Triple

T11987674
Position Surface form Disambiguated ID Type / Status
Subject Albstadt E285322 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object BL
BL is the vehicle registration code used on license plates for vehicles registered in the German town of Albstadt.
 E958431 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: BL | Statement: [Albstadt, vehicleRegistrationCode, BL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BL
Context triple: [Albstadt, vehicleRegistrationCode, BL]
  • A. BL
    BL is the postcode area in the United Kingdom that covers Bolton and surrounding parts of Greater Manchester and Lancashire.
  • B. BL
    BL is the two-letter ISO 3166-1 alpha-2 country code assigned to the Caribbean island territory of Saint Barthélemy.
  • C. BL
    BL is the vehicle registration code used on license plates for vehicles registered in the Bratislava Region of Slovakia.
  • D. BL
    BL is the vehicle registration code used for the city of Banja Luka in Bosnia and Herzegovina.
  • E. BL
    BL is the commonly used abbreviation for British Leyland, a major former UK vehicle manufacturer known for producing a wide range of cars, trucks, and buses in the 20th century.
  • 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: BL
Triple: [Albstadt, vehicleRegistrationCode, BL]
Generated description
BL is the vehicle registration code used on license plates for vehicles registered in the German town of Albstadt.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BL
Target entity description: BL is the vehicle registration code used on license plates for vehicles registered in the German town of Albstadt.
  • A. BL
    BL is the vehicle registration code used on license plates for the Swiss canton of Basel-Landschaft.
  • B. BL
    BL is the vehicle registration code used on license plates for vehicles registered in the Bratislava Region of Slovakia.
  • C. BL
    BL is the vehicle registration code used for the city of Banja Luka in Bosnia and Herzegovina.
  • D. BL
    BL is the commonly used abbreviation for British Leyland, a major former UK vehicle manufacturer known for producing a wide range of cars, trucks, and buses in the 20th century.
  • E. BL
    BL is the postcode area in the United Kingdom that covers Bolton and surrounding parts of Greater Manchester and Lancashire.
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903ae28708190a826bad1624343eb completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f472492ebc8190b064e691bb70e356 completed May 1, 2026, 9:28 a.m.
NEDg Description generation batch_69f47b7c5af08190ab0bff1232530a0c completed May 1, 2026, 10:07 a.m.
NED2 Entity disambiguation (via description) batch_69f47dd51e648190bddd41766221e22d completed May 1, 2026, 10:17 a.m.
Created at: April 8, 2026, 9:46 p.m.