Triple

T22354993
Position Surface form Disambiguated ID Type / Status
Subject Farruko E552627 entity
Predicate notableWork P4 FINISHED
Object “Si Se Da”
“Si Se Da” is a popular reggaeton/Latin urban track by Puerto Rican singer Farruko known for its catchy rhythm and club-oriented sound.
 E1532872 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: “Si Se Da” | Statement: [Farruko, notableWork, “Si Se Da”]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: “Si Se Da”
Context triple: [Farruko, notableWork, “Si Se Da”]
  • A. "Si Te Vas"
    "Si Te Vas" is a popular Spanish-language song by Cuban-American singer Jon Secada, known for its romantic themes and Latin pop style.
  • B. “Donna Donna”
    “Donna Donna” is a Yiddish-origin folk song that became widely known in the English-speaking world through Joan Baez’s 1960s recordings and performances.
  • C. Al Di La
    "Al Di La" is a popular Italian song best known in the English-speaking world through Al Hirt’s hit instrumental recording.
  • D. No Sé Tú
    "No Sé Tú" is a romantic bolero ballad by Mexican singer Luis Miguel, featured on his acclaimed 1991 album *Romances*.
  • E. Mas Que Nada
    "Mas Que Nada" is a globally popular Brazilian song, originally written and recorded by Jorge Ben and later famously popularized in a bossa nova/samba style by Sérgio Mendes & Brasil '66.
  • 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: “Si Se Da”
Triple: [Farruko, notableWork, “Si Se Da”]
Generated description
“Si Se Da” is a popular reggaeton/Latin urban track by Puerto Rican singer Farruko known for its catchy rhythm and club-oriented sound.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: “Si Se Da”
Target entity description: “Si Se Da” is a popular reggaeton/Latin urban track by Puerto Rican singer Farruko known for its catchy rhythm and club-oriented sound.
  • A. "Si Te Vas"
    "Si Te Vas" is a popular Spanish-language song by Cuban-American singer Jon Secada, known for its romantic themes and Latin pop style.
  • B. “Donna Donna”
    “Donna Donna” is a Yiddish-origin folk song that became widely known in the English-speaking world through Joan Baez’s 1960s recordings and performances.
  • C. Al Di La
    "Al Di La" is a popular Italian song best known in the English-speaking world through Al Hirt’s hit instrumental recording.
  • D. No Sé Tú
    "No Sé Tú" is a romantic bolero ballad by Mexican singer Luis Miguel, featured on his acclaimed 1991 album *Romances*.
  • E. Mas Que Nada
    "Mas Que Nada" is a globally popular Brazilian song, originally written and recorded by Jorge Ben and later famously popularized in a bossa nova/samba style by Sérgio Mendes & Brasil '66.
  • 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_69e11e4a0ad08190a385b4d343cf6524 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f157ceb2308190941f6507e605a612 completed April 29, 2026, 12:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ae05e243081908fb6067c80b213e1 completed May 18, 2026, 9:48 a.m.
NEDg Description generation batch_6a0ae2c1dc888190a32541b120332e88 completed May 18, 2026, 9:58 a.m.
NED2 Entity disambiguation (via description) batch_6a0ae3855838819080bf6883946b62f7 completed May 18, 2026, 10:01 a.m.
Created at: April 16, 2026, 8:44 p.m.