Triple

T5724741
Position Surface form Disambiguated ID Type / Status
Subject Swiss Federal Railways network E126235 entity
Predicate safetySystem P840 FINISHED
Object ZUB
ZUB is a Swiss train protection and automatic train control system used to enhance operational safety on the Swiss Federal Railways network.
 E540420 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: ZUB | Statement: [Swiss Federal Railways network, safetySystem, ZUB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ZUB
Context triple: [Swiss Federal Railways network, safetySystem, ZUB]
  • A. ZUT
    ZUT is the three-letter IATA airport code assigned to Teuge Airport in the Netherlands.
  • B. ZUE
    ZUE is the railway station code for Zürich Hauptbahnhof, Switzerland’s largest and busiest train station and a major European rail hub.
  • C. ZMB
    ZMB is the three-letter ISO 3166-1 alpha-3 country code assigned to Zambia.
  • D. ZS
    ZS is the vehicle registration code assigned to cars registered in the Polish city of Szczecin.
  • E. ZAG
    ZAG is the commonly used abbreviation for Zagłębie Lubin, a professional football club based in Lubin, Poland.
  • 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: ZUB
Triple: [Swiss Federal Railways network, safetySystem, ZUB]
Generated description
ZUB is a Swiss train protection and automatic train control system used to enhance operational safety on the Swiss Federal Railways network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ZUB
Target entity description: ZUB is a Swiss train protection and automatic train control system used to enhance operational safety on the Swiss Federal Railways network.
  • A. ZUT
    ZUT is the three-letter IATA airport code assigned to Teuge Airport in the Netherlands.
  • B. ZUE
    ZUE is the railway station code for Zürich Hauptbahnhof, Switzerland’s largest and busiest train station and a major European rail hub.
  • C. ZMB
    ZMB is the three-letter ISO 3166-1 alpha-3 country code assigned to Zambia.
  • D. ZS
    ZS is the vehicle registration code assigned to cars registered in the Polish city of Szczecin.
  • E. ZAG
    ZAG is the commonly used abbreviation for Zagłębie Lubin, a professional football club based in Lubin, Poland.
  • 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_69c0082f723881908ce8bb13a0c0f8b7 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c025085f508190adf5d540bc8a5b1c completed March 22, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a83c88c819097abe565ba010a29 completed March 22, 2026, 9:09 p.m.
NEDg Description generation batch_69c05b7c3bd48190ad8303bf1bb3ec6a completed March 22, 2026, 9:13 p.m.
NED2 Entity disambiguation (via description) batch_69c05c22c31081909a9a67d99e7c728c completed March 22, 2026, 9:16 p.m.
Created at: March 22, 2026, 3:47 p.m.