Triple

T5945174
Position Surface form Disambiguated ID Type / Status
Subject Universidad de la República (Uruguay) E132261 entity
Predicate hasCampusIn P4623 FINISHED
Object Rocha
Rocha is a coastal department in southeastern Uruguay known for its beaches, lagoons, and ecotourism.
 E557567 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: Rocha | Statement: [Universidad de la República (Uruguay), hasCampusIn, Rocha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rocha
Context triple: [Universidad de la República (Uruguay), hasCampusIn, Rocha]
  • A. Rocha
    Rocha is a Portuguese-origin surname common in Lusophone countries and among their diasporas.
  • B. Trancoso
    Trancoso is a historic Portuguese town in the Centro Region, known for its medieval walls, castle, and well-preserved old quarter.
  • C. Costa Alegre
    Costa Alegre is a scenic stretch of Mexico’s Pacific coastline in Jalisco known for its secluded beaches, luxury resorts, and unspoiled natural beauty.
  • D. Serra
    Serra is a Spanish surname most famously associated with Junípero Serra, the 18th-century Franciscan friar who founded several missions in what is now California.
  • E. Mauá
    Mauá is an industrial and residential city located in the metropolitan region of São Paulo, Brazil.
  • 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: Rocha
Triple: [Universidad de la República (Uruguay), hasCampusIn, Rocha]
Generated description
Rocha is a coastal department in southeastern Uruguay known for its beaches, lagoons, and ecotourism.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rocha
Target entity description: Rocha is a coastal department in southeastern Uruguay known for its beaches, lagoons, and ecotourism.
  • A. Rocha
    Rocha is a Portuguese-origin surname common in Lusophone countries and among their diasporas.
  • B. Trancoso
    Trancoso is a historic Portuguese town in the Centro Region, known for its medieval walls, castle, and well-preserved old quarter.
  • C. Costa Alegre
    Costa Alegre is a scenic stretch of Mexico’s Pacific coastline in Jalisco known for its secluded beaches, luxury resorts, and unspoiled natural beauty.
  • D. Serra
    Serra is a Spanish surname most famously associated with Junípero Serra, the 18th-century Franciscan friar who founded several missions in what is now California.
  • E. Mauá
    Mauá is an industrial and residential city located in the metropolitan region of São Paulo, Brazil.
  • 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_69c00869d3308190af89b2453e0f7546 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0393a10448190b0960f4487e87448 completed March 22, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0c084fce481909c306d6eeb99066d completed March 23, 2026, 4:24 a.m.
NEDg Description generation batch_69c0c19665b08190ab3c66b7c6c33f61 completed March 23, 2026, 4:29 a.m.
NED2 Entity disambiguation (via description) batch_69c0c4576824819080ced71df8fdda6c completed March 23, 2026, 4:40 a.m.
Created at: March 22, 2026, 4:01 p.m.