Triple

T17760735
Position Surface form Disambiguated ID Type / Status
Subject Miguel Cabanellas E443363 entity
Predicate familyName P18 FINISHED
Object Cabanellas
Cabanellas is a Spanish surname most notably associated with Miguel Cabanellas, a military figure involved in the early stages of the Spanish Civil War.
 E1287104 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: Cabanellas | Statement: [Miguel Cabanellas, familyName, Cabanellas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cabanellas
Context triple: [Miguel Cabanellas, familyName, Cabanellas]
  • A. Báguanos
    Báguanos is a municipality in eastern Cuba located in the province of Holguín, known for its agricultural activities and rural communities.
  • B. Cantarranas
    Cantarranas is a small, historic town in central Honduras known for its colorful street murals and traditional cultural festivals.
  • C. Camuñas
    Camuñas is a Spanish surname of likely toponymic origin, associated with individuals such as politician Ignacio Camuñas.
  • D. Turrubares
    Turrubares is a rural canton in Costa Rica known for its mountainous landscapes, agricultural activities, and low population density.
  • E. Las Cabras
    Las Cabras is a commune and town in Chile’s O'Higgins Region, known for its agricultural activity and location within the Cachapoal Valley wine-producing 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: Cabanellas
Triple: [Miguel Cabanellas, familyName, Cabanellas]
Generated description
Cabanellas is a Spanish surname most notably associated with Miguel Cabanellas, a military figure involved in the early stages of the Spanish Civil War.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cabanellas
Target entity description: Cabanellas is a Spanish surname most notably associated with Miguel Cabanellas, a military figure involved in the early stages of the Spanish Civil War.
  • A. Báguanos
    Báguanos is a municipality in eastern Cuba located in the province of Holguín, known for its agricultural activities and rural communities.
  • B. Cantarranas
    Cantarranas is a small, historic town in central Honduras known for its colorful street murals and traditional cultural festivals.
  • C. Camuñas
    Camuñas is a Spanish surname of likely toponymic origin, associated with individuals such as politician Ignacio Camuñas.
  • D. Turrubares
    Turrubares is a rural canton in Costa Rica known for its mountainous landscapes, agricultural activities, and low population density.
  • E. Las Cabras
    Las Cabras is a commune and town in Chile’s O'Higgins Region, known for its agricultural activity and location within the Cachapoal Valley wine-producing area.
  • 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_69d8b9edf16c8190a59ebd245d378f4f completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e485f7a8e08190a4a6b8368b70c381 completed April 19, 2026, 7:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02efbaa62c81908870dba8f0e2e55d completed May 12, 2026, 9:15 a.m.
NEDg Description generation batch_6a02f1232f348190ab28e8d5ed4d3ad3 completed May 12, 2026, 9:21 a.m.
NED2 Entity disambiguation (via description) batch_6a02f18beeb88190ac8cb9540b6b7b88 completed May 12, 2026, 9:23 a.m.
Created at: April 10, 2026, 10:11 a.m.