Triple

T18449767
Position Surface form Disambiguated ID Type / Status
Subject Anna Leopoldowna E450748 entity
Predicate given name P17 FINISHED
Object Katharina 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: Katharina | Statement: [Anna Leopoldowna, given name, Katharina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Katharina
Context triple: [Anna Leopoldowna, given name, Katharina]
  • A. Katharina chosen
    Katharina is a feminine given name, commonly used in German-speaking and other European countries, that is a variant of Katherine/Catherine.
  • B. Katherina
    Katherina is the sharp-tongued, strong-willed heroine of Shakespeare’s comedy *The Taming of the Shrew*, whose fiery personality and contentious courtship drive the play’s central conflict.
  • C. Katherina
    Katherina is the given first name of Katia Mann, the wife of German novelist Thomas Mann.
  • D. Verena
    Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
  • E. Gertrudis
    Gertrudis is a passionate and rebellious sister in "Like Water for Chocolate" whose fiery nature and unconventional choices challenge her family's strict traditions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8d38345688190b565eac2e4cd7935 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5264748dc8190984501af3e4b2036 completed April 19, 2026, 7 p.m.
Created at: April 10, 2026, 11:30 a.m.