Triple
T22742504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siméon Denis Poisson |
E562450
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Poisson kernel |
—
|
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: Poisson kernel | Statement: [Siméon Denis Poisson, notableWork, Poisson kernel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Poisson kernel Context triple: [Siméon Denis Poisson, notableWork, Poisson kernel]
-
A.
Poisson kernel
chosen
The Poisson kernel is a fundamental function in harmonic analysis and potential theory used to represent harmonic functions inside a domain from their boundary values, especially in the unit disk and upper half-plane.
-
B.
Poisson integral
The Poisson integral is a fundamental formula in harmonic analysis that reconstructs harmonic functions inside a disk (or half-plane) from their boundary values using the Poisson kernel.
-
C.
Fejér kernel
The Fejér kernel is a sequence of nonnegative trigonometric polynomials used in Fourier analysis to study and ensure the Cesàro (Fejér) summability of Fourier series.
-
D.
Bergman kernel
The Bergman kernel is a fundamental object in complex analysis that reproduces holomorphic functions on a domain and encodes its geometric and analytic structure.
-
E.
Nevanlinna–Pick kernels
Nevanlinna–Pick kernels are special positive-definite kernels that characterize when and how analytic interpolation problems of Nevanlinna–Pick type admit solutions, often serving as the reproducing kernels of associated Hilbert spaces of analytic functions.
- 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_69e245513a5c81908d5cb471b4fc429d |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1797400fc8190bec26726f434f787 |
completed | April 29, 2026, 3:22 a.m. |
Created at: April 17, 2026, 3:23 p.m.