Triple

T25146649
Position Surface form Disambiguated ID Type / Status
Subject Friedrichshagen S-Bahn station E629951 entity
Predicate adjacentStationOnLine S3 P41425 FINISHED
Object Rahnsdorf station
Rahnsdorf station is a Berlin S-Bahn railway stop in the borough of Treptow-Köpenick serving the suburban area of Rahnsdorf on the city's eastern outskirts.
 E1736124 NE FINISHED

How this triple was built (3 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: Rahnsdorf station | Statement: [Friedrichshagen S-Bahn station, adjacentStationOnLine S3, Rahnsdorf station]
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: Rahnsdorf station
Triple: [Friedrichshagen S-Bahn station, adjacentStationOnLine S3, Rahnsdorf station]
Generated description
Rahnsdorf station is a Berlin S-Bahn railway stop in the borough of Treptow-Köpenick serving the suburban area of Rahnsdorf on the city's eastern outskirts.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: adjacentStationOnLine S3
Context triple: [Friedrichshagen S-Bahn station, adjacentStationOnLine S3, Rahnsdorf station]
  • A. adjacentStationOnLine3
    Indicates that one station is directly next to another station along transit line 3, with no other stations in between on that line.
  • B. adjacentStationOnLine chosen
    Indicates that one station is directly next to another station along the same transit line, with no other station in between.
  • C. adjacentStationOnLineD
    Indicates that one station is directly next to another station along line D, with no other stations in between on that line.
  • D. adjacentStationOnLine2
    Indicates that one station is directly next to another station along line 2 in a transit or rail network.
  • E. adjacentStationOnLineU2
    Indicates that two stations are directly next to each other on subway line U2, with no other station in between.
  • F. None of above.

Provenance (6 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_69e2ff349e408190a6f4a5a66279f54d completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f638d11c988190af7fd4572b08e038 completed May 2, 2026, 5:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11ebe8b6148190bd3ffd2a7a7fa011 completed May 23, 2026, 6:03 p.m.
NEDg Description generation batch_6a11efbbc08081908061e4a0703c16e8 completed May 23, 2026, 6:19 p.m.
NED2 Entity disambiguation (via description) batch_6a11f014db348190a497218396a16e4b completed May 23, 2026, 6:21 p.m.
PD Predicate disambiguation batch_69f63706b6008190993577193c85ff50 completed May 2, 2026, 5:40 p.m.
Created at: April 18, 2026, 6:30 a.m.