Triple

T1747683
Position Surface form Disambiguated ID Type / Status
Subject Marisa del Toro E38371 entity
Predicate hasGivenName P17 FINISHED
Object Marisa
Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
 E195715 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: Marisa | Statement: [Marisa del Toro, hasGivenName, Marisa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marisa
Context triple: [Marisa del Toro, hasGivenName, Marisa]
  • A. Valeria
    Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
  • B. Valeria
    Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
  • C. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • D. Reona
    Reona is the Japanese given name of Nobel Prize–winning physicist Leo Esaki, known for his pioneering work on quantum tunneling and semiconductor devices.
  • E. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • 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: Marisa
Triple: [Marisa del Toro, hasGivenName, Marisa]
Generated description
Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marisa
Target entity description: Marisa is a feminine given name of Latin origin, commonly used in Spanish- and Italian-speaking cultures.
  • A. Valeria
    Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
  • B. Valeria
    Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
  • C. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • D. Reona
    Reona is the Japanese given name of Nobel Prize–winning physicist Leo Esaki, known for his pioneering work on quantum tunneling and semiconductor devices.
  • E. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • 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_69a8862b01a48190ab47209063af82d9 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa63ecda0c819091f81942a5bde31d completed March 6, 2026, 5:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0e058948190939e936af8f0e221 completed March 8, 2026, 4:16 p.m.
NEDg Description generation batch_69ada1a2122481909c7a3470e090af17 completed March 8, 2026, 4:19 p.m.
NED2 Entity disambiguation (via description) batch_69ada23515d08190833ad1a35bb7a265 completed March 8, 2026, 4:22 p.m.
Created at: March 4, 2026, 7:31 p.m.