Triple
T12491833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Probabilistic Graphical Models: Principles and Techniques |
E298583
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | artificial intelligence textbook |
C15973
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: artificial intelligence textbook Context triple: [Probabilistic Graphical Models: Principles and Techniques, instanceOf, artificial intelligence textbook]
-
A.
machine learning book
A machine learning book is a structured, written resource that explains the theories, algorithms, and practical applications of machine learning to help readers understand and apply data-driven modeling techniques.
-
B.
foundational artificial intelligence text
chosen
A foundational artificial intelligence text is a comprehensive, authoritative work that establishes core theories, methods, and principles of AI, serving as a primary reference for learning and advancing the field.
-
C.
artificial intelligence
Artificial intelligence is a field of computer science focused on creating systems that can perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making.
-
D.
theory in artificial intelligence
A theory in artificial intelligence is a systematic, formal framework that explains, predicts, or guides the design of intelligent behavior in machines by defining underlying principles, models, and assumptions.
-
E.
computer science book
A computer science book is a structured, written resource that explains concepts, theories, and practices related to computing, algorithms, programming, and information systems.
- F. None of above.
Provenance (1 batch)
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_69d6ada377208190a36011199a4d8558 |
completed | April 8, 2026, 7:33 p.m. |
Created at: April 8, 2026, 9:56 p.m.