Triple

T4416670
Position Surface form Disambiguated ID Type / Status
Subject Lisp E94990 entity
Predicate standardizedAs P1371 FINISHED
Object Common Lisp E133804 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: Common Lisp | Statement: [Lisp, standardizedAs, Common Lisp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Common Lisp
Context triple: [Lisp, standardizedAs, Common Lisp]
  • A. Common Lisp chosen
    Common Lisp is a powerful, multi-paradigm dialect of the Lisp programming language standardised in the 1980s, known for its rich macro system, dynamic typing, and suitability for large-scale, extensible software systems.
  • B. Maclisp
    Maclisp is an early and influential dialect of the Lisp programming language developed at MIT, notable for shaping later Lisp systems and language designs.
  • C. Lisp programming language
    Lisp is a pioneering high-level programming language, especially influential in artificial intelligence research and known for its symbolic processing and distinctive parenthesized syntax.
  • D. Scheme
    Scheme is a minimalist, lexically scoped dialect of the Lisp programming language known for its elegant functional programming model and powerful macro system.
  • E. Chez Scheme
    Chez Scheme is a high-performance, optimizing implementation of the Scheme programming language widely used for both research and production systems.
  • 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_69b613664c548190b5cd0c2667baecc7 completed March 15, 2026, 2:03 a.m.
Created at: March 12, 2026, 11:29 p.m.