Triple
T25310920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shannon Lecture |
E634604
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | event in information theory |
C1824
|
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: event in information theory Context triple: [Shannon Lecture, instanceOf, event in information theory]
-
A.
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.
-
B.
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.
-
C.
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.
-
D.
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.
-
E.
event
chosen
An event is a distinct occurrence or happening, often bounded in time and space, that involves one or more participants and may trigger changes in state or behavior within a system.
- 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_69e75a972c6481909bc11710e8d30a6c |
completed | April 21, 2026, 11:08 a.m. |
Created at: April 21, 2026, 1:26 p.m.