Triple
T10060471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsallis divergence |
E212974
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | f-divergence generalization |
C8696
|
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: f-divergence generalization Context triple: [Tsallis divergence, instanceOf, f-divergence generalization]
-
A.
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.
-
B.
statistical distance
chosen
Statistical distance is a numerical measure of how different two probability distributions are, often used to quantify distinguishability or divergence between random variables or datasets.
-
C.
tool in large deviation theory
A tool in large deviation theory is a mathematical method or result—such as rate functions, the Gartner–Ellis theorem, or contraction principles—used to quantify and analyze the exponentially small probabilities of rare events in stochastic systems.
-
D.
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.
-
E.
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.
- 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_69ca83977128819084084eb7d1d8c52a |
completed | March 30, 2026, 2:07 p.m. |
Created at: March 30, 2026, 8:57 p.m.