Triple
T24957170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | noisy-channel coding theorem |
E624507
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | result in information theory |
C8028
|
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: result in information theory Context triple: [noisy-channel coding theorem, instanceOf, result in information theory]
-
A.
set of axioms in information theory
A set of axioms in information theory is a foundational collection of formal assumptions that precisely define and constrain measures of information, uncertainty, and related concepts so that theorems and results can be derived consistently.
-
B.
set of axioms in information theory
A set of axioms in information theory is a foundational collection of formal principles that precisely define and constrain measures of information, uncertainty, and related concepts so that consistent theorems and results can be derived.
-
C.
inequality in information theory
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.
-
D.
result in probability theory
chosen
In probability theory, a result is a formally stated and proven fact—such as a theorem, lemma, or corollary—that describes a property or relationship involving probabilistic concepts like random variables, events, or distributions.
-
E.
entropy measure
An entropy measure is a quantitative metric that captures the amount of uncertainty, randomness, or information content in a system, distribution, or process.
- 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.