Triple

T8714877
Position Surface form Disambiguated ID Type / Status
Subject Marienplatz U-Bahn station E206868 entity
Predicate servedByLine P1293 FINISHED
Object S3
S3 is a line of the Munich S-Bahn suburban rail network that connects central Munich with its surrounding metropolitan area.
 E753871 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: S3 | Statement: [Marienplatz U-Bahn station, servedByLine, S3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: S3
Context triple: [Marienplatz U-Bahn station, servedByLine, S3]
  • A. S3
    S3 is a line of the Berlin S-Bahn urban rail network that connects various districts across the Berlin metropolitan area.
  • B. 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.
  • 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. S33
    S33 is a UK postcode district in the Hope Valley area of Derbyshire, covering several rural villages within the Peak District National Park.
  • E. S2
    S2 is a line of Berlin's S-Bahn rapid transit network that connects northern and southern suburbs through the city center.
  • 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: S3
Triple: [Marienplatz U-Bahn station, servedByLine, S3]
Generated description
S3 is a line of the Munich S-Bahn suburban rail network that connects central Munich with its surrounding metropolitan area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: S3
Target entity description: S3 is a line of the Munich S-Bahn suburban rail network that connects central Munich with its surrounding metropolitan area.
  • A. S3
    S3 is a line of the Berlin S-Bahn urban rail network that connects various districts across the Berlin metropolitan area.
  • B. 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.
  • 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. S33
    S33 is a UK postcode district in the Hope Valley area of Derbyshire, covering several rural villages within the Peak District National Park.
  • E. S2
    S2 is a line of Berlin's S-Bahn rapid transit network that connects northern and southern suburbs through the city center.
  • 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_69ca83572d4881909bef3be2b578d539 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5cd6707c819092c9fca34f273d5e completed March 31, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf28d62af88190acf2d8692d73b9f5 completed April 3, 2026, 2:41 a.m.
NEDg Description generation batch_69cf2bd222b08190907ba7e98991996e completed April 3, 2026, 2:54 a.m.
NED2 Entity disambiguation (via description) batch_69cf2fcb5e7c819086b441d1ef4fc368 completed April 3, 2026, 3:11 a.m.
Created at: March 30, 2026, 6:35 p.m.