Triple

T17677417
Position Surface form Disambiguated ID Type / Status
Subject FDR model checker E440674 entity
Predicate abbreviationOf P590 FINISHED
Object Failures-Divergence Refinement
Failures-Divergence Refinement is a formal method in concurrency theory used to compare and verify the behavior of communicating processes by analyzing both their observable actions and potential divergences.
E1282524 NE FINISHED

How this triple was built (4 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: Failures-Divergence Refinement | Statement: [FDR model checker, abbreviationOf, Failures-Divergence Refinement]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Failures-Divergence Refinement
Context triple: [FDR model checker, abbreviationOf, Failures-Divergence Refinement]
  • A. IC3: Incremental Construction of Inductive Clauses for Indubitable Correctness
    IC3: Incremental Construction of Inductive Clauses for Indubitable Correctness is a seminal model checking algorithm introduced by Kenneth McMillan that incrementally builds inductive invariants to efficiently verify hardware and software system correctness.
  • B. Compositional model checking
    Compositional model checking is a formal verification technique that proves system correctness by analyzing components separately and then combining the results, enabling scalable verification of complex systems.
  • C. IC3 model checking algorithm
    The IC3 model checking algorithm is a SAT-based formal verification technique that incrementally constructs inductive invariants to efficiently prove or refute safety properties of hardware and software systems.
  • D. On reachability of hybrid automata
    "On reachability of hybrid automata" is a foundational research paper in formal verification and hybrid systems theory that investigates algorithmic methods for determining whether certain states can be reached in systems combining discrete and continuous dynamics.
  • E. Dijkstra weakest precondition calculus
    Dijkstra weakest precondition calculus is a formal method for reasoning about program correctness by computing the weakest conditions that must hold before execution to guarantee a desired postcondition.
  • 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: Failures-Divergence Refinement
Triple: [FDR model checker, abbreviationOf, Failures-Divergence Refinement]
Generated description
Failures-Divergence Refinement is a formal method in concurrency theory used to compare and verify the behavior of communicating processes by analyzing both their observable actions and potential divergences.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Failures-Divergence Refinement
Target entity description: Failures-Divergence Refinement is a formal method in concurrency theory used to compare and verify the behavior of communicating processes by analyzing both their observable actions and potential divergences.
  • A. IC3: Incremental Construction of Inductive Clauses for Indubitable Correctness
    IC3: Incremental Construction of Inductive Clauses for Indubitable Correctness is a seminal model checking algorithm introduced by Kenneth McMillan that incrementally builds inductive invariants to efficiently verify hardware and software system correctness.
  • B. Compositional model checking
    Compositional model checking is a formal verification technique that proves system correctness by analyzing components separately and then combining the results, enabling scalable verification of complex systems.
  • C. IC3 model checking algorithm
    The IC3 model checking algorithm is a SAT-based formal verification technique that incrementally constructs inductive invariants to efficiently prove or refute safety properties of hardware and software systems.
  • D. On reachability of hybrid automata
    "On reachability of hybrid automata" is a foundational research paper in formal verification and hybrid systems theory that investigates algorithmic methods for determining whether certain states can be reached in systems combining discrete and continuous dynamics.
  • E. Dijkstra weakest precondition calculus
    Dijkstra weakest precondition calculus is a formal method for reasoning about program correctness by computing the weakest conditions that must hold before execution to guarantee a desired postcondition.
  • F. None of above. chosen

Provenance (5 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_69d8b9e940b081908b862bb0e6e89b0d completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e46f6e959c819092d9d33e79bd51f7 completed April 19, 2026, 6 a.m.
NED1 Entity disambiguation (via context triple) batch_6a022325d6108190975082abcdb21ce1 completed May 11, 2026, 6:42 p.m.
NEDg Description generation batch_6a022759531c8190a77c5dc4e3ccaee6 completed May 11, 2026, 7 p.m.
NED2 Entity disambiguation (via description) batch_6a0227ff79d881908ad190b2665bc63d completed May 11, 2026, 7:03 p.m.
Created at: April 10, 2026, 10:01 a.m.