Triple

T17427376
Position Surface form Disambiguated ID Type / Status
Subject Charleroi Metro E423773 entity
Predicate hasStation P35 FINISHED
Object Parc station
Parc station is a metro stop on the Charleroi Metro network in Charleroi, Belgium.
 E1281464 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: Parc station | Statement: [Charleroi Metro, hasStation, Parc station]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Parc station
Context triple: [Charleroi Metro, hasStation, Parc station]
  • A. Parc station
    Parc station is a Montreal Metro station in the Parc-Extension area that serves the Blue Line and connects with commuter rail services.
  • B. Beaurepaire station
    Beaurepaire station is a commuter rail station serving the Beaurepaire area of Beaconsfield, a suburb on the Island of Montreal in Quebec, Canada.
  • C. Raspail station
    Raspail station is a Paris Métro station serving lines 4 and 6, located in the city's 14th arrondissement.
  • D. Saint-Just station
    Saint-Just station is a terminal stop on Lyon’s historic funicular network, serving the Saint-Just neighborhood in the city’s Fourvière area.
  • E. Vaucelles station
    Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
  • 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: Parc station
Triple: [Charleroi Metro, hasStation, Parc station]
Generated description
Parc station is a metro stop on the Charleroi Metro network in Charleroi, Belgium.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Parc station
Target entity description: Parc station is a metro stop on the Charleroi Metro network in Charleroi, Belgium.
  • A. Parc station
    Parc station is a Montreal Metro station in the Parc-Extension area that serves the Blue Line and connects with commuter rail services.
  • B. Beaurepaire station
    Beaurepaire station is a commuter rail station serving the Beaurepaire area of Beaconsfield, a suburb on the Island of Montreal in Quebec, Canada.
  • C. Raspail station
    Raspail station is a Paris Métro station serving lines 4 and 6, located in the city's 14th arrondissement.
  • D. Saint-Just station
    Saint-Just station is a terminal stop on Lyon’s historic funicular network, serving the Saint-Just neighborhood in the city’s Fourvière area.
  • E. Vaucelles station
    Vaucelles station is a railway station serving the commune of Taverny in the northern suburbs of Paris, France.
  • 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_69d889d88b6081908bada047f5b3ba51 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e448fdc1348190985db52c8c74c394 completed April 19, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02163f3c34819087f3a5d8c25a3c09 completed May 11, 2026, 5:47 p.m.
NEDg Description generation batch_6a021db3ea748190a453eeb6c70852e5 completed May 11, 2026, 6:19 p.m.
NED2 Entity disambiguation (via description) batch_6a021e1daa048190a2ccc566204b558d completed May 11, 2026, 6:21 p.m.
Created at: April 10, 2026, 5:46 a.m.