Triple

T9498564
Position Surface form Disambiguated ID Type / Status
Subject Weiden in der Oberpfalz E229075 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object WEN
WEN is the vehicle registration code for the German city of Weiden in der Oberpfalz in Bavaria.
 E802085 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: WEN | Statement: [Weiden in der Oberpfalz, vehicleRegistrationCode, WEN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WEN
Context triple: [Weiden in der Oberpfalz, vehicleRegistrationCode, WEN]
  • A. WHE
    WHE is the National Rail station code for Whalley railway station in Lancashire, England.
  • B. WUN
    WUN is a global consortium of research-intensive universities that collaborate on international education and research initiatives.
  • C. WUN
    WUN is the vehicle registration code for the district of Wunsiedel im Fichtelgebirge in Upper Franconia, Germany.
  • D. WUN
    WUN is the commonly used abbreviation for Western United FC, a professional soccer club based in Victoria, Australia that competes in the A-League Men.
  • E. WNE
    WNE is the station code for West Newton railway station in West Yorkshire, England.
  • 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: WEN
Triple: [Weiden in der Oberpfalz, vehicleRegistrationCode, WEN]
Generated description
WEN is the vehicle registration code for the German city of Weiden in der Oberpfalz in Bavaria.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WEN
Target entity description: WEN is the vehicle registration code for the German city of Weiden in der Oberpfalz in Bavaria.
  • A. WHE
    WHE is the National Rail station code for Whalley railway station in Lancashire, England.
  • B. WUN
    WUN is the vehicle registration code for the district of Wunsiedel im Fichtelgebirge in Upper Franconia, Germany.
  • C. WUN
    WUN is a global consortium of research-intensive universities that collaborate on international education and research initiatives.
  • D. WUN
    WUN is the commonly used abbreviation for Western United FC, a professional soccer club based in Victoria, Australia that competes in the A-League Men.
  • E. WNE
    WNE is the station code for West Newton railway station in West Yorkshire, England.
  • 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_69ca84753660819098e8d416e89e26ae completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd95ef06b88190b7a840caddea3e38 completed April 1, 2026, 10:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69d12d3aafb88190ac53289039bca88a completed April 4, 2026, 3:24 p.m.
NEDg Description generation batch_69d12dcae2088190bdb4ebac9021e622 completed April 4, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_69d12e4077a4819094e86eb0de69b2ed completed April 4, 2026, 3:29 p.m.
Created at: March 30, 2026, 7:56 p.m.