Triple
T23142611
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naive Bayes classifier |
E577500
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Bayes' theorem |
—
|
NE NERFINISHED |
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' theorem | Statement: [Naive Bayes classifier, basedOn, Bayes' theorem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayes' theorem Context triple: [Naive Bayes classifier, basedOn, Bayes' theorem]
-
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.
Bayes
Bayes is a surname most famously associated with Thomas Bayes, the 18th-century statistician and minister whose work led to the development of Bayesian probability theory.
-
C.
Bésayes
Bésayes is a small commune in southeastern France’s Drôme department, known for its rural setting at the foot of the Vercors massif.
-
D.
Bayes rules
Bayes rules are decision rules in statistical decision theory that minimize expected loss with respect to a prior distribution, forming a central concept in Bayesian optimal decision-making.
-
E.
Laplace's rule of succession (as a special case)
Laplace's rule of succession is a classical Bayesian rule for estimating the probability of an event based on observed successes and failures, assigning a nonzero prior probability to unobserved outcomes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18ecb72fc8190a24e8f5756217a36 |
completed | April 29, 2026, 4:53 a.m. |
Created at: April 17, 2026, 4 p.m.