Triple
T31948025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marzullo's algorithm |
E815703
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | fault-tolerant algorithm |
C41035
|
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: fault-tolerant algorithm Context triple: [Marzullo's algorithm, instanceOf, fault-tolerant algorithm]
-
A.
fault-tolerant consensus protocol
A fault-tolerant consensus protocol is a distributed algorithm that enables a group of nodes to reliably agree on a shared state or value even when some nodes fail or behave maliciously.
-
B.
fault-tolerant operating system
A fault-tolerant operating system is an OS designed to continue correct operation and maintain essential services despite hardware or software faults, through redundancy, error detection, isolation, and recovery mechanisms.
-
C.
distributed consensus algorithm
chosen
A distributed consensus algorithm is a protocol that enables a group of independent, networked nodes to reliably agree on a single shared value or state, even in the presence of failures or unreliable communication.
-
D.
theorem in distributed computing
A theorem in distributed computing is a formally proven statement that characterizes fundamental limits, guarantees, or behaviors of distributed systems under specified models, assumptions, and failure conditions.
-
E.
GPU fault‑tolerance mechanism
A GPU fault-tolerance mechanism is a system of hardware and software techniques that detect, isolate, and recover from errors in GPU computation or memory to ensure correct and reliable execution.
- 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_69f348f42d188190a33fc8d20ec50517 |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 12:07 a.m.