Triple

T13031933
Position Surface form Disambiguated ID Type / Status
Subject Marlon E326461 entity
Predicate hasVariant P455 FINISHED
Object Marlón
Marlón is a given name, typically a variant spelling of Marlon used in Spanish-speaking contexts.
 E1017093 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: Marlón | Statement: [Marlon, hasVariant, Marlón]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marlón
Context triple: [Marlon, hasVariant, Marlón]
  • A. Marcelo
    Marcelo is a common Portuguese and Spanish given name, notably borne by figures such as Brazilian footballer Marcelo Vieira and former Portuguese Prime Minister Marcelo Caetano.
  • B. Adrián Sosa
    Adrián Sosa is a musician best known as a member of the Latin Grammy–winning Río de la Plata music collective Bajofondo, which blends tango with electronic and contemporary styles.
  • C. Matías Romero
    Matías Romero was a prominent 19th-century Mexican diplomat, politician, and statesman known for his key role in strengthening Mexico–United States relations.
  • D. Nando Torres
    Nando Torres is a central character in the family comedy film "Yes Day," portrayed as one of the children whose parents agree to say yes to all of their requests for 24 hours.
  • E. Álvaro
    Álvaro is a masculine given name of Spanish origin commonly used in Spain and Latin America.
  • 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: Marlón
Triple: [Marlon, hasVariant, Marlón]
Generated description
Marlón is a given name, typically a variant spelling of Marlon used in Spanish-speaking contexts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marlón
Target entity description: Marlón is a given name, typically a variant spelling of Marlon used in Spanish-speaking contexts.
  • A. Marcelo
    Marcelo is a common Portuguese and Spanish given name, notably borne by figures such as Brazilian footballer Marcelo Vieira and former Portuguese Prime Minister Marcelo Caetano.
  • B. Adrián Sosa
    Adrián Sosa is a musician best known as a member of the Latin Grammy–winning Río de la Plata music collective Bajofondo, which blends tango with electronic and contemporary styles.
  • C. Matías Romero
    Matías Romero was a prominent 19th-century Mexican diplomat, politician, and statesman known for his key role in strengthening Mexico–United States relations.
  • D. Nando Torres
    Nando Torres is a central character in the family comedy film "Yes Day," portrayed as one of the children whose parents agree to say yes to all of their requests for 24 hours.
  • E. Álvaro
    Álvaro is a masculine given name of Spanish origin commonly used in Spain and Latin America.
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97efe72348190b52fb4068f5fb829 completed April 10, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbcd25108190a6c4a129cde81534 completed May 3, 2026, 4:15 a.m.
NEDg Description generation batch_69f6cd0d21e08190855dcbee000fc25d completed May 3, 2026, 4:20 a.m.
NED2 Entity disambiguation (via description) batch_69f6ce6b220c8190b1f49a9b2bfce692 completed May 3, 2026, 4:26 a.m.
Created at: April 9, 2026, 8:54 p.m.