Triple

T8308579
Position Surface form Disambiguated ID Type / Status
Subject Juan Uriagereka E194526 entity
Predicate hasNotableConcept P531 FINISHED
Object Syntactic Anchors framework E726991 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: Syntactic Anchors framework | Statement: [Juan Uriagereka, hasNotableConcept, Syntactic Anchors framework]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Syntactic Anchors framework
Context triple: [Juan Uriagereka, hasNotableConcept, Syntactic Anchors framework]
  • A. Syntactic Anchors chosen
    Syntactic Anchors is a linguistics monograph by Juan Uriagereka that develops a theory of syntactic structure within the generative grammar framework, focusing on how hierarchical phrase structures are anchored in grammatical systems.
  • B. Multiple Spell-Out model of syntax
    The Multiple Spell-Out model of syntax is a theoretical framework in generative grammar that proposes cyclic, phase-based transfer of syntactic structure to phonological and semantic components during derivation.
  • C. Types of A-bar Dependencies
    Types of A-bar Dependencies is a seminal linguistics monograph by Guglielmo Cinque that analyzes the structure and behavior of A-bar movement and related syntactic dependencies across languages.
  • D. Augmented Backus–Naur Form
    Augmented Backus–Naur Form (ABNF) is a standardized, extended version of Backus–Naur Form used to formally specify the syntax of languages and protocols, notably in Internet and communication standards.
  • E. Aho–Ullman algorithms for parsing
    Aho–Ullman algorithms for parsing are foundational compiler-construction techniques that efficiently analyze and translate the syntactic structure of programming languages based on formal grammar theory.
  • 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_69ca82e613e88190bf8139669bbd0d53 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7f2c06608190bd21633af07a530b completed March 31, 2026, 8 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc6e4eb808190b138c52810f35040 completed April 2, 2026, 1:31 a.m.
Created at: March 30, 2026, 5:54 p.m.