Triple

T798404
Position Surface form Disambiguated ID Type / Status
Subject O’Higgins Region E17072 entity
Predicate hasMajorCity P316 FINISHED
Object San Fernando
San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
 E101533 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: [O’Higgins Region, hasMajorCity, San Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Fernando
Context triple: [O’Higgins Region, hasMajorCity, 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. Rosarito
    Rosarito is a coastal resort city in northern Baja California, Mexico, known for its beaches, tourism, and proximity to the U.S. border.
  • C. San Antonio de los Baños
    San Antonio de los Baños is a Cuban town known for its film school and cultural traditions, located southwest of Havana.
  • D. Vallejo
    Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
  • E. Toa Baja
    Toa Baja is a coastal municipality in northern Puerto Rico, known for its proximity to San Juan and its mix of urban, industrial, and residential areas.
  • 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: [O’Higgins Region, hasMajorCity, San Fernando]
Generated description
San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San Fernando
Target entity description: San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • 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. Rosarito
    Rosarito is a coastal resort city in northern Baja California, Mexico, known for its beaches, tourism, and proximity to the U.S. border.
  • C. San Antonio de los Baños
    San Antonio de los Baños is a Cuban town known for its film school and cultural traditions, located southwest of Havana.
  • D. Vallejo
    Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
  • E. Toa Baja
    Toa Baja is a coastal municipality in northern Puerto Rico, known for its proximity to San Juan and its mix of urban, industrial, and residential areas.
  • 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_69a49378b9c48190adbf5f62e5b7aca1 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a7b4d9548190aad5fdf1211cf8cd completed March 1, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7a3b1a81481908c831d1f43b9d014 completed March 4, 2026, 3:14 a.m.
NEDg Description generation batch_69a7a5929de48190b01f45fbf19c72dc completed March 4, 2026, 3:22 a.m.
NED2 Entity disambiguation (via description) batch_69a7a63205308190b55b76116c0d5e7c completed March 4, 2026, 3:25 a.m.
Created at: March 1, 2026, 7:38 p.m.