Triple

T11646931
Position Surface form Disambiguated ID Type / Status
Subject Praterstern railway station E276798 entity
Predicate railwayLine P848 FINISHED
Object S1 line
The S1 line is a route of the Vienna S-Bahn suburban rail network that connects central Vienna with surrounding areas, including service through major hubs like Praterstern.
 E938848 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: S1 line | Statement: [Praterstern railway station, railwayLine, S1 line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: S1 line
Context triple: [Praterstern railway station, railwayLine, S1 line]
  • A. S1 line
    The S1 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area and surrounding region.
  • B. S1 Line
    The S1 Line is a rapid transit route within the Nanjing Metro system in Nanjing, China, providing urban rail service along one of the city’s key corridors.
  • C. S1 Line
    The S1 Line is a medium-capacity maglev rapid transit line in the Beijing Subway system serving the western suburbs of the city.
  • D. S4 Line
    The S4 Line is a rapid transit route within the Nanjing Metro system in Nanjing, China.
  • E. S2 line
    The S2 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area, including central hubs such as Frankfurt Hauptwache station.
  • 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: S1 line
Triple: [Praterstern railway station, railwayLine, S1 line]
Generated description
The S1 line is a route of the Vienna S-Bahn suburban rail network that connects central Vienna with surrounding areas, including service through major hubs like Praterstern.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: S1 line
Target entity description: The S1 line is a route of the Vienna S-Bahn suburban rail network that connects central Vienna with surrounding areas, including service through major hubs like Praterstern.
  • A. S1 line
    The S1 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area and surrounding region.
  • B. S1 Line
    The S1 Line is a rapid transit route within the Nanjing Metro system in Nanjing, China, providing urban rail service along one of the city’s key corridors.
  • C. S1 Line
    The S1 Line is a medium-capacity maglev rapid transit line in the Beijing Subway system serving the western suburbs of the city.
  • D. S4 Line
    The S4 Line is a rapid transit route within the Nanjing Metro system in Nanjing, China.
  • E. S2 line
    The S2 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan area, including central hubs such as Frankfurt Hauptwache station.
  • 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_69d6aafbb3c081908a9cdb4ecb8d981d completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a2cc8bfc8190a063cc37de9596a9 completed April 10, 2026, 7:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69ee87f903c48190b9055ad4cfebb1e1 completed April 26, 2026, 9:47 p.m.
NEDg Description generation batch_69eeb314eef0819091a949bbfc820ee9 completed April 27, 2026, 12:51 a.m.
NED2 Entity disambiguation (via description) batch_69eee9a79d348190bb3e15f0d37b9d57 completed April 27, 2026, 4:44 a.m.
Created at: April 8, 2026, 9:39 p.m.