Triple

T3409490
Position Surface form Disambiguated ID Type / Status
Subject Karel E71855 entity
Predicate hasLanguageUsage P207 FINISHED
Object Hungarian E15030 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: Hungarian | Statement: [Karel, hasLanguageUsage, Hungarian]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hungarian
Context triple: [Karel, hasLanguageUsage, Hungarian]
  • A. Hungarian language chosen
    The Hungarian language is a Uralic language spoken primarily in Hungary, known for its agglutinative grammar, extensive case system, and vocabulary distinct from most other European languages.
  • B. Hungarians
    Hungarians are a Uralic-speaking ethnic group native primarily to Hungary and the Carpathian Basin, known for their distinct language, culture, and historical kingdom in Central Europe.
  • C. Hungarian Wikinews
    Hungarian Wikinews is the Hungarian-language edition of the Wikinews project, offering collaboratively written, free-content news articles.
  • D. Ferenc (Hungarian)
    Ferenc is the Hungarian given name equivalent to Francisco, commonly used as a male first name in Hungary.
  • E. Mátraháza
    Mátraháza is a small mountain resort village in northern Hungary, known for its scenic location in the Mátra range and its hiking and wellness tourism.
  • 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_69ad85ac312481909e7027ced1456a9f completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb90754788190ab85e2bec020f99e completed March 8, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69b34bdd99248190823875cae2531609 completed March 12, 2026, 11:27 p.m.
Created at: March 8, 2026, 3:15 p.m.