Triple

T16286283
Position Surface form Disambiguated ID Type / Status
Subject Karel Gut E395395 entity
Predicate givenName P17 FINISHED
Object Karel E71855 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: Karel | Statement: [Karel Gut, givenName, Karel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karel
Context triple: [Karel Gut, givenName, Karel]
  • A. Karel chosen
    Karel is a given name, commonly used in Central and Eastern Europe, that corresponds to the English name Charles.
  • B. Karel Lamač
    Karel Lamač was a Czech film director, actor, and screenwriter active in the early 20th century, known for his prolific work in European cinema and frequent collaborations with star actress Anny Ondra.
  • C. Karel Roden
    Karel Roden is a Czech actor known internationally for his roles in films such as "Hellboy," "The Bourne Supremacy," and various European and Hollywood productions.
  • D. Havlíček
    Havlíček is a Czech surname most famously associated with basketball Hall of Famer John Havlicek and several notable Czech cultural and public figures.
  • E. Ondrík
    Ondrík is a Slovak diminutive form of the male given name Ondrej, used as an affectionate or familiar variant.
  • 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_69e24914bda08190a5d6315414ee3f76 completed April 17, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0017caaa448190a5034ddbd90d2fd5 completed May 10, 2026, 5:29 a.m.
Created at: April 10, 2026, 5:05 a.m.