Triple

T11398038
Position Surface form Disambiguated ID Type / Status
Subject Pinzón E270030 entity
Predicate hasNotableBearer P458 FINISHED
Object Miguel Pinzón
Miguel Pinzón is a Colombian actor and television personality known for his roles in Spanish-language telenovelas and series.
E926973 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: Miguel Pinzón | Statement: [Pinzón, hasNotableBearer, Miguel Pinzón]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Miguel Pinzón
Context triple: [Pinzón, hasNotableBearer, Miguel Pinzón]
  • A. Jorge Pinzón
    Jorge Pinzón is a notable individual who shares the Spanish surname Pinzón, historically associated with prominent figures in exploration and public life.
  • B. Andrés Pinzón
    Andrés Pinzón is a person notable enough to be specifically cited as a bearer of the surname Pinzón.
  • C. Sebastián Ramírez
    Sebastián Ramírez is a software developer best known for creating the modern, high-performance Python web framework FastAPI.
  • D. Andrés Páez de Sotomayor
    Andrés Páez de Sotomayor was a Spanish colonial figure known as the founder of the Colombian city of Bucaramanga.
  • E. Agustín de Betancourt
    Agustín de Betancourt was a Spanish engineer and architect renowned for his pioneering work in civil engineering and urban planning across Europe and the Russian Empire.
  • 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: Miguel Pinzón
Triple: [Pinzón, hasNotableBearer, Miguel Pinzón]
Generated description
Miguel Pinzón is a Colombian actor and television personality known for his roles in Spanish-language telenovelas and series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Miguel Pinzón
Target entity description: Miguel Pinzón is a Colombian actor and television personality known for his roles in Spanish-language telenovelas and series.
  • A. Jorge Pinzón
    Jorge Pinzón is a notable individual who shares the Spanish surname Pinzón, historically associated with prominent figures in exploration and public life.
  • B. Andrés Pinzón
    Andrés Pinzón is a person notable enough to be specifically cited as a bearer of the surname Pinzón.
  • C. Sebastián Ramírez
    Sebastián Ramírez is a software developer best known for creating the modern, high-performance Python web framework FastAPI.
  • D. Andrés Páez de Sotomayor
    Andrés Páez de Sotomayor was a Spanish colonial figure known as the founder of the Colombian city of Bucaramanga.
  • E. Agustín de Betancourt
    Agustín de Betancourt was a Spanish engineer and architect renowned for his pioneering work in civil engineering and urban planning across Europe and the Russian Empire.
  • 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_69d6aacdbc6c8190af6dc3d5f5d22836 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d80019d3d48190a2f473deb6eae33a completed April 9, 2026, 7:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e8cc338081908da977b5b7c6bef3 completed April 20, 2026, 8:50 a.m.
NEDg Description generation batch_69e5f1557e9c8190b53ce391793b2c7f completed April 20, 2026, 9:26 a.m.
NED2 Entity disambiguation (via description) batch_69e5f863bf7c81908969ed0a5b99f032 completed April 20, 2026, 9:56 a.m.
Created at: April 8, 2026, 9:34 p.m.