Triple
T2578034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Book III: Induction and Analogy |
E57021
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Bayesian approaches to probability |
E40249
|
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: Bayesian approaches to probability | Statement: [Book III: Induction and Analogy, relatedTo, Bayesian approaches to probability]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayesian approaches to probability Context triple: [Book III: Induction and Analogy, relatedTo, Bayesian approaches to probability]
-
A.
Bayesian inference
chosen
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.
-
B.
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.
-
C.
Logical Foundations of Probability
Logical Foundations of Probability is a seminal philosophical work by Rudolf Carnap that develops a rigorous logical and formal account of probability and inductive reasoning.
-
D.
Foundations of Probability
Foundations of Probability is a seminal textbook by mathematician Alfréd Rényi that presents a rigorous, axiomatic treatment of probability theory.
-
E.
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.
- 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_69ab4a4dca6481908c301f8e317396e7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd3a86f9881908df29a7caaf9a7df |
completed | March 7, 2026, 7:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af6576a0a8819080d9241801675b19 |
completed | March 10, 2026, 12:27 a.m. |
Created at: March 6, 2026, 9:49 p.m.