Triple

T1345974
Position Surface form Disambiguated ID Type / Status
Subject Sinaloa E28571 entity
Predicate hasMunicipalities P747 FINISHED
Object 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.
 E154228 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: [Sinaloa, hasMunicipalities, Rosario]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rosario
Context triple: [Sinaloa, hasMunicipalities, Rosario]
  • A. Rosario
    Rosario is a major Argentine port city and industrial center located in the province of Santa Fe.
  • B. El Rosario
    El Rosario is a major Mexico City transit hub and neighborhood that serves as a key terminus and interchange point for multiple public transportation lines.
  • C. La Boca
    La Boca is a colorful, working-class neighborhood in Buenos Aires famous for its vividly painted houses, tango culture, and the Boca Juniors football stadium.
  • D. Belén
    Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
  • E. Somosta
    Somosta is a town located within Egypt's Beni Suef Governorate, known as one of the local urban centers in this Upper Egyptian region.
  • 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: [Sinaloa, hasMunicipalities, Rosario]
Generated description
Rosario is a coastal municipality in the Mexican state of Sinaloa known for its historic architecture, mining heritage, and proximity to the Pacific Ocean.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rosario
Target entity description: Rosario is a coastal municipality in the Mexican state of Sinaloa known for its historic architecture, mining heritage, and proximity to the Pacific Ocean.
  • A. Rosario
    Rosario is a major Argentine port city and industrial center located in the province of Santa Fe.
  • B. El Rosario
    El Rosario is a major Mexico City transit hub and neighborhood that serves as a key terminus and interchange point for multiple public transportation lines.
  • C. La Boca
    La Boca is a colorful, working-class neighborhood in Buenos Aires famous for its vividly painted houses, tango culture, and the Boca Juniors football stadium.
  • D. Belén
    Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
  • E. Somosta
    Somosta is a town located within Egypt's Beni Suef Governorate, known as one of the local urban centers in this Upper Egyptian region.
  • 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_69a49854eb3481908c7d56b2e449a290 completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c23e84188190b0395c57dd45b62a completed March 1, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc639201c81908ed9c9ac37cd358f completed March 8, 2026, 12:43 a.m.
NEDg Description generation batch_69acc71a3e808190aecbb57a64f39b6b completed March 8, 2026, 12:47 a.m.
NED2 Entity disambiguation (via description) batch_69acc88e4ec08190945b366524b83088 completed March 8, 2026, 12:53 a.m.
Created at: March 1, 2026, 7:56 p.m.