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.