Triple

T19636687
Position Surface form Disambiguated ID Type / Status
Subject Byzantine fault tolerance E471415 entity
Predicate relatedTo P37 FINISHED
Object PBFT 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: PBFT | Statement: [Byzantine fault tolerance, relatedTo, PBFT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PBFT
Context triple: [Byzantine fault tolerance, relatedTo, PBFT]
  • A. FBFT
    FBFT is the ICAO airport code assigned to Francistown Airport in Botswana.
  • B. Paxos
    Paxos is a small Greek island in the Ionian Sea, known for its clear turquoise waters, olive groves, and tranquil, less-touristed atmosphere.
  • C. 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.
  • D. Practical Byzantine Fault Tolerance chosen
    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.
  • E. 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.
  • 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_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.