Practical Byzantine Fault Tolerance
E467813
Practical Byzantine Fault Tolerance is a consensus algorithm for distributed systems that efficiently tolerates Byzantine (arbitrary) faults, enabling reliable operation even when some nodes behave maliciously or unpredictably.
All labels observed (4)
| Label | Occurrences |
|---|---|
| PBFT | 2 |
| Practical Byzantine Fault Tolerance canonical | 2 |
| IBFT 2.0 | 1 |
| practical Byzantine fault tolerance (PBFT) | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4765360 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Practical Byzantine Fault Tolerance Context triple: [Byzantine Generals Problem, inspired, Practical Byzantine Fault Tolerance]
-
A.
Paxos consensus algorithm
The Paxos consensus algorithm is a fault-tolerant protocol for achieving agreement among distributed systems, widely used as a foundation for reliable, replicated state machines and modern distributed databases.
-
B.
"Reaching Agreement in the Presence of Faults"
"Reaching Agreement in the Presence of Faults" is a seminal paper in distributed computing that introduced the Byzantine Generals Problem and laid the foundations for understanding consensus in unreliable, fault-prone systems.
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C.
Byzantine Generals Problem
The Byzantine Generals Problem is a classic computer science and distributed systems thought experiment that illustrates the difficulty of achieving reliable consensus among participants in the presence of faulty or malicious actors.
-
D.
Paxos
Paxos is a small Greek island in the Ionian Sea, known for its clear turquoise waters, olive groves, and tranquil, less-touristed atmosphere.
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E.
"Time, Clocks, and the Ordering of Events in a Distributed System"
"Time, Clocks, and the Ordering of Events in a Distributed System" is a seminal 1978 paper that introduced logical clocks and the happened-before relation, fundamentally shaping the theory and practice of distributed computing.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Practical Byzantine Fault Tolerance Target entity description: Practical Byzantine Fault Tolerance is a consensus algorithm for distributed systems that efficiently tolerates Byzantine (arbitrary) faults, enabling reliable operation even when some nodes behave maliciously or unpredictably.
-
A.
Paxos consensus algorithm
The Paxos consensus algorithm is a fault-tolerant protocol for achieving agreement among distributed systems, widely used as a foundation for reliable, replicated state machines and modern distributed databases.
-
B.
"Reaching Agreement in the Presence of Faults"
"Reaching Agreement in the Presence of Faults" is a seminal paper in distributed computing that introduced the Byzantine Generals Problem and laid the foundations for understanding consensus in unreliable, fault-prone systems.
-
C.
Byzantine Generals Problem
The Byzantine Generals Problem is a classic computer science and distributed systems thought experiment that illustrates the difficulty of achieving reliable consensus among participants in the presence of faulty or malicious actors.
-
D.
Paxos
Paxos is a small Greek island in the Ionian Sea, known for its clear turquoise waters, olive groves, and tranquil, less-touristed atmosphere.
-
E.
"Time, Clocks, and the Ordering of Events in a Distributed System"
"Time, Clocks, and the Ordering of Events in a Distributed System" is a seminal 1978 paper that introduced logical clocks and the happened-before relation, fundamentally shaping the theory and practice of distributed computing.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
Byzantine fault tolerant protocol
ⓘ
consensus algorithm ⓘ distributed systems protocol ⓘ |
| abbreviation |
PBFT
ⓘ
linked to:
Practical Byzantine Fault Tolerance
|
| applicableTo | mission-critical distributed services ⓘ |
| assumes |
authenticated channels between replicas
ⓘ
digital signatures or message authentication codes ⓘ |
| category | state machine replication protocol ⓘ |
| communicationComplexity | O(n^2) per consensus decision ⓘ |
| comparedTo | classical Byzantine Generals Problem solutions ⓘ |
| designedFor | distributed systems ⓘ |
| ensures |
agreement among non-faulty replicas
ⓘ
fault isolation of Byzantine replicas ⓘ integrity of decisions ⓘ validity of decided values ⓘ |
| focusesOn | safety over availability under severe faults ⓘ |
| goal | practical performance for Byzantine fault tolerance ⓘ |
| guarantees |
liveness under partial synchrony assumptions
ⓘ
safety under asynchronous network assumptions with some synchrony conditions ⓘ |
| handles | primary failures via view change ⓘ |
| improvesOn | naive Byzantine agreement protocols in performance ⓘ |
| influenced |
early permissioned blockchain designs
ⓘ
enterprise blockchain consensus protocols ⓘ |
| messagePattern | all-to-all communication among replicas in prepare and commit phases ⓘ |
| optimizesFor | low latency in local-area networks ⓘ |
| originatedIn | late 1990s ⓘ |
| phase |
commit phase
ⓘ
pre-prepare phase ⓘ prepare phase ⓘ |
| property |
deterministic finality
ⓘ
replicated state machine semantics ⓘ strong consistency ⓘ |
| requires |
a designated primary replica per view
ⓘ
at least 3f+1 replicas to tolerate f Byzantine faults ⓘ deterministic application logic for replicas ⓘ |
| securityModel | up to f Byzantine replicas out of 3f+1 total ⓘ |
| tolerates |
Byzantine faults
ⓘ
arbitrary faults ⓘ malicious node behavior ⓘ unpredictable node behavior ⓘ |
| typeOfFaultTolerance | Byzantine fault tolerance ⓘ |
| usedIn |
fault-tolerant services
ⓘ
permissioned distributed ledger systems ⓘ replicated databases ⓘ |
| uses |
primary-backup replication model
ⓘ
three-phase commit-like message pattern ⓘ view-change protocol for primary replacement ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Practical Byzantine Fault Tolerance Description of subject: Practical Byzantine Fault Tolerance is a consensus algorithm for distributed systems that efficiently tolerates Byzantine (arbitrary) faults, enabling reliable operation even when some nodes behave maliciously or unpredictably.
Referenced by (6)
Full triples — surface form annotated when it differs from this entity's canonical label.
Reaching Agreement in the Presence of Faults
→
influenced
→
practical Byzantine fault tolerance (PBFT)
ⓘ
subject linked to:
"Reaching Agreement in the Presence of Faults"
linked to: Practical Byzantine Fault Tolerance
linked to: Practical Byzantine Fault Tolerance
linked to: Practical Byzantine Fault Tolerance
linked to: Practical Byzantine Fault Tolerance