Triple
T14369216
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold Jeffreys |
E356313
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object |
Jeffreys scale for Bayes factors
The Jeffreys scale for Bayes factors is a guideline in Bayesian statistics that categorizes the strength of evidence provided by Bayes factors in favor of one hypothesis over another.
|
E1096365
|
NE FINISHED |
How this triple was built (4 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: Jeffreys scale for Bayes factors | Statement: [Harold Jeffreys, knownFor, Jeffreys scale for Bayes factors]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffreys scale for Bayes factors Context triple: [Harold Jeffreys, knownFor, Jeffreys scale for Bayes factors]
-
A.
Bayes factor
The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
-
B.
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.
-
C.
Bayesian model averaging
Bayesian model averaging is a statistical technique that combines predictions from multiple models by weighting them according to their posterior probabilities to account for model uncertainty.
-
D.
Jeffreys prior
Jeffreys prior is an objective Bayesian prior distribution defined to be invariant under reparameterization by constructing it from the square root of the determinant of the Fisher information matrix.
-
E.
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.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jeffreys scale for Bayes factors Triple: [Harold Jeffreys, knownFor, Jeffreys scale for Bayes factors]
Generated description
The Jeffreys scale for Bayes factors is a guideline in Bayesian statistics that categorizes the strength of evidence provided by Bayes factors in favor of one hypothesis over another.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jeffreys scale for Bayes factors Target entity description: The Jeffreys scale for Bayes factors is a guideline in Bayesian statistics that categorizes the strength of evidence provided by Bayes factors in favor of one hypothesis over another.
-
A.
Bayes factor
The Bayes factor is a Bayesian model comparison metric that quantifies how much more strongly data support one statistical model or hypothesis over another.
-
B.
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.
-
C.
Bayesian model averaging
Bayesian model averaging is a statistical technique that combines predictions from multiple models by weighting them according to their posterior probabilities to account for model uncertainty.
-
D.
Jeffreys prior
Jeffreys prior is an objective Bayesian prior distribution defined to be invariant under reparameterization by constructing it from the square root of the determinant of the Fisher information matrix.
-
E.
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.
- F. None of above. chosen
Provenance (5 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fb0b8988190ab834a85911c015c |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c51bf888190b1776461884c4514 |
completed | May 8, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_69fd5020e6f081909686fe3d143d31fa |
completed | May 8, 2026, 2:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd50c2cdb48190a438dc0641e3c25e |
completed | May 8, 2026, 2:56 a.m. |
Created at: April 10, 2026, 1:15 a.m.