Triple

T11560508
Position Surface form Disambiguated ID Type / Status
Subject Grace Wahba E274128 entity
Predicate notableConcept P201 FINISHED
Object Wahba problem in spline smoothing
The Wahba problem in spline smoothing is a foundational statistical formulation that determines an optimal smoothing spline by balancing data fidelity with smoothness, widely used in nonparametric regression and curve fitting.
E933493 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: Wahba problem in spline smoothing | Statement: [Grace Wahba, notableConcept, Wahba problem in spline smoothing]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wahba problem in spline smoothing
Context triple: [Grace Wahba, notableConcept, Wahba problem in spline smoothing]
  • A. B-splines
    B-splines are piecewise polynomial functions widely used in computer graphics and numerical analysis to create smooth, flexible curves and surfaces controlled by a set of control points.
  • B. Catmull–Rom spline
    The Catmull–Rom spline is a type of interpolating spline commonly used in computer graphics and animation to create smooth curves that pass through a given set of control points.
  • C. Extrapolation, Interpolation, and Smoothing of Stationary Time Series
    "Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
  • D. On Estimation of a Probability Density Function and Mode
    "On Estimation of a Probability Density Function and Mode" is a seminal statistical paper by Emanuel Parzen that develops kernel-based methods for nonparametric density and mode estimation.
  • E. Kailath factorization in linear systems
    Kailath factorization in linear systems is a matrix factorization technique used in control and signal processing to efficiently analyze and solve linear dynamical systems.
  • 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: Wahba problem in spline smoothing
Triple: [Grace Wahba, notableConcept, Wahba problem in spline smoothing]
Generated description
The Wahba problem in spline smoothing is a foundational statistical formulation that determines an optimal smoothing spline by balancing data fidelity with smoothness, widely used in nonparametric regression and curve fitting.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Wahba problem in spline smoothing
Target entity description: The Wahba problem in spline smoothing is a foundational statistical formulation that determines an optimal smoothing spline by balancing data fidelity with smoothness, widely used in nonparametric regression and curve fitting.
  • A. B-splines
    B-splines are piecewise polynomial functions widely used in computer graphics and numerical analysis to create smooth, flexible curves and surfaces controlled by a set of control points.
  • B. Catmull–Rom spline
    The Catmull–Rom spline is a type of interpolating spline commonly used in computer graphics and animation to create smooth curves that pass through a given set of control points.
  • C. Extrapolation, Interpolation, and Smoothing of Stationary Time Series
    "Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
  • D. On Estimation of a Probability Density Function and Mode
    "On Estimation of a Probability Density Function and Mode" is a seminal statistical paper by Emanuel Parzen that develops kernel-based methods for nonparametric density and mode estimation.
  • E. Kailath factorization in linear systems
    Kailath factorization in linear systems is a matrix factorization technique used in control and signal processing to efficiently analyze and solve linear dynamical systems.
  • 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_69d6aae4dfa48190a3ab0b19a159a3c5 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d88a899d4481909a3bce3147763b51 completed April 10, 2026, 5:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69e6e88b84d48190948243646bb5fd2b completed April 21, 2026, 3:01 a.m.
NEDg Description generation batch_69e6ef951eb881909810b5923385c4c6 completed April 21, 2026, 3:31 a.m.
NED2 Entity disambiguation (via description) batch_69e6f92ed97c819081576add624dcc27 completed April 21, 2026, 4:12 a.m.
Created at: April 8, 2026, 9:37 p.m.