Triple

T24989156
Position Surface form Disambiguated ID Type / Status
Subject نظامی گنجوی E625401 entity
Predicate وابستگی زبانی P138982 FINISHED
Object ادبیات فارسی LITERAL 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: ادبیات فارسی | Statement: [نظامی گنجوی, وابستگی زبانی, ادبیات فارسی]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: وابستگی زبانی
Context triple: [نظامی گنجوی, وابستگی زبانی, ادبیات فارسی]
  • A. languageDependency chosen
    Indicates that one entity’s behavior, interpretation, or validity depends on or is constrained by a particular language or linguistic system associated with another entity.
  • B. linguisticIsolation
    Indicates a condition where an entity is separated from others in terms of language, lacking shared or effective linguistic communication.
  • C. linguisticFeature
    Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
  • D. linguisticallyRelatedTo
    Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
  • E. partOfLanguage
    Indicates that one linguistic element belongs to, is included within, or is a component of a particular language.
  • F. None of above.

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_69e2ff2611c081908710457fbe6d376b completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f44a42f61c8190bcb01ceec1f889b1 completed May 1, 2026, 6:37 a.m.
PD Predicate disambiguation batch_69f442c0c2e88190acd7f170f10ccef6 completed May 1, 2026, 6:05 a.m.
Created at: April 18, 2026, 6:03 a.m.