Triple

T3503855
Position Surface form Disambiguated ID Type / Status
Subject Cavite E74028 entity
Predicate hasMunicipality P847 FINISHED
Object Rosario
Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
 E363942 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: [Cavite, hasMunicipality, Rosario]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rosario
Context triple: [Cavite, hasMunicipality, 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 province of Northern Samar in the Eastern Visayas region of the Philippines.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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: [Cavite, hasMunicipality, Rosario]
Generated description
Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rosario
Target entity description: Rosario is a coastal municipality in the province of Cavite in the Philippines, known for its fishing industry and proximity to Manila Bay.
  • 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. 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.
  • E. 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.
  • 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbbf0c2b48190b49923137bb9e45d completed March 8, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69b373db933881908557d678dcdee382 completed March 13, 2026, 2:18 a.m.
NEDg Description generation batch_69b377a690348190a765b021bbbc820c completed March 13, 2026, 2:34 a.m.
NED2 Entity disambiguation (via description) batch_69b3781aaab48190a497a0929966ec12 completed March 13, 2026, 2:36 a.m.
Created at: March 8, 2026, 3:18 p.m.