Triple

T19111817
Position Surface form Disambiguated ID Type / Status
Subject TLA E467807 entity
Predicate influenced P9 FINISHED
Object TLA+ 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: TLA+ | Statement: [TLA, influenced, TLA+]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TLA+
Context triple: [TLA, influenced, TLA+]
  • A. TLA+ chosen
    TLA+ is a formal specification language developed by Leslie Lamport for modeling and verifying concurrent and distributed systems using mathematical logic.
  • B. TLA+ model checker TLC
    TLA+ model checker TLC is an automated verification tool that exhaustively explores the state space of TLA+ specifications to detect errors such as deadlocks, invariant violations, and liveness issues.
  • C. PlusCal algorithm language
    PlusCal algorithm language is a high-level pseudocode-style language designed by Leslie Lamport for writing and reasoning about algorithms that can be automatically translated into TLA+ specifications.
  • D. TLC model checker
    The TLC model checker is a tool for exhaustively verifying TLA+ specifications by exploring all possible system behaviors to detect errors such as deadlocks and invariant violations.
  • E. Dafny programming language
    Dafny is a verification-aware programming language and toolchain designed to support formal specification, automated proof of correctness, and executable code generation for imperative and functional programs.
  • 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_69d8dd06a26481908039e2a1bae8c597 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e394969c81909d09b2300ea0e041 completed April 20, 2026, 8:28 a.m.
Created at: April 10, 2026, 12:04 p.m.