Triple
T21768821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blum–Shub–Smale model of computation |
E537367
|
entity |
| Predicate | extends |
P1244
|
FINISHED |
| Object | classical Turing machine model |
—
|
NE NERFINISHED |
How this triple was built (2 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: classical Turing machine model | Statement: [Blum–Shub–Smale model of computation, extends, classical Turing machine model]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: classical Turing machine model Context triple: [Blum–Shub–Smale model of computation, extends, classical Turing machine model]
-
A.
Turing machine
chosen
A Turing machine is an abstract computational model that manipulates symbols on an infinite tape according to a set of rules, providing a formal foundation for the concept of algorithm and computability.
-
B.
Church–Turing thesis
The Church–Turing thesis is a foundational principle in computability theory stating that any function that can be effectively computed by an algorithm can be computed by a Turing machine (or equivalently by other formal models of computation).
-
C.
Turing completeness
Turing completeness is a property of a computational system indicating that it can simulate any Turing machine and thus perform any computation that is algorithmically possible, given enough time and memory.
-
D.
Computing with Register Machines
"Computing with Register Machines" is a chapter in the classic computer science textbook *Structure and Interpretation of Computer Programs* that introduces low-level machine models and shows how higher-level language constructs can be implemented using simple register-based operations.
-
E.
Hartmanis–Stearns theorem
The Hartmanis–Stearns theorem is a foundational result in computational complexity theory that formally established time complexity as a central measure of computational resources for Turing machines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46f5d1c8190bf830409e98464e5 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f031ac10808190837a0f69c4f8a02d |
completed | April 28, 2026, 4:03 a.m. |
Created at: April 16, 2026, 6:51 p.m.