Triple

T21586261
Position Surface form Disambiguated ID Type / Status
Subject Verden an der Aller E532658 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object VER
VER is the vehicle registration code used for cars registered in the district of Verden in Lower Saxony, Germany.
E1491988 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: VER | Statement: [Verden an der Aller, vehicleRegistrationCode, VER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VER
Context triple: [Verden an der Aller, vehicleRegistrationCode, VER]
  • A. VER
    VER is the IATA airport code for General Heriberto Jara International Airport serving the city of Veracruz, Mexico.
  • B. VE
    VE is the Italian vehicle registration code assigned to the Metropolitan City of Venice.
  • C. VE
    VE is the two-letter ISO 3166-1 alpha-2 country code assigned to Venezuela for international standardization and identification purposes.
  • D. VE
    VE is the vehicle registration code used on license plates for vehicles registered in Ajka, Hungary.
  • E. VEN
    VEN is the three-letter ISO 3166-1 alpha-3 country code assigned to Venezuela for international identification and data standards.
  • 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: VER
Triple: [Verden an der Aller, vehicleRegistrationCode, VER]
Generated description
VER is the vehicle registration code used for cars registered in the district of Verden in Lower Saxony, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: VER
Target entity description: VER is the vehicle registration code used for cars registered in the district of Verden in Lower Saxony, Germany.
  • A. VER
    VER is the IATA airport code for General Heriberto Jara International Airport serving the city of Veracruz, Mexico.
  • B. VE
    VE is the Italian vehicle registration code assigned to the Metropolitan City of Venice.
  • C. VE
    VE is the two-letter ISO 3166-1 alpha-2 country code assigned to Venezuela for international standardization and identification purposes.
  • D. VE
    VE is the vehicle registration code used on license plates for vehicles registered in Ajka, Hungary.
  • E. VEN
    VEN is the three-letter ISO 3166-1 alpha-3 country code assigned to Venezuela for international identification and data standards.
  • 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_69e0c46251648190876f0427cf2d321b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eeeb6137fc8190840b7c1275e62a1d completed April 27, 2026, 4:51 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09f67cc2288190886c4124c97edb67 completed May 17, 2026, 5:10 p.m.
NEDg Description generation batch_6a09f9614ab88190806a3ed38b7ba537 completed May 17, 2026, 5:22 p.m.
NED2 Entity disambiguation (via description) batch_6a09fa3c92a8819082e454d907971b21 completed May 17, 2026, 5:26 p.m.
Created at: April 16, 2026, 6:31 p.m.