Triple
T19636666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Byzantine fault tolerance |
E471415
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | fault tolerance model |
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 tolerance model Context triple: [Byzantine fault tolerance, instanceOf, fault tolerance model]
-
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.
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.
-
C.
fault management framework
A fault management framework is a structured system of processes, tools, and policies designed to detect, isolate, diagnose, and resolve faults in a network or IT environment to maintain reliability and service continuity.
-
D.
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.
-
E.
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.
- 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
Created at: April 10, 2026, 1:44 p.m.