Triple

T8376560
Position Surface form Disambiguated ID Type / Status
Subject Buenos Aires Underground Line A E197590 entity
Predicate hasStation P35 FINISHED
Object Piedras
Piedras is a station on Buenos Aires’ historic Line A subway, serving the central Monserrat area near Avenida de Mayo.
 E729745 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: Piedras | Statement: [Buenos Aires Underground Line A, hasStation, Piedras]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Piedras
Context triple: [Buenos Aires Underground Line A, hasStation, Piedras]
  • A. Paredones
    Paredones is a rural commune and town in central Chile known for its agricultural activities and traditional countryside character.
  • B. Las Piedras
    Las Piedras is a municipality in eastern Puerto Rico known for its suburban communities and proximity to the island’s metropolitan area.
  • C. Las Piedras
    Las Piedras is a town in Uruguay historically notable as the site of the pivotal Battle of Las Piedras during the country’s struggle for independence.
  • D. Arbol de Piedra
    Arbol de Piedra is a famous wind-eroded rock formation in Bolivia’s high-altitude desert, noted for its tree-like shape and striking isolation in the Altiplano landscape.
  • E. Camuñas
    Camuñas is a Spanish surname of likely toponymic origin, associated with individuals such as politician Ignacio Camuñas.
  • 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: Piedras
Triple: [Buenos Aires Underground Line A, hasStation, Piedras]
Generated description
Piedras is a station on Buenos Aires’ historic Line A subway, serving the central Monserrat area near Avenida de Mayo.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Piedras
Target entity description: Piedras is a station on Buenos Aires’ historic Line A subway, serving the central Monserrat area near Avenida de Mayo.
  • A. Paredones
    Paredones is a rural commune and town in central Chile known for its agricultural activities and traditional countryside character.
  • B. Las Piedras
    Las Piedras is a municipality in eastern Puerto Rico known for its suburban communities and proximity to the island’s metropolitan area.
  • C. Las Piedras
    Las Piedras is a town in Uruguay historically notable as the site of the pivotal Battle of Las Piedras during the country’s struggle for independence.
  • D. Arbol de Piedra
    Arbol de Piedra is a famous wind-eroded rock formation in Bolivia’s high-altitude desert, noted for its tree-like shape and striking isolation in the Altiplano landscape.
  • E. Camuñas
    Camuñas is a Spanish surname of likely toponymic origin, associated with individuals such as politician Ignacio Camuñas.
  • 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_69ca82f64c188190af4e1608036b865d completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb80c094908190afe9cc54ce4f4d58 completed March 31, 2026, 8:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde7f19ba08190a08cf5aea522c021 completed April 2, 2026, 3:52 a.m.
NEDg Description generation batch_69cdebf944008190b7e758ac59257e22 completed April 2, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_69cdeccedf4081909cab853ee1ff1b82 completed April 2, 2026, 4:13 a.m.
Created at: March 30, 2026, 6:01 p.m.