Triple

T10574962
Position Surface form Disambiguated ID Type / Status
Subject Rodalies de Catalunya E249583 entity
Predicate hasService P182 FINISHED
Object R30
R30 is a regional commuter rail line within the Rodalies de Catalunya network serving the Barcelona metropolitan area and surrounding regions.
 E871756 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: R30 | Statement: [Rodalies de Catalunya, hasService, R30]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: R30
Context triple: [Rodalies de Catalunya, hasService, R30]
  • A. R3
    R3 is one of the general-purpose registers in the PDP-11 minicomputer architecture, used for arithmetic, logical operations, and addressing.
  • B. R37
    R37 is a regional road in South Africa that serves as a key route connecting the town of Lydenburg with other parts of the Mpumalanga and Limpopo provinces.
  • C. R36
    R36 is a regional road in South Africa that connects the town of Lydenburg with other major routes and settlements in the region.
  • D. R230
    The R230 is the fifth-generation Mercedes-Benz SL-Class, a luxury two-seat roadster produced in the 2000s known for its retractable hardtop and advanced technology features.
  • E. R29
    R29 is the internal station code used by the New York City Subway system to identify the 7th Avenue station on the BMT Brighton Line.
  • 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: R30
Triple: [Rodalies de Catalunya, hasService, R30]
Generated description
R30 is a regional commuter rail line within the Rodalies de Catalunya network serving the Barcelona metropolitan area and surrounding regions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: R30
Target entity description: R30 is a regional commuter rail line within the Rodalies de Catalunya network serving the Barcelona metropolitan area and surrounding regions.
  • A. R3
    R3 is one of the general-purpose registers in the PDP-11 minicomputer architecture, used for arithmetic, logical operations, and addressing.
  • B. R37
    R37 is a regional road in South Africa that serves as a key route connecting the town of Lydenburg with other parts of the Mpumalanga and Limpopo provinces.
  • C. R36
    R36 is a regional road in South Africa that connects the town of Lydenburg with other major routes and settlements in the region.
  • D. R230
    The R230 is the fifth-generation Mercedes-Benz SL-Class, a luxury two-seat roadster produced in the 2000s known for its retractable hardtop and advanced technology features.
  • E. R29
    R29 is the internal station code used by the New York City Subway system to identify the 7th Avenue station on the BMT Brighton Line.
  • 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.