Triple
T12798013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carnap's continuum of inductive methods |
E305938
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Bayesian conditionalization |
E679748
|
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 conditionalization | Statement: [Carnap's continuum of inductive methods, relatedTo, Bayesian conditionalization]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayesian conditionalization Context triple: [Carnap's continuum of inductive methods, relatedTo, Bayesian conditionalization]
-
A.
Bayesian epistemology
chosen
Bayesian epistemology is a theory of knowledge that models rational belief and updating in terms of subjective probabilities governed by the rules of Bayesian probability theory.
-
B.
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.
-
C.
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.
-
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.
Truth and Probability
Truth and Probability is a foundational 1926 essay by philosopher F. P. Ramsey that develops a subjective theory of probability and lays groundwork for modern Bayesian decision theory.
- 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_69d7bdf366888190a8cccb982606889c |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e6f858c8190915ede38e9a6a2df |
completed | April 10, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6850f9ae4819094599b48d8d3a074 |
completed | May 2, 2026, 11:13 p.m. |
Created at: April 9, 2026, 5:30 p.m.