Triple

T1160247
Position Surface form Disambiguated ID Type / Status
Subject Scheme E24474 entity
Predicate notableImplementation P102 FINISHED
Object Guile Scheme E61969 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: Guile Scheme | Statement: [Scheme, notableImplementation, Guile Scheme]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guile Scheme
Context triple: [Scheme, notableImplementation, Guile Scheme]
  • A. GNU Guile chosen
    GNU Guile is the official extension language platform of the GNU Project, providing a Scheme-based scripting and programming environment for extending and customizing applications.
  • B. MIT Scheme
    MIT Scheme is a long-standing, feature-rich implementation of the Scheme programming language developed at the Massachusetts Institute of Technology, often used for teaching and research in computer science.
  • C. Scheme
    Scheme is a minimalist, lexically scoped dialect of the Lisp programming language known for its elegant functional programming model and powerful macro system.
  • D. Racket
    Racket is a modern, multi-paradigm programming language in the Lisp/Scheme family, designed for language-oriented programming, scripting, and education.
  • E. TinyScheme
    TinyScheme is a lightweight, embeddable implementation of the Scheme programming language designed for easy integration into applications.
  • 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_69a494060e148190abb42f971242c197 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bcaf3a9081908bad2eba74dffbc1 completed March 1, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac667d2bdc8190b7d797683a4d3fe8 completed March 7, 2026, 5:55 p.m.
Created at: March 1, 2026, 7:45 p.m.