Triple

T8147866
Position Surface form Disambiguated ID Type / Status
Subject Erzgebirgskreis E190258 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object AU
AU is a German vehicle registration code used on license plates to identify cars registered in the Erzgebirgskreis district of Saxony.
 E717339 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: AU | Statement: [Erzgebirgskreis, hasVehicleRegistrationCode, AU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AU
Context triple: [Erzgebirgskreis, hasVehicleRegistrationCode, AU]
  • A. AU
    Aarhus University (AU) is a major public research university in Aarhus, Denmark, known for its broad range of academic programs and strong international profile.
  • B. AU
    AU is the commonly used abbreviation for Anna University, a prominent public technical university based in Chennai, India.
  • C. AU
    AU is the commonly used abbreviation for the African Union, a continental organization that promotes political and economic cooperation among African states.
  • D. AUS
    AUS is the three-letter IATA airport code for Austin–Bergstrom International Airport, the primary commercial airport serving Austin, Texas.
  • E. Aust
    Aust is a small village in South Gloucestershire, England, situated near the Severn Estuary and known historically for its ferry crossing and proximity to the Severn Bridge.
  • 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: AU
Triple: [Erzgebirgskreis, hasVehicleRegistrationCode, AU]
Generated description
AU is a German vehicle registration code used on license plates to identify cars registered in the Erzgebirgskreis district of Saxony.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: AU
Target entity description: AU is a German vehicle registration code used on license plates to identify cars registered in the Erzgebirgskreis district of Saxony.
  • A. AU
    AU is the commonly used abbreviation for the African Union, a continental organization that promotes political and economic cooperation among African states.
  • B. AU
    Aarhus University (AU) is a major public research university in Aarhus, Denmark, known for its broad range of academic programs and strong international profile.
  • C. AU
    AU is the commonly used abbreviation for Anna University, a prominent public technical university based in Chennai, India.
  • D. AUS
    AUS is the three-letter IATA airport code for Austin–Bergstrom International Airport, the primary commercial airport serving Austin, Texas.
  • E. Aust
    Aust is a small village in South Gloucestershire, England, situated near the Severn Estuary and known historically for its ferry crossing and proximity to the Severn Bridge.
  • 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_69ca82be7ba8819087de0147e9292c83 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb447e74e081908df774edb2134209 completed March 31, 2026, 3:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbee697208190a1d9c98b2a4414bd completed April 1, 2026, 6:44 a.m.
NEDg Description generation batch_69ccc30f1fc48190991e0caa9ea6e735 completed April 1, 2026, 7:02 a.m.
NED2 Entity disambiguation (via description) batch_69ccd8041c00819094094701ace21aa0 completed April 1, 2026, 8:32 a.m.
Created at: March 30, 2026, 5:36 p.m.