Triple
T10055327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ackermann function |
E208846
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | classical example in theoretical computer science |
C17349
|
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: classical example in theoretical computer science Context triple: [Ackermann function, instanceOf, classical example in theoretical computer science]
-
A.
foundational principle in theoretical computer science
A foundational principle in theoretical computer science is a core, abstract concept or rule—such as computability, complexity, or formal language theory—that underlies and unifies the study of algorithms, computation models, and their inherent limits.
-
B.
theoretical computer science conference
A theoretical computer science conference is a formal academic gathering where researchers present, discuss, and critique new results and ideas in areas such as algorithms, complexity theory, cryptography, and formal methods.
-
C.
model of computation
A model of computation is an abstract mathematical framework that defines how algorithms are represented and executed, specifying the rules, operations, and resources available for performing computations.
-
D.
computer science problem
chosen
A computer science problem is a well-defined computational task or question that requires designing algorithms, data structures, or formal methods to determine a solution or prove properties about its solvability or complexity.
-
E.
pioneer of algorithmic information theory
A pioneer of algorithmic information theory is a foundational thinker who developed the core concepts and formal frameworks for measuring information, complexity, and randomness using algorithms and computation.
- 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_69ca836094408190a36a1ea7e9a86fcd |
completed | March 30, 2026, 2:06 p.m. |
Created at: March 30, 2026, 8:57 p.m.