Triple
T25725481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shannon–Hartley theorem |
E645105
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | channel capacity theorem |
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: channel capacity theorem Context triple: [Shannon–Hartley theorem, instanceOf, channel capacity theorem]
-
A.
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.
-
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.
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.
-
D.
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.
-
E.
communication theory
Communication theory is the systematic study of how information is created, encoded, transmitted, received, and interpreted across various channels and contexts.
- 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_69e77e8476fc8190bd5e9d05b89fad0a |
completed | April 21, 2026, 1:41 p.m. |
Created at: April 21, 2026, 10:23 p.m.