Triple

T16290500
Position Surface form Disambiguated ID Type / Status
Subject Alcover E395507 entity
Predicate hasOfficialLanguage P236 FINISHED
Object Catalan E5109 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: Catalan | Statement: [Alcover, hasOfficialLanguage, Catalan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Catalan
Context triple: [Alcover, hasOfficialLanguage, Catalan]
  • A. Catalan chosen
    Catalan is a Romance language spoken primarily in Catalonia, Valencia, the Balearic Islands, and parts of eastern Spain and southern France.
  • B. Catalão
    Catalão is a municipality in the southeastern part of the Brazilian state of Goiás, known for its agriculture, mining activities, and growing industrial sector.
  • C. Catalan Wikipedia
    Catalan Wikipedia is the Catalan-language edition of the free, collaboratively edited online encyclopedia Wikipedia.
  • D. Valencian Spanish
    Valencian Spanish is a regional variety of Spanish spoken in the Valencian Community in eastern Spain, characterized by influences from both standard Spanish and the local Valencian (Catalan) language.
  • E. Catalan Wikisource
    Catalan Wikisource is the Catalan-language edition of Wikisource, a free online digital library of public domain and freely licensed texts.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2491821d0819086cffdd7551ba85a completed April 17, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a001f94ede48190835e8a0c6f5d0f19 completed May 10, 2026, 6:03 a.m.
Created at: April 10, 2026, 5:05 a.m.