Triple

T18506337
Position Surface form Disambiguated ID Type / Status
Subject WNYC Studios E452206 entity
Predicate produces P490 FINISHED
Object La Brega
La Brega is a narrative podcast exploring Puerto Rican history, culture, and politics through personal stories and investigative reporting.
 E1327740 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: La Brega | Statement: [WNYC Studios, produces, La Brega]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: La Brega
Context triple: [WNYC Studios, produces, La Brega]
  • A. Bailén
    Bailén is a town in the province of Jaén in Andalusia, Spain, historically known for the 1808 Battle of Bailén during the Peninsular War.
  • B. Rascafría
    Rascafría is a mountain village and municipality in the Sierra de Guadarrama of central Spain, known for its natural landscapes and the nearby Monastery of El Paular.
  • C. Brihuega
    Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
  • D. Caleruega
    Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
  • E. Nalón
    The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
  • 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: La Brega
Triple: [WNYC Studios, produces, La Brega]
Generated description
La Brega is a narrative podcast exploring Puerto Rican history, culture, and politics through personal stories and investigative reporting.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: La Brega
Target entity description: La Brega is a narrative podcast exploring Puerto Rican history, culture, and politics through personal stories and investigative reporting.
  • A. Bailén
    Bailén is a town in the province of Jaén in Andalusia, Spain, historically known for the 1808 Battle of Bailén during the Peninsular War.
  • B. Rascafría
    Rascafría is a mountain village and municipality in the Sierra de Guadarrama of central Spain, known for its natural landscapes and the nearby Monastery of El Paular.
  • C. Brihuega
    Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
  • D. Caleruega
    Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
  • E. Nalón
    The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
  • 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_69d8d386df84819092355ebb260d848e completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5334266708190b59aca3a2218c095 completed April 19, 2026, 7:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0471470bfc8190bf64c367d2e37a81 completed May 13, 2026, 12:40 p.m.
NEDg Description generation batch_6a047dabf1e481908179fd57c83215d0 completed May 13, 2026, 1:33 p.m.
NED2 Entity disambiguation (via description) batch_6a047e5148a08190b3288b2a07c7ed78 completed May 13, 2026, 1:36 p.m.
Created at: April 10, 2026, 11:36 a.m.