Triple

T9364331
Position Surface form Disambiguated ID Type / Status
Subject district of Dachau E225361 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object DAH
DAH is the vehicle registration code used for motor vehicles registered in the rural district of Dachau in Bavaria, Germany.
 E794717 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: DAH | Statement: [district of Dachau, hasVehicleRegistrationCode, DAH]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DAH
Context triple: [district of Dachau, hasVehicleRegistrationCode, DAH]
  • A. DAR
    DAR is the Philippine government agency responsible for implementing agrarian reform and redistributing agricultural land to farmers.
  • B. DA
    DA is a postcode area in southeast England covering parts of south-east London and northwest Kent, including towns such as Dartford and Sidcup.
  • C. DA
    DA is the vehicle registration code for the German city of Darmstadt and its surrounding district in the state of Hesse.
  • D. DA
    DA is the commonly used abbreviation for the Defence Academy of the United Kingdom, the institution responsible for advanced education and training of the UK’s armed forces and defence personnel.
  • E. DA
    DA is the official abbreviation for the United States Department of the Army, the federal agency responsible for organizing, training, and equipping the U.S. Army.
  • 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: DAH
Triple: [district of Dachau, hasVehicleRegistrationCode, DAH]
Generated description
DAH is the vehicle registration code used for motor vehicles registered in the rural district of Dachau in Bavaria, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DAH
Target entity description: DAH is the vehicle registration code used for motor vehicles registered in the rural district of Dachau in Bavaria, Germany.
  • A. DAR
    DAR is the Philippine government agency responsible for implementing agrarian reform and redistributing agricultural land to farmers.
  • B. DA
    DA is the commonly used abbreviation for the Defence Academy of the United Kingdom, the institution responsible for advanced education and training of the UK’s armed forces and defence personnel.
  • C. DA
    DA is a postcode area in southeast England covering parts of south-east London and northwest Kent, including towns such as Dartford and Sidcup.
  • D. DA
    DA is the vehicle registration code for the German city of Darmstadt and its surrounding district in the state of Hesse.
  • E. DA
    DA is the official abbreviation for the United States Department of the Army, the federal agency responsible for organizing, training, and equipping the U.S. Army.
  • 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_69ca842bdd648190904131d58620d448 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd503fd7f081909655e2a880c84834 completed April 1, 2026, 5:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0f3f3c420819084b65fd4537aaf93 completed April 4, 2026, 11:20 a.m.
NEDg Description generation batch_69d0f619e91081909c2ec17e89376295 completed April 4, 2026, 11:29 a.m.
NED2 Entity disambiguation (via description) batch_69d0f6ce1a0c8190aca34958935e0e59 completed April 4, 2026, 11:32 a.m.
Created at: March 30, 2026, 7:42 p.m.