Triple

T17040855
Position Surface form Disambiguated ID Type / Status
Subject John Crank E413441 entity
Predicate notableConcept P201 FINISHED
Object Crank–Nicolson scheme E87777 NE FINISHED

How this triple was built (2 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: Crank–Nicolson scheme | Statement: [John Crank, notableConcept, Crank–Nicolson scheme]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Crank–Nicolson scheme
Context triple: [John Crank, notableConcept, Crank–Nicolson scheme]
  • A. Crank–Nicolson scheme chosen
    The Crank–Nicolson scheme is a finite difference method for numerically solving time-dependent partial differential equations, especially parabolic ones like the heat equation, known for its second-order accuracy and unconditional stability.
  • B. Lax–Friedrichs scheme
    The Lax–Friedrichs scheme is a numerical method for approximating solutions to hyperbolic partial differential equations, known for its simplicity and strong stability properties.
  • C. Lax–Wendroff method
    The Lax–Wendroff method is a numerical scheme for solving hyperbolic partial differential equations that achieves second-order accuracy in both space and time by using a Taylor series expansion and flux approximations.
  • D. Godunov-type schemes
    Godunov-type schemes are a class of finite-volume numerical methods for solving hyperbolic conservation laws that use Riemann solvers to accurately capture shock waves and discontinuities.
  • E. Runge–Kutta methods
    Runge–Kutta methods are a family of iterative techniques for numerically solving ordinary differential equations with higher accuracy than simple one-step schemes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d886cd18288190b006abab23f811b7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d8f6a0c08190a838279b83b55b72 completed April 18, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0139f17a5481908896c1c6ff326c2f completed May 11, 2026, 2:07 a.m.
Created at: April 10, 2026, 5:33 a.m.