Triple

T10574942
Position Surface form Disambiguated ID Type / Status
Subject Rodalies de Catalunya E249583 entity
Predicate hasService P182 FINISHED
Object R8
R8 is a commuter rail line in the Rodalies de Catalunya network that connects various towns in the Barcelona metropolitan area without passing through the city center.
 E871737 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: R8 | Statement: [Rodalies de Catalunya, hasService, R8]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: R8
Context triple: [Rodalies de Catalunya, hasService, R8]
  • A. R8
    The Audi R8 is a high-performance mid-engine sports car known for its powerful engines, quattro all-wheel drive, and use of advanced lightweight construction.
  • B. R98
    R98 is the hull number of the French aircraft carrier Clemenceau, a Cold War-era flagship of the French Navy.
  • C. R08
    R08 is the pennant number of HMS Queen Elizabeth, the lead ship of the Royal Navy’s Queen Elizabeth-class aircraft carriers and one of the largest warships ever built for the United Kingdom.
  • D. R5
    R5 is the U.S. Forest Service’s Pacific Southwest Region, which oversees national forests primarily in California and parts of neighboring areas.
  • E. R5
    R5 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • 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: R8
Triple: [Rodalies de Catalunya, hasService, R8]
Generated description
R8 is a commuter rail line in the Rodalies de Catalunya network that connects various towns in the Barcelona metropolitan area without passing through the city center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: R8
Target entity description: R8 is a commuter rail line in the Rodalies de Catalunya network that connects various towns in the Barcelona metropolitan area without passing through the city center.
  • A. R8
    The Audi R8 is a high-performance mid-engine sports car known for its powerful engines, quattro all-wheel drive, and use of advanced lightweight construction.
  • B. R98
    R98 is the hull number of the French aircraft carrier Clemenceau, a Cold War-era flagship of the French Navy.
  • C. R08
    R08 is the pennant number of HMS Queen Elizabeth, the lead ship of the Royal Navy’s Queen Elizabeth-class aircraft carriers and one of the largest warships ever built for the United Kingdom.
  • D. R5
    R5 is the U.S. Forest Service’s Pacific Southwest Region, which oversees national forests primarily in California and parts of neighboring areas.
  • E. R5
    R5 is a government office building in Oslo that forms part of Norway’s central Regjeringskvartalet complex.
  • 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_69d381c8bd708190acf3d275c908251e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d52749dda08190b0c9627a931c5848 completed April 7, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b5d89748190bb398943e4a16e9b completed April 10, 2026, 7:11 p.m.
NEDg Description generation batch_69d94e1502108190a81bfa1d5a425e5a completed April 10, 2026, 7:23 p.m.
NED2 Entity disambiguation (via description) batch_69d94f0bb6888190b4038df6dcd96d33 completed April 10, 2026, 7:27 p.m.
Created at: April 6, 2026, 12:38 p.m.