Triple

T19636688
Position Surface form Disambiguated ID Type / Status
Subject Byzantine fault tolerance E471415 entity
Predicate relatedTo P37 FINISHED
Object Byzantine agreement protocols NE NERFINISHED

How this triple was built (3 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: Byzantine agreement protocols | Statement: [Byzantine fault tolerance, relatedTo, Byzantine agreement protocols]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Byzantine agreement protocols
Context triple: [Byzantine fault tolerance, relatedTo, Byzantine agreement protocols]
  • A. Byzantine fault tolerance
    Byzantine fault tolerance is a property of distributed systems that enables them to continue operating correctly even when some components behave arbitrarily or maliciously.
  • B. Practical Byzantine Fault Tolerance
    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.
  • C. "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.
  • D. Elements of Distributed Algorithms
    Elements of Distributed Algorithms is a foundational textbook that systematically presents the principles, models, and key techniques used in the design and analysis of distributed algorithms.
  • E. 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.
  • 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: Byzantine agreement protocols
Target entity description: Byzantine agreement protocols are distributed computing algorithms that enable a group of nodes to reliably reach consensus even when some participants behave arbitrarily or maliciously.
  • A. Byzantine fault tolerance chosen
    Byzantine fault tolerance is a property of distributed systems that enables them to continue operating correctly even when some components behave arbitrarily or maliciously.
  • B. Practical Byzantine Fault Tolerance
    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.
  • C. "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.
  • D. Elements of Distributed Algorithms
    Elements of Distributed Algorithms is a foundational textbook that systematically presents the principles, models, and key techniques used in the design and analysis of distributed algorithms.
  • E. 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.
  • F. None of above.

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_69d8e511f28481909f4bc3ea9191e54a completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e641070528819085663c439f50148e completed April 20, 2026, 3:06 p.m.
Created at: April 10, 2026, 1:44 p.m.