Triple

T4416677
Position Surface form Disambiguated ID Type / Status
Subject Lisp E94990 entity
Predicate influenced P9 FINISHED
Object Racket E131743 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: Racket | Statement: [Lisp, influenced, Racket]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Racket
Context triple: [Lisp, influenced, Racket]
  • A. Racket chosen
    Racket is a modern, multi-paradigm programming language in the Lisp/Scheme family, designed for language-oriented programming, scripting, and education.
  • 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. 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. Gambit Scheme
    Gambit Scheme is a high-performance implementation of the Scheme programming language, known for its efficient compiler, support for concurrent and distributed programming, and ability to generate C code for portability.
  • 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_69b3453a36908190b95a79a297ca083c completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3551afb448190a2ce2000193808ac completed March 13, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69b5f61b56a8819099b5302f1b53f76d completed March 14, 2026, 11:58 p.m.
Created at: March 12, 2026, 11:29 p.m.