Triple
T2515009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emanuel Parzen |
E55391
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | Parzen–Rosenblatt window |
E55391
|
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: Parzen–Rosenblatt window | Statement: [Emanuel Parzen, knownFor, Parzen–Rosenblatt window]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parzen–Rosenblatt window Context triple: [Emanuel Parzen, knownFor, Parzen–Rosenblatt window]
-
A.
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.
-
B.
Emanuel Parzen
chosen
Emanuel Parzen was an American statistician renowned for pioneering kernel density estimation, particularly through the development of the Parzen window method.
-
C.
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.
-
D.
Helmholtz machine
The Helmholtz machine is a pioneering generative neural network model that learns internal representations by using separate recognition and generative pathways to perform unsupervised learning.
-
E.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd20db7e0819096d901eb20ae65e5 |
completed | March 7, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af654de164819091cf3d5384d7d72c |
completed | March 10, 2026, 12:26 a.m. |
Created at: March 6, 2026, 9:46 p.m.