Triple
T6236736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayes’ theorem |
E139495
|
entity |
| Predicate | alsoCalled |
P39
|
FINISHED |
| Object | Bayes’ rule |
E139495
|
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: Bayes’ rule | Statement: [Bayes’ theorem, alsoCalled, Bayes’ rule]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayes’ rule Context triple: [Bayes’ theorem, alsoCalled, Bayes’ rule]
-
A.
Bayes’ theorem
chosen
Bayes’ theorem is a fundamental result in probability theory that describes how to update the probability of a hypothesis based on new evidence.
-
B.
Bayesian inference
Bayesian inference is a statistical framework that updates the probability of hypotheses as more evidence or data becomes available, using Bayes’ theorem to combine prior beliefs with observed information.
-
C.
Bayesian networks
Bayesian networks are probabilistic graphical models that represent variables and their conditional dependencies using directed acyclic graphs, enabling structured reasoning and inference under uncertainty.
-
D.
Bayesian Occam factor
The Bayesian Occam factor is a term in Bayesian model comparison that automatically penalizes overly complex models by integrating over their larger parameter spaces, thereby implementing Occam’s razor in probabilistic inference.
-
E.
Of Knowledge and Probability
"Of Knowledge and Probability" is a section in John Locke’s *An Essay Concerning Human Understanding* that analyzes the nature, degrees, and limits of human knowledge in contrast with mere probability or belief.
- 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_69c008b0e7ac8190808a59573ee646f3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063021258819093a9237041816638 |
completed | March 22, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20dfbf42c8190842a471db4ff3de0 |
completed | March 24, 2026, 4:07 a.m. |
Created at: March 22, 2026, 4:23 p.m.