Triple
T20069200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dependency grammar |
E499688
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Functional Generative Description |
—
|
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: Functional Generative Description | Statement: [dependency grammar, hasVariant, Functional Generative Description]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Functional Generative Description Context triple: [dependency grammar, hasVariant, Functional Generative Description]
-
A.
Word Formation in Generative Grammar
Word Formation in Generative Grammar is a foundational linguistics monograph that systematically analyzes how words are structured and created within the framework of generative grammar.
-
B.
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.
-
C.
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.
-
D.
Generative Phonology: Description and Theory
Generative Phonology: Description and Theory is a foundational textbook in theoretical linguistics that systematically presents the principles and methods of generative phonology.
-
E.
Standard Theory of generative grammar
The Standard Theory of generative grammar is an early framework in Noam Chomsky’s generative linguistics that formalizes how deep structures are transformed into surface structures to explain the syntax of natural languages.
- 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: Functional Generative Description Target entity description: Functional Generative Description is a theoretical framework within dependency grammar that models sentence structure through functionally motivated, generative rules linking form and meaning.
-
A.
Word Formation in Generative Grammar
Word Formation in Generative Grammar is a foundational linguistics monograph that systematically analyzes how words are structured and created within the framework of generative grammar.
-
B.
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.
-
C.
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.
-
D.
Generative Phonology: Description and Theory
Generative Phonology: Description and Theory is a foundational textbook in theoretical linguistics that systematically presents the principles and methods of generative phonology.
-
E.
Standard Theory of generative grammar
The Standard Theory of generative grammar is an early framework in Noam Chomsky’s generative linguistics that formalizes how deep structures are transformed into surface structures to explain the syntax of natural languages.
- 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.