Triple

T20069201
Position Surface form Disambiguated ID Type / Status
Subject dependency grammar E499688 entity
Predicate hasVariant P455 FINISHED
Object Link Grammar — NE NERFINISHED

How this triple was built (3 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: Link Grammar | Statement: [dependency grammar, hasVariant, Link Grammar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Link Grammar
Context triple: [dependency grammar, hasVariant, Link Grammar]
  • A. Tree Adjoining Grammar
    Tree Adjoining Grammar is a highly structured formal grammar framework in computational linguistics used to model the syntax of natural languages with greater expressive power than context-free grammars.
  • B. dependency grammar
    Dependency grammar is a syntactic theory that analyzes sentence structure in terms of binary relations between words, focusing on how each word depends on a governing head rather than on phrase-structure constituents.
  • C. General and Rational Grammar
    General and Rational Grammar is a 17th-century French linguistic treatise from the Port-Royal school that seeks to explain the universal, rational principles underlying all human languages.
  • D. Lexical-Functional Grammar
    Lexical-Functional Grammar is a non-transformational theory of syntax that models sentence structure through parallel levels of representation, emphasizing the relationship between grammatical functions and lexical information.
  • E. Hakka grammars
    Hakka grammars are linguistic descriptions and reference works that analyze the structure, usage, and rules of the Hakka Chinese language and its dialects.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Link Grammar
Target entity description: Link Grammar is a syntactic parsing framework that connects words in a sentence via labeled links according to a lexicon of linking requirements, providing a rule-based alternative to traditional phrase-structure grammars.
  • A. Tree Adjoining Grammar
    Tree Adjoining Grammar is a highly structured formal grammar framework in computational linguistics used to model the syntax of natural languages with greater expressive power than context-free grammars.
  • B. dependency grammar
    Dependency grammar is a syntactic theory that analyzes sentence structure in terms of binary relations between words, focusing on how each word depends on a governing head rather than on phrase-structure constituents.
  • C. General and Rational Grammar
    General and Rational Grammar is a 17th-century French linguistic treatise from the Port-Royal school that seeks to explain the universal, rational principles underlying all human languages.
  • D. Lexical-Functional Grammar
    Lexical-Functional Grammar is a non-transformational theory of syntax that models sentence structure through parallel levels of representation, emphasizing the relationship between grammatical functions and lexical information.
  • E. Hakka grammars
    Hakka grammars are linguistic descriptions and reference works that analyze the structure, usage, and rules of the Hakka Chinese language and its dialects.
  • F. None of above. chosen

Provenance (2 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_69da627770948190997f486f9a2e370f completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e664365ad0819089103b00d1cf8c9f completed April 20, 2026, 5:36 p.m.
Created at: April 11, 2026, 3:39 p.m.