Triple

T1449556
Position Surface form Disambiguated ID Type / Status
Subject Verena Huber-Dyson E31257 entity
Predicate givenName P17 FINISHED
Object Verena
Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
 E165554 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: Verena | Statement: [Verena Huber-Dyson, givenName, Verena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Verena
Context triple: [Verena Huber-Dyson, givenName, Verena]
  • A. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
  • B. Ricarda
    Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
  • C. Dorothee
    Dorothee is a feminine given name, commonly used in German- and French-speaking countries, that is a variant of the name Dorothea.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • 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. 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: Verena
Triple: [Verena Huber-Dyson, givenName, Verena]
Generated description
Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Verena
Target entity description: Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
  • A. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
  • B. Ricarda
    Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
  • C. Dorothee
    Dorothee is a feminine given name, commonly used in German- and French-speaking countries, that is a variant of the name Dorothea.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • 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. 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_69a499171a28819085b993a3ac78e363 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c55c408c8190917ed44d9070a2fb completed March 1, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad08c4c940819091d15c4d2ffa6b1c completed March 8, 2026, 5:27 a.m.
NEDg Description generation batch_69ad09d0c4d88190b5f79a92821ef577 completed March 8, 2026, 5:32 a.m.
NED2 Entity disambiguation (via description) batch_69ad0a5eeac08190a4d13f4819fc1aba completed March 8, 2026, 5:34 a.m.
Created at: March 1, 2026, 8 p.m.