Triple

T5923372
Position Surface form Disambiguated ID Type / Status
Subject Bigloo E131747 entity
Predicate implements P1417 FINISHED
Object Scheme E24474 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: Scheme | Statement: [Bigloo, implements, Scheme]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Scheme
Context triple: [Bigloo, implements, 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 (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_69c1412fcc84819084be2f6210a00fb0 completed March 23, 2026, 1:33 p.m.
Created at: March 22, 2026, 4 p.m.