Triple

T14497005
Position Surface form Disambiguated ID Type / Status
Subject Universidad del Rosario E359526 entity
Predicate shortName P43 FINISHED
Object Rosario
Rosario is a prestigious private university in Bogotá, Colombia, known for its historic role in the country’s political and academic life.
 E1103232 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: Rosario | Statement: [Universidad del Rosario, shortName, Rosario]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rosario
Context triple: [Universidad del Rosario, shortName, Rosario]
  • A. Rosario
    Rosario is a coastal municipality in the Mexican state of Sinaloa known for its historic architecture, mining heritage, and proximity to the Pacific Ocean.
  • B. Rosario
    Rosario is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • C. Rosario
    Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
  • D. Rosario
    Rosario is a feminine given name of Spanish and Italian origin, commonly associated with the Roman Catholic devotion to the Rosary.
  • E. Rosario
    Rosario is a first-class agricultural municipality in the province of Batangas in the Philippines, known for its coconut and rice farming.
  • 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: Rosario
Triple: [Universidad del Rosario, shortName, Rosario]
Generated description
Rosario is a prestigious private university in Bogotá, Colombia, known for its historic role in the country’s political and academic life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rosario
Target entity description: Rosario is a prestigious private university in Bogotá, Colombia, known for its historic role in the country’s political and academic life.
  • A. Rosario
    Rosario is a major Argentine port city and industrial center located in the province of Santa Fe.
  • B. Rosario
    Rosario is a coastal municipality in the Mexican state of Sinaloa known for its historic architecture, mining heritage, and proximity to the Pacific Ocean.
  • C. Rosario
    Rosario is a first-class agricultural municipality in the province of Batangas in the Philippines, known for its coconut and rice farming.
  • D. Rosario
    Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
  • E. Rosario
    Rosario is a feminine given name of Spanish and Italian origin, commonly associated with the Roman Catholic devotion to the Rosary.
  • 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_69d8279740308190af9df93a3af8592e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de93109cb081909a6e846db23a4635 completed April 14, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd6d9731588190b27a826582e5fc6d completed May 8, 2026, 4:59 a.m.
NEDg Description generation batch_69fd6f82453481909a3e1b032f30a7a5 completed May 8, 2026, 5:07 a.m.
NED2 Entity disambiguation (via description) batch_69fd708521c881909863b7cd3fc4a313 completed May 8, 2026, 5:11 a.m.
Created at: April 10, 2026, 1:21 a.m.