Triple

T5223785
Position Surface form Disambiguated ID Type / Status
Subject Venezia Santa Lucia railway station E117933 entity
Predicate hasStationCode P1289 FINISHED
Object VSL
VSL is the station code for Venezia Santa Lucia, the main railway terminal serving the historic center of Venice, Italy.
 E503092 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: VSL | Statement: [Venezia Santa Lucia railway station, hasStationCode, VSL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VSL
Context triple: [Venezia Santa Lucia railway station, hasStationCode, VSL]
  • A. VLKSM
    VLKSM was the Russian abbreviation for the All-Union Leninist Young Communist League, the Soviet Union’s official youth organization affiliated with the Communist Party.
  • B. VSELJ
    VSELJ is a student-edited law journal at the University of Virginia School of Law focusing on legal issues in sports and entertainment.
  • C. VIV
    VIV is the ICAO airline designator assigned to Viva Aerobus, a Mexican low-cost carrier.
  • D. Vuse
    Vuse is an electronic cigarette and vaping product brand owned by British American Tobacco, known for its range of nicotine e-liquids and devices.
  • E. VŠE
    VŠE is the commonly used abbreviation for the University of Economics in Prague, a leading Czech institution specializing in economics and business studies.
  • 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: VSL
Triple: [Venezia Santa Lucia railway station, hasStationCode, VSL]
Generated description
VSL is the station code for Venezia Santa Lucia, the main railway terminal serving the historic center of Venice, Italy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VSL
Target entity description: VSL is the station code for Venezia Santa Lucia, the main railway terminal serving the historic center of Venice, Italy.
  • A. VLKSM
    VLKSM was the Russian abbreviation for the All-Union Leninist Young Communist League, the Soviet Union’s official youth organization affiliated with the Communist Party.
  • B. VSELJ
    VSELJ is a student-edited law journal at the University of Virginia School of Law focusing on legal issues in sports and entertainment.
  • C. VIV
    VIV is the ICAO airline designator assigned to Viva Aerobus, a Mexican low-cost carrier.
  • D. Vuse
    Vuse is an electronic cigarette and vaping product brand owned by British American Tobacco, known for its range of nicotine e-liquids and devices.
  • E. VŠE
    VŠE is the commonly used abbreviation for the University of Economics in Prague, a leading Czech institution specializing in economics and business studies.
  • 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_69bd4465e03081909bfcfd7113062590 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7abd3ed48190bfd8d2f2ca399741 completed March 20, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69beeff852bc81908467a343c5ded404 completed March 21, 2026, 7:22 p.m.
NEDg Description generation batch_69bef0761c208190bac06ff1f92c8224 completed March 21, 2026, 7:24 p.m.
NED2 Entity disambiguation (via description) batch_69bef14ca27c81909a5a44155c9ddaf9 completed March 21, 2026, 7:28 p.m.
Created at: March 20, 2026, 1:48 p.m.