Triple

T8476495
Position Surface form Disambiguated ID Type / Status
Subject Leipzig Hauptbahnhof E200406 entity
Predicate railwayStationCode P1289 FINISHED
Object LEJ
LEJ is the station code for Leipzig Hauptbahnhof, one of Europe’s largest and busiest railway terminals located in Leipzig, Germany.
 E735553 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: LEJ | Statement: [Leipzig Hauptbahnhof, railwayStationCode, LEJ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LEJ
Context triple: [Leipzig Hauptbahnhof, railwayStationCode, LEJ]
  • A. LEZL
    LEZL is the ICAO airport code for Seville Airport, a major international airport serving the city of Seville in southern Spain.
  • B. LEAL
    LEAL is the ICAO airport code for Alicante–Elche Airport, the main international airport serving Spain’s Costa Blanca region.
  • C. LEW
    LEW is the IATA airport code for Auburn/Lewiston Municipal Airport in Maine, United States.
  • D. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • E. LAJ
    LAJ is the station code for La Junta station, an Amtrak railroad stop in La Junta, Colorado, serving long-distance passenger trains.
  • 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: LEJ
Triple: [Leipzig Hauptbahnhof, railwayStationCode, LEJ]
Generated description
LEJ is the station code for Leipzig Hauptbahnhof, one of Europe’s largest and busiest railway terminals located in Leipzig, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LEJ
Target entity description: LEJ is the station code for Leipzig Hauptbahnhof, one of Europe’s largest and busiest railway terminals located in Leipzig, Germany.
  • A. LEZL
    LEZL is the ICAO airport code for Seville Airport, a major international airport serving the city of Seville in southern Spain.
  • B. LEAL
    LEAL is the ICAO airport code for Alicante–Elche Airport, the main international airport serving Spain’s Costa Blanca region.
  • C. LEW
    LEW is the IATA airport code for Auburn/Lewiston Municipal Airport in Maine, United States.
  • D. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • E. LAJ
    LAJ is the station code for La Junta station, an Amtrak railroad stop in La Junta, Colorado, serving long-distance passenger trains.
  • 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_69ca831b17988190a1f3f3413d57b820 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe51e21548190811e3c7ba7b196e5 completed March 31, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce3a196ad48190b3887a2a0c43f87f completed April 2, 2026, 9:42 a.m.
NEDg Description generation batch_69ce3b1f6f7c8190927b5e2684ae207b completed April 2, 2026, 9:47 a.m.
NED2 Entity disambiguation (via description) batch_69ce3b9d38e081909be10cb209b15427 completed April 2, 2026, 9:49 a.m.
Created at: March 30, 2026, 6:12 p.m.