Triple
T11560381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emanuel Parzen |
E274125
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | Parzen window method |
E274132
|
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 window method | Statement: [Emanuel Parzen, knownFor, Parzen window method]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parzen window method Context triple: [Emanuel Parzen, knownFor, Parzen window method]
-
A.
On Estimation of a Probability Density Function and Mode
chosen
"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.
Parzen
Parzen is a surname most notably associated with Emanuel Parzen, an American statistician known for the Parzen window method in probability and statistics.
-
C.
Mahalanobis distance
Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.
-
D.
Gaussian process
A Gaussian process is a collection of random variables indexed by a set (often time or space) such that every finite subset has a joint multivariate normal distribution, widely used to model functions in probability theory and machine learning.
-
E.
KNN
KNN (k-nearest neighbors) is a simple, non-parametric machine learning algorithm used for classification and regression by predicting labels based on the closest training examples in the feature space.
- 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_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. |
Created at: April 8, 2026, 9:37 p.m.