Triple

T8199267
Position Surface form Disambiguated ID Type / Status
Subject Université de Hearst E191525 entity
Predicate campusLanguagePolicy P73903 FINISHED
Object French-language environment 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: French-language environment | Statement: [Université de Hearst, campusLanguagePolicy, French-language environment]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: campusLanguagePolicy
Context triple: [Université de Hearst, campusLanguagePolicy, French-language environment]
  • A. languageAcademyPolicy
    Indicates a policy or set of rules established by a language academy regarding language use, standards, or regulation.
  • B. hasOfficialLanguagePolicy
    Indicates that there exists a formally adopted rule or set of rules governing the use, status, or regulation of one or more languages within a given context or jurisdiction.
  • C. campusUse
    Indicates that something is intended for, associated with, or occurring in the use or activities of a campus or campus community.
  • D. governsCampus
    Indicates that one entity has administrative or authoritative control over the operations and policies of a campus.
  • E. languagePolicyType chosen
    Indicates the specific category or type of language policy that governs how languages are used, managed, or regulated in a given context.
  • 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_69ca82c6e9548190a4c5ca14516e4417 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb5df426cc81908b676d3d6852e29f completed March 31, 2026, 5:39 a.m.
PD Predicate disambiguation batch_69cb36aac86081909b83636e352e0ced completed March 31, 2026, 2:51 a.m.
Created at: March 30, 2026, 5:42 p.m.