Triple

T4142708
Position Surface form Disambiguated ID Type / Status
Subject Valparaíso Metro E89306 entity
Predicate hasStation P35 FINISHED
Object Barón station
Barón station is a passenger rail stop on the Valparaíso Metro system in Valparaíso, Chile, serving the coastal urban area.
 E415114 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: Barón station | Statement: [Valparaíso Metro, hasStation, Barón station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Barón station
Context triple: [Valparaíso Metro, hasStation, Barón station]
  • A. Belen station
    Belen station is a commuter rail station in Belen, New Mexico, serving as a key stop on the New Mexico Rail Runner Express line.
  • B. Príncipe Pío station
    Príncipe Pío station is a major intermodal transport hub in Madrid, Spain, combining commuter rail, metro, and bus services in a historic former railway terminal.
  • C. La Granja station
    La Granja station is a stop on Madrid Metro’s Line 4A serving the La Granja area in the city’s rapid transit network.
  • D. Julius-Leber-Brücke station
    Julius-Leber-Brücke station is a Berlin S-Bahn railway stop located in the Schöneberg district of Germany’s capital.
  • E. Südkreuz station
    Südkreuz station is a major Berlin transport hub serving regional, long-distance, and S-Bahn trains in the southern part of the city.
  • 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: Barón station
Triple: [Valparaíso Metro, hasStation, Barón station]
Generated description
Barón station is a passenger rail stop on the Valparaíso Metro system in Valparaíso, Chile, serving the coastal urban area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Barón station
Target entity description: Barón station is a passenger rail stop on the Valparaíso Metro system in Valparaíso, Chile, serving the coastal urban area.
  • A. Belen station
    Belen station is a commuter rail station in Belen, New Mexico, serving as a key stop on the New Mexico Rail Runner Express line.
  • B. Príncipe Pío station
    Príncipe Pío station is a major intermodal transport hub in Madrid, Spain, combining commuter rail, metro, and bus services in a historic former railway terminal.
  • C. La Granja station
    La Granja station is a stop on Madrid Metro’s Line 4A serving the La Granja area in the city’s rapid transit network.
  • D. Julius-Leber-Brücke station
    Julius-Leber-Brücke station is a Berlin S-Bahn railway stop located in the Schöneberg district of Germany’s capital.
  • E. Südkreuz station
    Südkreuz station is a major Berlin transport hub serving regional, long-distance, and S-Bahn trains in the southern part of 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_69aed95785788190ae75bcf0cd1cafdf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af024cc7e88190b23b39d6f5f2a2e0 completed March 9, 2026, 5:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69b576cff6c881909134804ba6f9876d completed March 14, 2026, 2:55 p.m.
NEDg Description generation batch_69b577d391ac8190b6062b1f64e2e7e8 completed March 14, 2026, 2:59 p.m.
NED2 Entity disambiguation (via description) batch_69b5787ed214819092fc425152069df9 completed March 14, 2026, 3:02 p.m.
Created at: March 9, 2026, 3:43 p.m.