Triple

T23142603
Position Surface form Disambiguated ID Type / Status
Subject chaos theory E577499 entity
Predicate coreExample P127119 FINISHED
Object Lorenz system 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: Lorenz system | Statement: [chaos theory, coreExample, Lorenz system]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lorenz system
Context triple: [chaos theory, coreExample, Lorenz system]
  • A. Lorenz attractor chosen
    The Lorenz attractor is a famous chaotic set arising from a simplified model of atmospheric convection, known for its butterfly-shaped trajectory and role as an early example of deterministic chaos in dynamical systems.
  • B. Lorenz
    Lorenz is a masculine given name of German origin, historically borne by various notable figures in Europe.
  • C. Lyapunov fractal
    The Lyapunov fractal is a complex, self-similar pattern arising from iterating logistic maps with periodically varying parameters, used to visualize stability and chaos in dynamical systems.
  • D. Strange Attractors
    "Strange Attractors" is a collection of philosophically rich short stories by Rebecca Goldstein that blend mathematics, science, and human relationships.
  • E. Lotka–Volterra equations
    The Lotka–Volterra equations are a pair of nonlinear differential equations that model the dynamics of biological systems in which two species interact as predator and prey.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: coreExample
Context triple: [chaos theory, coreExample, Lorenz system]
  • A. baseExamples
    Indicates that something serves as a fundamental or illustrative example for understanding or demonstrating another concept, item, or case.
  • B. centralExample chosen
    Indicates that one entity serves as the primary or most representative example of another entity or concept.
  • C. backendExample
    Indicates that something serves as an example or illustrative instance within a backend or server-side context.
  • D. codeExample
    Indicates that one entity provides a snippet or sample of source code that illustrates how to use, implement, or demonstrate another entity.
  • E. standardExample
    Indicates that something is a typical or canonical instance used to illustrate a general case or concept.
  • F. None of above.

Provenance (3 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_69e245f8e6248190ba3d58e068b4dccb completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18ecb72fc8190a24e8f5756217a36 completed April 29, 2026, 4:53 a.m.
PD Predicate disambiguation batch_69ef89f83b108190aaaa1db6221fc163 completed April 27, 2026, 4:08 p.m.
Created at: April 17, 2026, 4 p.m.