Triple
T10152162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graham C. Goodwin |
E232670
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Dynamic System Identification: Experiment Design and Data Analysis
"Dynamic System Identification: Experiment Design and Data Analysis" is a technical book that presents methods for designing experiments and analyzing data to model and identify dynamic systems in engineering and control applications.
|
E843468
|
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: Dynamic System Identification: Experiment Design and Data Analysis | Statement: [Graham C. Goodwin, notableWork, Dynamic System Identification: Experiment Design and Data Analysis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dynamic System Identification: Experiment Design and Data Analysis Context triple: [Graham C. Goodwin, notableWork, Dynamic System Identification: Experiment Design and Data Analysis]
-
A.
Feedback Systems: An Introduction for Scientists and Engineers
Feedback Systems: An Introduction for Scientists and Engineers is a widely used textbook that provides a modern, rigorous introduction to control theory and feedback principles for science and engineering students.
-
B.
Prediction and Regulation by Linear Least-Square Methods
"Prediction and Regulation by Linear Least-Square Methods" is a foundational monograph in stochastic control and time-series analysis that systematically develops linear least-squares techniques for prediction, filtering, and optimal regulation.
-
C.
Åström–Wittenmark adaptive control framework
The Åström–Wittenmark adaptive control framework is a foundational methodology in control theory that systematically designs controllers capable of adjusting their parameters in real time to handle unknown or time-varying system dynamics.
-
D.
Quantitative Feedback Theory
Quantitative Feedback Theory is a robust control design methodology that uses frequency-domain techniques and quantitative bounds to ensure system performance and stability under uncertainty.
-
E.
Sampled-Data Control Systems
Sampled-Data Control Systems is a foundational work in control theory that systematically develops the analysis and design of systems combining continuous-time dynamics with discrete-time sampling.
- 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: Dynamic System Identification: Experiment Design and Data Analysis Triple: [Graham C. Goodwin, notableWork, Dynamic System Identification: Experiment Design and Data Analysis]
Generated description
"Dynamic System Identification: Experiment Design and Data Analysis" is a technical book that presents methods for designing experiments and analyzing data to model and identify dynamic systems in engineering and control applications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dynamic System Identification: Experiment Design and Data Analysis Target entity description: "Dynamic System Identification: Experiment Design and Data Analysis" is a technical book that presents methods for designing experiments and analyzing data to model and identify dynamic systems in engineering and control applications.
-
A.
Feedback Systems: An Introduction for Scientists and Engineers
Feedback Systems: An Introduction for Scientists and Engineers is a widely used textbook that provides a modern, rigorous introduction to control theory and feedback principles for science and engineering students.
-
B.
Prediction and Regulation by Linear Least-Square Methods
"Prediction and Regulation by Linear Least-Square Methods" is a foundational monograph in stochastic control and time-series analysis that systematically develops linear least-squares techniques for prediction, filtering, and optimal regulation.
-
C.
Åström–Wittenmark adaptive control framework
The Åström–Wittenmark adaptive control framework is a foundational methodology in control theory that systematically designs controllers capable of adjusting their parameters in real time to handle unknown or time-varying system dynamics.
-
D.
Quantitative Feedback Theory
Quantitative Feedback Theory is a robust control design methodology that uses frequency-domain techniques and quantitative bounds to ensure system performance and stability under uncertainty.
-
E.
Sampled-Data Control Systems
Sampled-Data Control Systems is a foundational work in control theory that systematically develops the analysis and design of systems combining continuous-time dynamics with discrete-time sampling.
- 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_69ca84885e48819088a31b127cf44904 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec361c7c8190b8fd841d728d5bbe |
completed | April 2, 2026, 4:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2e64b5a40819093f29ec34cb0a763 |
completed | April 5, 2026, 10:46 p.m. |
| NEDg | Description generation | batch_69d2e7408e58819083c43e334a87a09f |
completed | April 5, 2026, 10:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2e7b854d08190ac2af642970b7f09 |
completed | April 5, 2026, 10:52 p.m. |
Created at: March 30, 2026, 9:08 p.m.