Triple

T19319200
Position Surface form Disambiguated ID Type / Status
Subject von Kármán constant E483174 entity
Predicate usedIn P98 FINISHED
Object Monin–Obukhov similarity theory
Monin–Obukhov similarity theory is a foundational framework in boundary-layer meteorology that describes how turbulence and mean profiles of wind, temperature, and other scalars scale in the atmospheric surface layer under varying stability conditions.
E1369953 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: Monin–Obukhov similarity theory | Statement: [von Kármán constant, usedIn, Monin–Obukhov similarity theory]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monin–Obukhov similarity theory
Context triple: [von Kármán constant, usedIn, Monin–Obukhov similarity theory]
  • A. Taylor microscale in turbulence
    The Taylor microscale in turbulence is a characteristic length scale that quantifies the size of eddies where viscous dissipation begins to significantly affect turbulent motion, bridging the gap between large energy-containing eddies and the smallest dissipative scales.
  • B. The Theory of Homogeneous Turbulence
    The Theory of Homogeneous Turbulence is a classic monograph in fluid dynamics that provides a rigorous mathematical treatment of statistically uniform turbulent flows.
  • C. Kolmogorov spectrum of turbulence
    The Kolmogorov spectrum of turbulence is a fundamental theory in fluid dynamics that predicts how kinetic energy is distributed across different scales in fully developed turbulent flow, most famously yielding the −5/3 power law for the inertial subrange.
  • D. Kraichnan model of passive scalar advection
    The Kraichnan model of passive scalar advection is a theoretical framework in turbulence that studies how a passively transported quantity (like temperature or pollutant concentration) evolves in a fluid flow modeled by a Gaussian, white-in-time random velocity field.
  • E. Turbulence: The Legacy of A. N. Kolmogorov
    Turbulence: The Legacy of A. N. Kolmogorov is a comprehensive monograph on the theory of turbulence that examines and extends Andrey Kolmogorov’s foundational contributions to fluid dynamics and statistical physics.
  • 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: Monin–Obukhov similarity theory
Triple: [von Kármán constant, usedIn, Monin–Obukhov similarity theory]
Generated description
Monin–Obukhov similarity theory is a foundational framework in boundary-layer meteorology that describes how turbulence and mean profiles of wind, temperature, and other scalars scale in the atmospheric surface layer under varying stability conditions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Monin–Obukhov similarity theory
Target entity description: Monin–Obukhov similarity theory is a foundational framework in boundary-layer meteorology that describes how turbulence and mean profiles of wind, temperature, and other scalars scale in the atmospheric surface layer under varying stability conditions.
  • A. Taylor microscale in turbulence
    The Taylor microscale in turbulence is a characteristic length scale that quantifies the size of eddies where viscous dissipation begins to significantly affect turbulent motion, bridging the gap between large energy-containing eddies and the smallest dissipative scales.
  • B. The Theory of Homogeneous Turbulence
    The Theory of Homogeneous Turbulence is a classic monograph in fluid dynamics that provides a rigorous mathematical treatment of statistically uniform turbulent flows.
  • C. Kolmogorov spectrum of turbulence
    The Kolmogorov spectrum of turbulence is a fundamental theory in fluid dynamics that predicts how kinetic energy is distributed across different scales in fully developed turbulent flow, most famously yielding the −5/3 power law for the inertial subrange.
  • D. Kraichnan model of passive scalar advection
    The Kraichnan model of passive scalar advection is a theoretical framework in turbulence that studies how a passively transported quantity (like temperature or pollutant concentration) evolves in a fluid flow modeled by a Gaussian, white-in-time random velocity field.
  • E. Turbulence: The Legacy of A. N. Kolmogorov
    Turbulence: The Legacy of A. N. Kolmogorov is a comprehensive monograph on the theory of turbulence that examines and extends Andrey Kolmogorov’s foundational contributions to fluid dynamics and statistical physics.
  • 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_69d8e8d13e3c81909d91d1d5ec37c095 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e60d868dd48190b1439a5f4ff58c48 completed April 20, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0714640aa481909bd6b1ca617f35a7 completed May 15, 2026, 12:41 p.m.
NEDg Description generation batch_6a0714eb6a0c8190bec7ae8ef785a4ab completed May 15, 2026, 12:43 p.m.
NED2 Entity disambiguation (via description) batch_6a0718ee8adc8190a74685f9783ba5d5 completed May 15, 2026, 1 p.m.
Created at: April 10, 2026, 1:32 p.m.