Triple

T7775109
Position Surface form Disambiguated ID Type / Status
Subject Metro de Medellín E179170 entity
Predicate hasLine P35 FINISHED
Object Line P
Line P is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the city’s main mass transit network.
 E688271 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: Line P | Statement: [Metro de Medellín, hasLine, Line P]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Line P
Context triple: [Metro de Medellín, hasLine, Line P]
  • A. line P
    Line P is a Transilien suburban rail line serving the eastern suburbs of the Paris metropolitan area.
  • B. Line L
    Line L is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the main urban transit network.
  • C. Line A
    Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
  • D. Line A
    Line A is one of the main tram lines serving the city of Reims, France, providing urban public transportation across key districts.
  • E. Line A
    Line A is the historic first subway line of the Buenos Aires Underground, known for its early 20th-century wooden cars and route through central neighborhoods.
  • 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: Line P
Triple: [Metro de Medellín, hasLine, Line P]
Generated description
Line P is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the city’s main mass transit network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Line P
Target entity description: Line P is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the city’s main mass transit network.
  • A. line P
    Line P is a Transilien suburban rail line serving the eastern suburbs of the Paris metropolitan area.
  • B. Line L
    Line L is a cable car line of the Medellín Metro system that serves hillside neighborhoods by connecting them to the main urban transit network.
  • C. Line A
    Line A is one of the main lines of the Prague Metro, running east–west through the city and serving several central and residential districts.
  • D. Line A
    Line A is the historic first subway line of the Buenos Aires Underground, known for its early 20th-century wooden cars and route through central neighborhoods.
  • E. Line A
    Line A is one of the main routes of the Strasbourg tramway network, providing key light-rail transit across the city.
  • 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_69c69f30602c819082ab52cd4af5c592 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c7046331e0819080ec1a5c23c27cd7 completed March 27, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8d6d65e308190924c05df5a0a4959 completed March 29, 2026, 7:37 a.m.
NEDg Description generation batch_69c8d779769c8190a9be6fbc065156e0 completed March 29, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_69c8d80a88f8819098bdb678e86f9be9 completed March 29, 2026, 7:43 a.m.
Created at: March 27, 2026, 4:11 p.m.