Triple

T20960007
Position Surface form Disambiguated ID Type / Status
Subject Hamburg S-Bahn E516213 entity
Predicate hasLine P35 FINISHED
Object S31
S31 is a suburban rapid transit line of the Hamburg S-Bahn network in Germany, serving various districts across the city and its metropolitan area.
 E1459248 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: S31 | Statement: [Hamburg S-Bahn, hasLine, S31]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: S31
Context triple: [Hamburg S-Bahn, hasLine, S31]
  • A. S33
    S33 is a UK postcode district in the Hope Valley area of Derbyshire, covering several rural villages within the Peak District National Park.
  • B. S30
    S30 is the pennant number of HMS Vigilant, a Royal Navy Vanguard-class nuclear-powered ballistic missile submarine.
  • C. S3
    S3 is one of the commuter rail lines of the Nuremberg S-Bahn network in Germany, serving regional passenger traffic between the city and its surrounding areas.
  • D. S3
    S3 is a commuter rail line of the Stuttgart S-Bahn network in Germany, connecting the city center with surrounding suburban areas.
  • E. S3
    S3 is a commuter rail line within Germany’s Rhine-Ruhr S-Bahn network, serving regional passenger traffic across the metropolitan area.
  • 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: S31
Triple: [Hamburg S-Bahn, hasLine, S31]
Generated description
S31 is a suburban rapid transit line of the Hamburg S-Bahn network in Germany, serving various districts across the city and its metropolitan area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: S31
Target entity description: S31 is a suburban rapid transit line of the Hamburg S-Bahn network in Germany, serving various districts across the city and its metropolitan area.
  • A. S33
    S33 is a UK postcode district in the Hope Valley area of Derbyshire, covering several rural villages within the Peak District National Park.
  • B. S30
    S30 is the pennant number of HMS Vigilant, a Royal Navy Vanguard-class nuclear-powered ballistic missile submarine.
  • C. S3
    S3 is a commuter rail line of the Stuttgart S-Bahn network in Germany, connecting the city center with surrounding suburban areas.
  • D. S3
    S3 is a commuter rail line within Germany’s Rhine-Ruhr S-Bahn network, serving regional passenger traffic across the metropolitan area.
  • E. S3
    S3 is a regional rail service designation used on the RER Vaud commuter rail network in the canton of Vaud, Switzerland.
  • 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_69e0b4fde6c48190af1398e7e734629e completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fb6e50988190a564d2aaf1a9bc54 completed April 21, 2026, 4:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0927969140819089027995f14d86ee completed May 17, 2026, 2:27 a.m.
NEDg Description generation batch_6a0928e11bc481909b4350770db28671 completed May 17, 2026, 2:33 a.m.
NED2 Entity disambiguation (via description) batch_6a0929e0fb308190a224fe9dceb74a1c completed May 17, 2026, 2:37 a.m.
Created at: April 16, 2026, 1:30 p.m.