Triple

T1382206
Position Surface form Disambiguated ID Type / Status
Subject Gaussian law of error E29362 entity
Predicate contrastedWith P278 FINISHED
Object Laplace law of error
The Laplace law of error is a probability distribution characterized by a sharp peak at the mean and heavier tails than the normal distribution, historically used to model the magnitude of observational errors.
E160629 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: Laplace law of error | Statement: [Gaussian law of error, contrastedWith, Laplace law of error]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laplace law of error
Context triple: [Gaussian law of error, contrastedWith, Laplace law of error]
  • A. Gaussian law of error
    The Gaussian law of error is a fundamental statistical principle stating that measurement errors tend to follow a normal (bell-shaped) distribution, forming the basis of much of probability theory and statistical inference.
  • B. Théorie analytique des probabilités
    Théorie analytique des probabilités is Pierre-Simon Laplace’s foundational treatise that systematically developed probability theory and laid the groundwork for modern statistics.
  • C. A Treatise on Probability
    A Treatise on Probability is John Maynard Keynes’s influential 1921 work that develops a logical and philosophical theory of probability, challenging classical and frequency-based interpretations.
  • D. Illustrations of the Dynamical Theory of Gases
    Illustrations of the Dynamical Theory of Gases is a foundational 1860 scientific paper by James Clerk Maxwell that introduced key ideas of kinetic theory and the statistical behavior of gas molecules.
  • E. Théorie analytique de la chaleur
    Théorie analytique de la chaleur is Joseph Fourier’s foundational 1822 treatise that introduced Fourier series and laid the mathematical groundwork for the modern theory of heat conduction and harmonic analysis.
  • 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: Laplace law of error
Triple: [Gaussian law of error, contrastedWith, Laplace law of error]
Generated description
The Laplace law of error is a probability distribution characterized by a sharp peak at the mean and heavier tails than the normal distribution, historically used to model the magnitude of observational errors.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Laplace law of error
Target entity description: The Laplace law of error is a probability distribution characterized by a sharp peak at the mean and heavier tails than the normal distribution, historically used to model the magnitude of observational errors.
  • A. Gaussian law of error
    The Gaussian law of error is a fundamental statistical principle stating that measurement errors tend to follow a normal (bell-shaped) distribution, forming the basis of much of probability theory and statistical inference.
  • B. Théorie analytique des probabilités
    Théorie analytique des probabilités is Pierre-Simon Laplace’s foundational treatise that systematically developed probability theory and laid the groundwork for modern statistics.
  • C. A Treatise on Probability
    A Treatise on Probability is John Maynard Keynes’s influential 1921 work that develops a logical and philosophical theory of probability, challenging classical and frequency-based interpretations.
  • D. Illustrations of the Dynamical Theory of Gases
    Illustrations of the Dynamical Theory of Gases is a foundational 1860 scientific paper by James Clerk Maxwell that introduced key ideas of kinetic theory and the statistical behavior of gas molecules.
  • E. Théorie analytique de la chaleur
    Théorie analytique de la chaleur is Joseph Fourier’s foundational 1822 treatise that introduced Fourier series and laid the mathematical groundwork for the modern theory of heat conduction and harmonic analysis.
  • 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_69a498d883a48190bfdca525296ef7ee completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c3361bf08190b3f6bbf82e17685b completed March 1, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69acde1f08e08190871fd9d539e902c8 completed March 8, 2026, 2:25 a.m.
NEDg Description generation batch_69acdf4e74b08190a18df73cca054b28 completed March 8, 2026, 2:30 a.m.
NED2 Entity disambiguation (via description) batch_69ace03eabf48190a79b5112fb2a917d completed March 8, 2026, 2:34 a.m.
Created at: March 1, 2026, 7:59 p.m.