Triple
T2515038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emanuel Parzen |
E55391
|
entity |
| Predicate | hasPublication |
P80
|
FINISHED |
| Object |
Time Series Analysis of Irregularly Observed Data
"Time Series Analysis of Irregularly Observed Data" is a scholarly work by statistician Emanuel Parzen that develops methods for modeling and analyzing time series when observations occur at uneven or irregular time intervals.
|
E274131
|
NE FINISHED |
How this triple was built (4 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: Time Series Analysis of Irregularly Observed Data | Statement: [Emanuel Parzen, hasPublication, Time Series Analysis of Irregularly Observed Data]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Time Series Analysis of Irregularly Observed Data Context triple: [Emanuel Parzen, hasPublication, Time Series Analysis of Irregularly Observed Data]
-
A.
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
"Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
-
B.
Sequential Analysis
Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
-
C.
Innovations approach to detection and estimation
"Innovations approach to detection and estimation" is a seminal work by Thomas Kailath that develops a powerful stochastic framework for solving signal detection and parameter estimation problems, particularly in control and communication systems.
-
D.
Standard Time series
The Standard Time series is a collection of jazz albums by trumpeter Wynton Marsalis that explores and reinterprets classic jazz standards and traditional American music.
-
E.
“A New Approach to Linear Filtering and Prediction Problems”
“A New Approach to Linear Filtering and Prediction Problems” is Rudolf E. Kálmán’s landmark 1960 paper that introduced the Kalman filter, a foundational algorithm for optimal estimation in control theory, signal processing, and navigation.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Time Series Analysis of Irregularly Observed Data Triple: [Emanuel Parzen, hasPublication, Time Series Analysis of Irregularly Observed Data]
Generated description
"Time Series Analysis of Irregularly Observed Data" is a scholarly work by statistician Emanuel Parzen that develops methods for modeling and analyzing time series when observations occur at uneven or irregular time intervals.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Time Series Analysis of Irregularly Observed Data Target entity description: "Time Series Analysis of Irregularly Observed Data" is a scholarly work by statistician Emanuel Parzen that develops methods for modeling and analyzing time series when observations occur at uneven or irregular time intervals.
-
A.
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
"Extrapolation, Interpolation, and Smoothing of Stationary Time Series" is a foundational mathematical work by Norbert Wiener that developed the theory of optimal prediction and filtering for stationary stochastic processes, laying the groundwork for modern signal processing and control theory.
-
B.
Sequential Analysis
Sequential Analysis is a foundational statistical methodology that develops procedures for evaluating data as it is collected, allowing decisions to be made at variable sample sizes rather than after a fixed number of observations.
-
C.
Innovations approach to detection and estimation
"Innovations approach to detection and estimation" is a seminal work by Thomas Kailath that develops a powerful stochastic framework for solving signal detection and parameter estimation problems, particularly in control and communication systems.
-
D.
Standard Time series
The Standard Time series is a collection of jazz albums by trumpeter Wynton Marsalis that explores and reinterprets classic jazz standards and traditional American music.
-
E.
“A New Approach to Linear Filtering and Prediction Problems”
“A New Approach to Linear Filtering and Prediction Problems” is Rudolf E. Kálmán’s landmark 1960 paper that introduced the Kalman filter, a foundational algorithm for optimal estimation in control theory, signal processing, and navigation.
- F. None of above. chosen
Provenance (5 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_69af2b975e6881909b70a1795e8e2776 |
completed | March 9, 2026, 8:20 p.m. |
| NEDg | Description generation | batch_69af461461d08190b50fa5ff80f1a774 |
completed | March 9, 2026, 10:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af467dd1c0819090bf8e01bbdb7e37 |
completed | March 9, 2026, 10:15 p.m. |
Created at: March 6, 2026, 9:46 p.m.