Triple
T20471024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hermite interpolation |
E502191
|
entity |
| Predicate | canBeExtendedTo |
P17547
|
FINISHED |
| Object | Hermite splines |
—
|
NE NERFINISHED |
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: Hermite splines | Statement: [Hermite interpolation, canBeExtendedTo, Hermite splines]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hermite splines Context triple: [Hermite interpolation, canBeExtendedTo, Hermite splines]
-
A.
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.
-
B.
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.
-
C.
Hermite interpolation
chosen
Hermite interpolation is a numerical analysis method for constructing a polynomial that matches both function values and specified derivatives at given data points.
-
D.
Bezier curves
Bézier curves are mathematically defined parametric curves widely used in computer graphics and digital design to model smooth, scalable shapes and paths.
-
E.
Spline Models for Observational Data
"Spline Models for Observational Data" is a foundational monograph by statistician Grace Wahba that develops the theory and applications of spline-based smoothing methods for analyzing real-world data.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4ae5f1081908768b0c9a3a0bf38 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e699608d7c8190910217817e789915 |
completed | April 20, 2026, 9:23 p.m. |
Created at: April 16, 2026, 11:33 a.m.