Triple

T2387951
Position Surface form Disambiguated ID Type / Status
Subject Katarina E48870 entity
Predicate relatedName P3889 FINISHED
Object Katharina
Katharina is a feminine given name, commonly used in German-speaking and other European countries, that is a variant of Katherine/Catherine.
 E263534 NE FINISHED

How this triple was built (4 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: [Katarina, relatedName, Katharina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Katharina
Context triple: [Katarina, relatedName, Katharina]
  • A. Katherina
    Katherina is the given first name of Katia Mann, the wife of German novelist Thomas Mann.
  • B. Verena
    Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
  • C. 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.
  • D. Ricarda
    Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
  • E. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Katharina
Triple: [Katarina, relatedName, Katharina]
Generated description
Katharina is a feminine given name, commonly used in German-speaking and other European countries, that is a variant of Katherine/Catherine.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Katharina
Target entity description: Katharina is a feminine given name, commonly used in German-speaking and other European countries, that is a variant of Katherine/Catherine.
  • A. Katherina
    Katherina is the given first name of Katia Mann, the wife of German novelist Thomas Mann.
  • B. Verena
    Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
  • C. 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.
  • D. Ricarda
    Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
  • E. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
  • F. None of above. chosen

Provenance (5 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_69a88aa5f63081908d07fd302029fcbd completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc7db4ee08190a5c114e42bc46f70 completed March 7, 2026, 6:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3cf53088190ba42e03d2de2b36e completed March 9, 2026, 11:49 a.m.
NEDg Description generation batch_69aeb48dfbfc81908193c909315bd030 completed March 9, 2026, 11:52 a.m.
NED2 Entity disambiguation (via description) batch_69aeb57af28c8190bfca30ad3e7ca8b3 completed March 9, 2026, 11:56 a.m.
Created at: March 4, 2026, 7:57 p.m.