Triple

T18266429
Position Surface form Disambiguated ID Type / Status
Subject Arc E437495 entity
Predicate influencedBy P9 FINISHED
Object Scheme 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: Scheme | Statement: [Arc, influencedBy, Scheme]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Scheme
Context triple: [Arc, influencedBy, Scheme]
  • A. Scheme chosen
    Scheme is a minimalist, lexically scoped dialect of the Lisp programming language known for its elegant functional programming model and powerful macro system.
  • B. Chez Scheme
    Chez Scheme is a high-performance, optimizing implementation of the Scheme programming language widely used for both research and production systems.
  • C. Racket
    Racket is a modern, multi-paradigm programming language in the Lisp/Scheme family, designed for language-oriented programming, scripting, and education.
  • D. PLT Scheme
    PLT Scheme is the original name of the programming language and environment that later evolved into Racket, known for its powerful support of functional and language-oriented programming.
  • E. 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.
  • 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_69d8b913351c8190932b6a426de04b41 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4ff7af85c81909859e7247738a535 completed April 19, 2026, 4:14 p.m.
Created at: April 10, 2026, 10:34 a.m.