Triple
T10038313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chernoff information |
E205228
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Rényi divergence |
E41069
|
NE FINISHED |
How this triple was built (2 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Rényi divergence | Statement: [Chernoff information, relatedTo, Rényi divergence]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rényi divergence Context triple: [Chernoff information, relatedTo, Rényi divergence]
-
A.
Rényi divergence
chosen
Rényi divergence is a family of information-theoretic measures that generalize Kullback–Leibler divergence to quantify the dissimilarity between probability distributions, parameterized by an order α.
-
B.
Kullback–Leibler divergence
Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
-
C.
Tsallis divergence
Tsallis divergence is a generalized measure of statistical distance between probability distributions derived from Tsallis entropy, often used in nonextensive statistical mechanics and information theory.
-
D.
Rényi entropy
Rényi entropy is a generalized measure of information and uncertainty that extends Shannon entropy by introducing a tunable order parameter to emphasize different aspects of a probability distribution.
-
E.
Jensen–Shannon divergence
Jensen–Shannon divergence is a symmetrized and smoothed measure of dissimilarity between probability distributions, widely used in information theory and machine learning.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
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_69ca834f70e88190b2d74828b7767ec1 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcee04afc8190904704d66e23a432 |
completed | April 2, 2026, 2:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d29a2cbdd08190b1ef7602ad160114 |
completed | April 5, 2026, 5:21 p.m. |
Created at: March 30, 2026, 8:55 p.m.