Triple

T5923861
Position Surface form Disambiguated ID Type / Status
Subject ML E131757 entity
Predicate influenced P9 FINISHED
Object OCaml E131758 NE FINISHED

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: OCaml | Statement: [ML, influenced, OCaml]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: OCaml
Context triple: [ML, influenced, OCaml]
  • A. OCaml chosen
    OCaml is a statically typed functional programming language from the ML family, known for its powerful type system, pattern matching, and efficient native code compilation.
  • B. ReasonML
    ReasonML is a syntax and toolchain for the OCaml language that offers a JavaScript-friendly, type-safe alternative for building web and native applications.
  • C. Standard ML
    Standard ML is a statically typed functional programming language with type inference and a formal semantics, widely used in programming language research and teaching.
  • D. F#
    F# is a functional-first, multi-paradigm programming language for the .NET platform, known for its strong type system and concise, expressive syntax.
  • E. Coq
    Coq is an interactive theorem prover and functional programming language based on dependent type theory, widely used for formally verifying mathematical proofs and software correctness.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c0085a1ed08190a7e9a8b6323fd680 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c03851189c819094524e8b5080545e completed March 22, 2026, 6:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0c0483e3481908e50f8b34b11a878 completed March 23, 2026, 4:23 a.m.
Created at: March 22, 2026, 4 p.m.