Triple

T15264342
Position Surface form Disambiguated ID Type / Status
Subject Besu E364860 entity
Predicate supportsConsensus P117864 FINISHED
Object Proof of Work
Proof of Work is a consensus mechanism in blockchain networks where participants solve computationally intensive puzzles to validate transactions and secure the ledger.
E1147451 NE FINISHED

How this triple was built (5 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: Proof of Work | Statement: [Besu, supportsConsensus, Proof of Work]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Proof of Work
Context triple: [Besu, supportsConsensus, Proof of Work]
  • A. RPOW (Reusable Proofs of Work)
    RPOW (Reusable Proofs of Work) was an early digital cash system proposed by Hal Finney that aimed to make proof-of-work tokens transferable and reusable, serving as a conceptual precursor to modern cryptocurrencies.
  • B. Equihash
    Equihash is a memory-hard, proof-of-work hashing algorithm designed to be ASIC-resistant and used in cryptocurrencies such as Zcash.
  • 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. 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.
  • E. Avalanche consensus
    Avalanche consensus is a family of probabilistic, metastable consensus protocols that achieve high throughput and fast finality for decentralized networks through repeated randomized sampling and gossip-based voting.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Proof of Work
Triple: [Besu, supportsConsensus, Proof of Work]
Generated description
Proof of Work is a consensus mechanism in blockchain networks where participants solve computationally intensive puzzles to validate transactions and secure the ledger.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Proof of Work
Target entity description: Proof of Work is a consensus mechanism in blockchain networks where participants solve computationally intensive puzzles to validate transactions and secure the ledger.
  • A. RPOW (Reusable Proofs of Work)
    RPOW (Reusable Proofs of Work) was an early digital cash system proposed by Hal Finney that aimed to make proof-of-work tokens transferable and reusable, serving as a conceptual precursor to modern cryptocurrencies.
  • B. Equihash
    Equihash is a memory-hard, proof-of-work hashing algorithm designed to be ASIC-resistant and used in cryptocurrencies such as Zcash.
  • 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. 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.
  • E. Avalanche consensus
    Avalanche consensus is a family of probabilistic, metastable consensus protocols that achieve high throughput and fast finality for decentralized networks through repeated randomized sampling and gossip-based voting.
  • F. None of above. chosen
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: supportsConsensus
Context triple: [Besu, supportsConsensus, Proof of Work]
  • A. adoptedByConsensus
    Indicates that a decision, action, or change was formally accepted through a consensus process among the relevant participants.
  • B. hasCurrentConsensus
    Indicates that there is a presently agreed-upon or widely accepted position, judgment, or state regarding the related entities.
  • C. notAdoptedByConsensus
    Indicates that a proposal, decision, or action was not accepted or approved through a consensus-based process among the relevant parties.
  • D. supportsBlockchain
    Indicates that one entity provides compatibility with, infrastructure for, or endorsement of blockchain technology used by another entity.
  • E. supportsRatificationOf
    Indicates that one entity endorses or advocates for the formal approval or ratification of another entity (such as a treaty, agreement, or decision).
  • F. None of above. chosen

Provenance (7 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_69d85a0f08408190b3c3259ae35d79d2 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0084fed0481908e452c89cba2be82 completed April 15, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5fdc21881909d87062db6fb8fb7 completed May 9, 2026, 7:45 a.m.
NEDg Description generation batch_69fee714cf6c81908dc4427590eeae85 completed May 9, 2026, 7:49 a.m.
NED2 Entity disambiguation (via description) batch_69feeae4731081909964bd8b1ea3dd7a completed May 9, 2026, 8:05 a.m.
PD Predicate disambiguation batch_69deca8d1bd48190a4b94f29b425e335 completed April 14, 2026, 11:15 p.m.
PDg Predicate description generation batch_69decf2ca6148190967c319728ec3661 completed April 14, 2026, 11:35 p.m.
Created at: April 10, 2026, 3:14 a.m.