Triple

T6258350
Position Surface form Disambiguated ID Type / Status
Subject Rosario Dawson E140230 entity
Predicate givenName P17 FINISHED
Object Rosario
Rosario is a feminine given name of Spanish and Italian origin, commonly associated with the Roman Catholic devotion to the Rosary.
 E580621 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: [Rosario Dawson, givenName, Rosario]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rosario
Context triple: [Rosario Dawson, givenName, Rosario]
  • 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 coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • 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. El Rosario
    El Rosario is a municipality on the island of Tenerife in Spain’s Canary Islands, known for its coastal landscapes and proximity to the island’s capital, Santa Cruz de Tenerife.
  • 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: [Rosario Dawson, givenName, Rosario]
Generated description
Rosario is a feminine given name of Spanish and Italian origin, commonly associated with the Roman Catholic devotion to the Rosary.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rosario
Target entity description: Rosario is a feminine given name of Spanish and Italian origin, commonly associated with the Roman Catholic devotion to the Rosary.
  • 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 coastal municipality in the province of Northern Samar in the Eastern Visayas region of the Philippines.
  • 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. El Rosario
    El Rosario is a municipality on the island of Tenerife in Spain’s Canary Islands, known for its coastal landscapes and proximity to the island’s capital, Santa Cruz de Tenerife.
  • 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_69c008c95c5c819084bd3dd56133d84d completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06367e0b48190967ebfb9bfbc9732 completed March 22, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c2443f0cb88190a32968fce0214045 completed March 24, 2026, 7:58 a.m.
NEDg Description generation batch_69c2471a08448190bb8d61f20403d44b completed March 24, 2026, 8:11 a.m.
NED2 Entity disambiguation (via description) batch_69c4fb839b2481909caa57f34a8837db completed March 26, 2026, 9:25 a.m.
Created at: March 22, 2026, 4:24 p.m.