Triple

T8181243
Position Surface form Disambiguated ID Type / Status
Subject Line A (Buenos Aires Underground) E191064 entity
Predicate hasStation P35 FINISHED
Object Lima station
Lima station is an underground metro station on Buenos Aires’ Line A, serving the city’s central area.
 E720351 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: Lima station | Statement: [Line A (Buenos Aires Underground), hasStation, Lima station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lima station
Context triple: [Line A (Buenos Aires Underground), hasStation, Lima station]
  • A. La Paz station
    La Paz station is a Mexico City Metro terminal station serving as the eastern endpoint of Line A in the State of Mexico.
  • B. Bolívar station
    Bolívar station is a stop on the Buenos Aires Underground (Subte) network, serving Line E in the historic center of Argentina’s capital.
  • C. San Pablo station
    San Pablo station is an interchange station in the Santiago Metro network that connects Line 5 with other lines in the western part of Santiago, Chile.
  • D. Santiago Bueras station
    Santiago Bueras station is an underground stop on Santiago’s Metro network serving Line 5 in the western part of the city.
  • E. Miramar station
    Miramar station is a passenger rail station on the Valparaíso Metro system in Chile, serving the coastal city of Viña del Mar.
  • 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: Lima station
Triple: [Line A (Buenos Aires Underground), hasStation, Lima station]
Generated description
Lima station is an underground metro station on Buenos Aires’ Line A, serving the city’s central area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lima station
Target entity description: Lima station is an underground metro station on Buenos Aires’ Line A, serving the city’s central area.
  • A. La Paz station
    La Paz station is a Mexico City Metro terminal station serving as the eastern endpoint of Line A in the State of Mexico.
  • B. Bolívar station
    Bolívar station is a stop on the Buenos Aires Underground (Subte) network, serving Line E in the historic center of Argentina’s capital.
  • C. San Pablo station
    San Pablo station is an interchange station in the Santiago Metro network that connects Line 5 with other lines in the western part of Santiago, Chile.
  • D. Santiago Bueras station
    Santiago Bueras station is an underground stop on Santiago’s Metro network serving Line 5 in the western part of the city.
  • E. Miramar station
    Miramar station is a passenger rail station on the Valparaíso Metro system in Chile, serving the coastal city of Viña del Mar.
  • 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_69ca82c4538081909404325aa5639483 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4c4c2e388190b86854f8b1765e61 completed March 31, 2026, 4:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd3489fd8c8190a919aff6e3b3df31 completed April 1, 2026, 3:06 p.m.
NEDg Description generation batch_69cd36ef47e88190ae96ea2459552247 completed April 1, 2026, 3:17 p.m.
NED2 Entity disambiguation (via description) batch_69cd4e9d362481908283d58b03c7ef9b completed April 1, 2026, 4:58 p.m.
Created at: March 30, 2026, 5:40 p.m.