Triple
T17035548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vienna Method of Pictorial Statistics |
E413312
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Wiener Methode der Bildstatistik |
E413312
|
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: Wiener Methode der Bildstatistik | Statement: [Vienna Method of Pictorial Statistics, alsoKnownAs, Wiener Methode der Bildstatistik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wiener Methode der Bildstatistik Context triple: [Vienna Method of Pictorial Statistics, alsoKnownAs, Wiener Methode der Bildstatistik]
-
A.
Wiener filter
The Wiener filter is a signal processing technique that optimally estimates a desired signal from noisy observations by minimizing the mean square error, based on statistical properties of signal and noise.
-
B.
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.
-
C.
Vienna Method of Pictorial Statistics
chosen
The Vienna Method of Pictorial Statistics was an early 20th-century visual communication system that used standardized pictograms to present social and economic data in an accessible, easily understandable form.
-
D.
Grad moment expansion
Grad moment expansion is a method in kinetic theory that approximates the distribution function of a gas by expanding it in a finite set of velocity moments to derive macroscopic fluid equations from the Boltzmann equation.
-
E.
The Fourier Integral and Certain of Its Applications
The Fourier Integral and Certain of Its Applications is a foundational mathematical work by Norbert Wiener that develops and applies Fourier analysis to problems in harmonic analysis and related areas.
- 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_69e3d8f05824819091d2aa02e5591e26 |
completed | April 18, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b59ad3c8190914ee6f8c903cbbf |
completed | May 10, 2026, 11:57 p.m. |
Created at: April 10, 2026, 5:33 a.m.