Triple

T16058888
Position Surface form Disambiguated ID Type / Status
Subject Argentine modern architecture E389555 entity
Predicate developedInCity P33964 FINISHED
Object Rosario
Rosario is a major Argentine city known for its influential role in shaping the country’s modern architecture and urban landscape.
 E99633 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: [Argentine modern architecture, developedInCity, Rosario]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rosario
Context triple: [Argentine modern architecture, developedInCity, Rosario]
  • A. Rosario
    Rosario is a prestigious private university in Bogotá, Colombia, known for its historic role in the country’s political and academic life.
  • B. Rosario
    Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
  • C. Rosario
    Rosario is a feminine given name of Spanish and Italian origin, commonly associated with the Roman Catholic devotion to the Rosary.
  • D. 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.
  • E. Rosario
    Rosario is a coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • 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: [Argentine modern architecture, developedInCity, Rosario]
Generated description
Rosario is a major Argentine city known for its influential role in shaping the country’s modern architecture and urban landscape.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rosario
Target entity description: Rosario is a major Argentine city known for its influential role in shaping the country’s modern architecture and urban landscape.
  • A. Rosario chosen
    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 prestigious private university in Bogotá, Colombia, known for its historic role in the country’s political and academic life.
  • 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 first-class agricultural municipality in the province of Batangas in the Philippines, known for its coconut and rice farming.
  • F. None of above.

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_69d86dae698881908327ef2d67706cb9 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1837729e4819086e7429e0a76b0d7 completed April 17, 2026, 12:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbe678fc8190b36737a9cd29691c completed May 10, 2026, 1:14 a.m.
NEDg Description generation batch_69ffdd2e0c38819091a57098985fa19e completed May 10, 2026, 1:19 a.m.
NED2 Entity disambiguation (via description) batch_69ffde20bd488190a06cedf311e37c80 completed May 10, 2026, 1:23 a.m.
Created at: April 10, 2026, 4:57 a.m.