Triple
T32747282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jensen–Shannon divergence |
E837388
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | distance-like measure between probability distributions |
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: distance-like measure between probability distributions Context triple: [Jensen–Shannon divergence, instanceOf, distance-like measure between probability distributions]
-
A.
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.
-
B.
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.
-
C.
multivariate dependence measure
A multivariate dependence measure is a quantitative function that assesses the strength and structure of statistical relationships among multiple random variables simultaneously, beyond simple pairwise associations.
-
D.
distance function
A distance function is a rule that assigns a non-negative real number to quantify how far apart two elements are in a given space, typically satisfying properties like non-negativity, identity, symmetry, and the triangle inequality.
-
E.
pseudometric
A pseudometric is a function that assigns a nonnegative real number as a "distance" between any two points in a set, satisfying all the axioms of a metric except that distinct points are allowed to have zero distance.
- 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_69f34936e1748190b797e406e4e9293a |
completed | April 30, 2026, 12:21 p.m. |
Created at: May 1, 2026, 1:12 a.m.