Triple
T21475293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | J. L. Mackie |
E529845
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Truth, Probability, and Paradox |
—
|
NE NERFINISHED |
How this triple was built (3 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: Truth, Probability, and Paradox | Statement: [J. L. Mackie, notableWork, Truth, Probability, and Paradox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Truth, Probability, and Paradox Context triple: [J. L. Mackie, notableWork, Truth, Probability, and Paradox]
-
A.
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.
-
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.
Probability, Statistics and Truth
Probability, Statistics and Truth is a foundational book by Richard von Mises that presents his frequentist interpretation of probability and explores the philosophical and practical implications of statistical reasoning.
-
D.
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.
-
E.
The Probability of Induction
The Probability of Induction is a seminal essay by Charles Sanders Peirce that analyzes how probabilistic reasoning can justify inductive inference in scientific inquiry.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Truth, Probability, and Paradox Target entity description: "Truth, Probability, and Paradox" is a philosophical work by J. L. Mackie that examines logical puzzles, probabilistic reasoning, and classic paradoxes to illuminate fundamental issues in logic and epistemology.
-
A.
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.
-
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.
Probability, Statistics and Truth
Probability, Statistics and Truth is a foundational book by Richard von Mises that presents his frequentist interpretation of probability and explores the philosophical and practical implications of statistical reasoning.
-
D.
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.
-
E.
The Probability of Induction
The Probability of Induction is a seminal essay by Charles Sanders Peirce that analyzes how probabilistic reasoning can justify inductive inference in scientific inquiry.
- F. None of above. chosen
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_69e0c459acb481909bb6ee452a0045c7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea1737f881908ef7889e9568a4d3 |
completed | April 23, 2026, 9:44 a.m. |
Created at: April 16, 2026, 6:19 p.m.