Triple

T400544
Position Surface form Disambiguated ID Type / Status
Subject C E9269 entity
Predicate influencedBy P9 FINISHED
Object ALGOL 68 E17646 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: ALGOL 68 | Statement: [C, influencedBy, ALGOL 68]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ALGOL 68
Context triple: [C, influencedBy, ALGOL 68]
  • A. Algol 68 chosen
    Algol 68 is a high-level, structured programming language from the ALGOL family, notable for its orthogonal design and influence on many later languages.
  • B. ALGOL 60
    ALGOL 60 is an early high-level programming language that pioneered block structure and lexical scoping, profoundly influencing the design of many later languages.
  • C. ALGOL W
    ALGOL W is an early procedural programming language developed in the 1960s as a successor to ALGOL 60, notable for introducing features that strongly influenced the design of Pascal.
  • D. BCPL
    BCPL (Basic Combined Programming Language) is an early, typeless systems programming language developed in the 1960s that significantly influenced the design of the C programming language.
  • E. ABC programming language
    ABC is an early high-level, interactive programming language developed at CWI that emphasized readability and simplicity, and later influenced the design of Python.
  • 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_69a2e8004cb88190b92ed1add6abf41a completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ec8e655c819081eff85c0ef55fa5 completed Feb. 28, 2026, 1:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a413f275ac81908b6fd095a6d5a415 completed March 1, 2026, 10:24 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.