Triple

T38036700
Position Surface form Disambiguated ID Type / Status
Subject Gonzales, Louisiana E949369 entity
Predicate secondaryLanguagePresence P9103 FINISHED
Object Louisiana French NE NERFINISHED

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: Louisiana French | Statement: [Gonzales, Louisiana, secondaryLanguagePresence, Louisiana French]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: secondaryLanguagePresence
Context triple: [Gonzales, Louisiana, secondaryLanguagePresence, Louisiana French]
  • A. secondaryLanguageSupport
    Indicates that an entity provides assistance, services, or functionality in an additional (non-primary) language.
  • B. hasSecondaryLanguage chosen
    Indicates that an entity possesses or uses a secondary language in addition to its primary language.
  • C. secondaryLanguageContext
    Indicates that the associated information, interaction, or content occurs within or is tailored to a secondary (non-primary) language setting or usage context.
  • D. hasSecondaryNationalLanguage
    Indicates that an entity possesses an officially recognized secondary national language in addition to its primary national language.
  • E. laterSecondaryLanguageOfAdministration
    Indicates that one language served as a subsequent or later secondary language used for administrative purposes in relation to another 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_69f76eff0bb0819084bc4e63997bd039 completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69ffc3f0b19c8190b5a749bc3cad21dd completed May 9, 2026, 11:32 p.m.
PD Predicate disambiguation batch_69ffc1b882808190932b2d43ea5537c9 completed May 9, 2026, 11:22 p.m.
Created at: May 3, 2026, 4:20 p.m.