Triple

T1769050
Position Surface form Disambiguated ID Type / Status
Subject Magdalena E38830 entity
Predicate hasVariant P455 FINISHED
Object Malena
Malena is a feminine given name, commonly used in various cultures and often considered a diminutive or variant of names like Magdalena.
 E201623 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: Malena | Statement: [Magdalena, hasVariant, Malena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Malena
Context triple: [Magdalena, hasVariant, Malena]
  • A. Marlene
    Marlene is a German biographical film directed by Joseph Vilsmaier about the life and career of actress and singer Marlene Dietrich.
  • B. Eva
    Eva is a feminine given name of Hebrew origin, equivalent to "Eve" and widely used in many languages and cultures.
  • C. Brigitte
    Brigitte is a French former teacher best known as the wife of Emmanuel Macron, the President of France.
  • D. Verena
    Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
  • E. Dorothee
    Dorothee is a feminine given name, commonly used in German- and French-speaking countries, that is a variant of the name Dorothea.
  • 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: Malena
Triple: [Magdalena, hasVariant, Malena]
Generated description
Malena is a feminine given name, commonly used in various cultures and often considered a diminutive or variant of names like Magdalena.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Malena
Target entity description: Malena is a feminine given name, commonly used in various cultures and often considered a diminutive or variant of names like Magdalena.
  • A. Marlene
    Marlene is a German biographical film directed by Joseph Vilsmaier about the life and career of actress and singer Marlene Dietrich.
  • B. Eva
    Eva is a feminine given name of Hebrew origin, equivalent to "Eve" and widely used in many languages and cultures.
  • C. Brigitte
    Brigitte is a French former teacher best known as the wife of Emmanuel Macron, the President of France.
  • D. Verena
    Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
  • E. Dorothee
    Dorothee is a feminine given name, commonly used in German- and French-speaking countries, that is a variant of the name Dorothea.
  • 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_69a8862e61708190af97b9838cc3f5de completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa648d9f2c8190aca4884648a69eb0 completed March 6, 2026, 5:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69adb5c727e48190b934e9b97b084c7a completed March 8, 2026, 5:45 p.m.
NEDg Description generation batch_69adb8b3c0a48190bf5f3a32d8862c54 completed March 8, 2026, 5:58 p.m.
NED2 Entity disambiguation (via description) batch_69adb97b8c8081909a806d16efd5882b completed March 8, 2026, 6:01 p.m.
Created at: March 4, 2026, 7:31 p.m.