Triple

T11248164
Position Surface form Disambiguated ID Type / Status
Subject Morazán Department E266259 entity
Predicate hasMunicipality P847 FINISHED
Object San Fernando
San Fernando is a municipality located in the Morazán Department of northeastern El Salvador, known for its rural character and mountainous surroundings.
 E914103 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: [Morazán Department, hasMunicipality, San Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Fernando
Context triple: [Morazán Department, 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 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 coastal municipality located in the island province of Romblon in the Philippines.
  • E. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • 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: [Morazán Department, hasMunicipality, San Fernando]
Generated description
San Fernando is a municipality located in the Morazán Department of northeastern El Salvador, known for its rural character and mountainous surroundings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San Fernando
Target entity description: San Fernando is a municipality located in the Morazán Department of northeastern El Salvador, known for its rural character and mountainous surroundings.
  • A. San Fernando
    San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
  • B. 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.
  • C. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • D. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • E. 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.
  • 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_69d6aac7953c8190b82caf9d7640fdf9 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e91d1484819098ee6b2efb5316a5 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4cc76b10c8190962b21c4cad6ce8f completed April 19, 2026, 12:37 p.m.
NEDg Description generation batch_69e4d9eb79608190b7ed108906f4e2bf completed April 19, 2026, 1:34 p.m.
NED2 Entity disambiguation (via description) batch_69e4df50eebc8190a4fe0aba7dc9fa62 completed April 19, 2026, 1:57 p.m.
Created at: April 8, 2026, 9:31 p.m.