Triple

T12546192
Position Surface form Disambiguated ID Type / Status
Subject Jan Žižka E299974 entity
Predicate nativeLanguage P151 FINISHED
Object Czech language E73024 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: Czech language | Statement: [Jan Žižka, nativeLanguage, Czech language]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Czech language
Context triple: [Jan Žižka, nativeLanguage, Czech language]
  • A. Czech language chosen
    Czech language is a West Slavic language spoken primarily in the Czech Republic and known for its rich literary tradition and complex grammar.
  • B. Czech–Slovak languages
    The Czech–Slovak languages are a closely related group of Slavic languages, primarily including Czech and Slovak, spoken in Central Europe.
  • C. Slovak language
    The Slovak language is a West Slavic language spoken primarily in Slovakia and closely related to Czech and Polish.
  • D. Letiny
    Letiny is a small municipality and village located in the Plzeň Region of the Czech Republic.
  • E. Middle Czech
    Middle Czech is a historical stage of the Czech language used roughly between the 15th and 17th centuries, marking the transition from Old Czech to Modern Czech.
  • 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_69d6ada707008190aaec1238117c9379 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9547f9a1c81908f54c58a116a8446 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f655801cac8190b1f9a72f8fed0399 completed May 2, 2026, 7:50 p.m.
Created at: April 8, 2026, 9:57 p.m.