Triple

T1695639
Position Surface form Disambiguated ID Type / Status
Subject District of Altenburger Land E36650 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object ABG
ABG is the vehicle registration code used on license plates for vehicles registered in the Altenburger Land district in Germany.
 E191603 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: ABG | Statement: [District of Altenburger Land, vehicleRegistrationCode, ABG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ABG
Context triple: [District of Altenburger Land, vehicleRegistrationCode, ABG]
  • A. ABJ
    ABJ is the vehicle registration code used for motor vehicles registered in Abuja, the capital city of Nigeria.
  • B. AG
    AG is the standard abbreviation for the United States Attorney General, the chief law enforcement officer and head of the U.S. Department of Justice.
  • C. AG
    AG is the two-letter ISO 3166-1 alpha-2 country code assigned to Antigua and Barbuda.
  • D. GAB
    GAB is the three-letter ISO 3166-1 alpha-3 country code assigned to Gabon.
  • E. ABL
    ABL is the commonly used abbreviation for the Academia Brasileira de Letras, Brazil’s foremost literary and language academy.
  • 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: ABG
Triple: [District of Altenburger Land, vehicleRegistrationCode, ABG]
Generated description
ABG is the vehicle registration code used on license plates for vehicles registered in the Altenburger Land district in Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ABG
Target entity description: ABG is the vehicle registration code used on license plates for vehicles registered in the Altenburger Land district in Germany.
  • A. ABJ
    ABJ is the vehicle registration code used for motor vehicles registered in Abuja, the capital city of Nigeria.
  • B. AG
    AG is the standard abbreviation for the United States Attorney General, the chief law enforcement officer and head of the U.S. Department of Justice.
  • C. AG
    AG is the two-letter ISO 3166-1 alpha-2 country code assigned to Antigua and Barbuda.
  • D. GAB
    GAB is the three-letter ISO 3166-1 alpha-3 country code assigned to Gabon.
  • E. ABL
    ABL is the commonly used abbreviation for the Academia Brasileira de Letras, Brazil’s foremost literary and language academy.
  • 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_69a886163dec8190859c514232a37a05 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa62b645a081909dafdf7a32f2a389 completed March 6, 2026, 5:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad7998e1108190aa7430cd4ef887d9 completed March 8, 2026, 1:28 p.m.
NEDg Description generation batch_69ad7a224d248190b0d1a7f70b76c164 completed March 8, 2026, 1:31 p.m.
NED2 Entity disambiguation (via description) batch_69ad7b4b4a208190966fa07a6f0d626e completed March 8, 2026, 1:36 p.m.
Created at: March 4, 2026, 7:30 p.m.