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.