Triple

T8237244
Position Surface form Disambiguated ID Type / Status
Subject Romblon E192440 entity
Predicate hasMunicipality P847 FINISHED
Object San Fernando
San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
 E720815 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: San Fernando | Statement: [Romblon, hasMunicipality, San Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Fernando
Context triple: [Romblon, hasMunicipality, San Fernando]
  • A. San Fernando
    San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
  • B. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • C. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • D. San Fernando
    San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, known for its naval base, salt marshes, and historical role in the Spanish War of Independence.
  • E. San Fernando
    San Fernando is a locality within the municipality of Huixquilucan in the State of Mexico, forming part of the greater Mexico City metropolitan area.
  • 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: San Fernando
Triple: [Romblon, hasMunicipality, San Fernando]
Generated description
San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San Fernando
Target entity description: San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
  • A. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • B. San Fernando
    San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, known for its naval base, salt marshes, and historical role in the Spanish War of Independence.
  • C. San Fernando
    San Fernando is a locality within the municipality of Huixquilucan in the State of Mexico, forming part of the greater Mexico City metropolitan area.
  • D. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • E. San Fernando
    San Fernando is a small independent city in Los Angeles County, California, surrounded by but administratively separate from the San Fernando Valley region of Los Angeles.
  • 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_69ca82dc8f148190a2c75a98501a7b91 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb783929c081909db1182947755bae completed March 31, 2026, 7:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd34fb069c8190965d69fae908482e completed April 1, 2026, 3:08 p.m.
NEDg Description generation batch_69cd37a47f3c81909491e9b0316c32f0 completed April 1, 2026, 3:20 p.m.
NED2 Entity disambiguation (via description) batch_69cd4ecc8d68819099932b1ecefc0d36 completed April 1, 2026, 4:58 p.m.
Created at: March 30, 2026, 5:47 p.m.