Triple

T22447026
Position Surface form Disambiguated ID Type / Status
Subject elm-repl E554886 entity
Predicate programmingLanguage P1592 FINISHED
Object Elm 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: Elm | Statement: [elm-repl, programmingLanguage, Elm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elm
Context triple: [elm-repl, programmingLanguage, Elm]
  • A. Elm
    Elm is a civil parish and village in Cambridgeshire, England, known for its rural character and historic church.
  • B. Elm chosen
    Elm is a statically typed, functional programming language that compiles to JavaScript and is designed for building reliable, maintainable web front-end applications.
  • C. ELM
    ELM is the commonly used abbreviation for the Estonian Literary Museum, a national research and memory institution dedicated to preserving and studying Estonia’s literary and folkloric heritage.
  • D. ELM
    ELM is the three-letter IATA airport code for Elmira/Corning Regional Airport in New York, United States.
  • E. Maple
    Maple is a comprehensive computer algebra system used for symbolic and numeric mathematics, modeling, and technical computing across education and research.
  • 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_69e11e5113208190ab58c6b595f9d1d0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15b4803908190990280ebd258cb03 completed April 29, 2026, 1:13 a.m.
Created at: April 16, 2026, 8:47 p.m.