2ff23a3a88190b1b9743fe5e15f94 |
completed | April 18, 2026, 3:48 a.m. |
Created at: April 18, 2026, 5:58 a.m.