Triple

T4850102
Position Surface form Disambiguated ID Type / Status
Subject Scott Fahlman E108392 entity
Predicate knownFor P22 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: [Scott Fahlman, knownFor, Common Lisp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Common Lisp
Context triple: [Scott Fahlman, knownFor, 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_69bd440a89548190a5f14ba6da6b97dc completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6d1e5cf08190bd6b6a524748f170 completed March 20, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69be5cdefda8819095fbc04446bf32f5 completed March 21, 2026, 8:54 a.m.
Created at: March 20, 2026, 1:25 p.m.