Triple

T757781
Position Surface form Disambiguated ID Type / Status
Subject Nuremberg U-Bahn E15595 entity
Predicate fareSystem P395 FINISHED
Object VGN
VGN (Verkehrsverbund Großraum Nürnberg) is the public transport association that coordinates and manages integrated ticketing and services across the greater Nuremberg metropolitan area in Germany.
 E90420 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: VGN | Statement: [Nuremberg U-Bahn, fareSystem, VGN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VGN
Context triple: [Nuremberg U-Bahn, fareSystem, VGN]
  • A. VS
    VS is the two-letter abbreviation commonly used for the Swiss canton of Valais.
  • B. VZ
    VZ is the stock ticker symbol for Verizon Communications Inc., a major U.S.-based telecommunications company providing wireless, internet, and related services.
  • C. VNM
    VNM is the three-letter ISO 3166-1 alpha-3 country code assigned to Vietnam.
  • D. VWAG
    VWAG is the stock ticker symbol under which the multinational automotive manufacturer Volkswagen Group is publicly traded.
  • E. VGIK
    VGIK is Russia’s renowned national film school and one of the world’s oldest film institutes, known for training influential filmmakers such as Sergei Eisenstein.
  • 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: VGN
Triple: [Nuremberg U-Bahn, fareSystem, VGN]
Generated description
VGN (Verkehrsverbund Großraum Nürnberg) is the public transport association that coordinates and manages integrated ticketing and services across the greater Nuremberg metropolitan area in Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VGN
Target entity description: VGN (Verkehrsverbund Großraum Nürnberg) is the public transport association that coordinates and manages integrated ticketing and services across the greater Nuremberg metropolitan area in Germany.
  • A. VS
    VS is the two-letter abbreviation commonly used for the Swiss canton of Valais.
  • B. VZ
    VZ is the stock ticker symbol for Verizon Communications Inc., a major U.S.-based telecommunications company providing wireless, internet, and related services.
  • C. VNM
    VNM is the three-letter ISO 3166-1 alpha-3 country code assigned to Vietnam.
  • D. VWAG
    VWAG is the stock ticker symbol under which the multinational automotive manufacturer Volkswagen Group is publicly traded.
  • E. VGIK
    VGIK is Russia’s renowned national film school and one of the world’s oldest film institutes, known for training influential filmmakers such as Sergei Eisenstein.
  • 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_69a493599a0081908da65f3407af1ef2 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a66c2e108190a754c60d2eac6676 completed March 1, 2026, 8:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69a65e426adc8190b7fa65aeacf8737f completed March 3, 2026, 4:06 a.m.
NEDg Description generation batch_69a65fea3b0c819089690f928bbe7bbd completed March 3, 2026, 4:13 a.m.
NED2 Entity disambiguation (via description) batch_69a660290dc881908130db992636fa57 completed March 3, 2026, 4:14 a.m.
Created at: March 1, 2026, 7:37 p.m.