Triple

T8690349
Position Surface form Disambiguated ID Type / Status
Subject Lais Ribeiro E206270 entity
Predicate givenName P17 FINISHED
Object Laís
Laís is a Brazilian given name notably borne by model Laís Ribeiro, recognized for her work with major international fashion brands.
 E750662 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: Laís | Statement: [Lais Ribeiro, givenName, Laís]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Laís
Context triple: [Lais Ribeiro, givenName, Laís]
  • A. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Rafaela
    Rafaela is a major city in central Argentina known for its agricultural industry and role as a regional economic center.
  • D. Suzana
    Suzana is the birth name of American actress Sasha Alexander, known for her roles in television series such as NCIS and Rizzoli & Isles.
  • E. Lilia
    Lilia is a feminine given name, often considered a variant of Lily and associated with the elegance and symbolism of the lily flower.
  • 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: Laís
Triple: [Lais Ribeiro, givenName, Laís]
Generated description
Laís is a Brazilian given name notably borne by model Laís Ribeiro, recognized for her work with major international fashion brands.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Laís
Target entity description: Laís is a Brazilian given name notably borne by model Laís Ribeiro, recognized for her work with major international fashion brands.
  • A. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Rafaela
    Rafaela is a major city in central Argentina known for its agricultural industry and role as a regional economic center.
  • D. Suzana
    Suzana is the birth name of American actress Sasha Alexander, known for her roles in television series such as NCIS and Rizzoli & Isles.
  • E. Lilia
    Lilia is a feminine given name, often considered a variant of Lily and associated with the elegance and symbolism of the lily flower.
  • 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_69ca835481fc819084e33d3bc883bfa6 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5734602c81909a0687e00f4a4a26 completed March 31, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69cef3df73b88190b67138ee5129de8b completed April 2, 2026, 10:55 p.m.
NEDg Description generation batch_69cef52200788190a8173da1aaa4f681 completed April 2, 2026, 11 p.m.
NED2 Entity disambiguation (via description) batch_69cef6c109b08190bb29ce2747f3ccb7 completed April 2, 2026, 11:07 p.m.
Created at: March 30, 2026, 6:33 p.m.