Triple

T12420581
Position Surface form Disambiguated ID Type / Status
Subject Leverkusen E296756 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object LEV
LEV is the vehicle registration code used on license plates for the German city of Leverkusen.
 E980418 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: LEV | Statement: [Leverkusen, vehicleRegistrationCode, LEV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LEV
Context triple: [Leverkusen, vehicleRegistrationCode, LEV]
  • A. lev
    The lev is the official national currency of Bulgaria, used for everyday transactions and financial operations throughout the country.
  • B. LV
    LV is the two-letter ISO 3166-1 alpha-2 country code representing Latvia.
  • C. LE
    LE is the abbreviation for Les Engagés, a centrist political party in Belgium that emerged from the reform of the former Humanist Democratic Centre (cdH).
  • D. LEM
    LEM is the original abbreviation for the Apollo Lunar Module, the spacecraft used by NASA astronauts to land on and ascend from the Moon during the Apollo missions.
  • E. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • 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: LEV
Triple: [Leverkusen, vehicleRegistrationCode, LEV]
Generated description
LEV is the vehicle registration code used on license plates for the German city of Leverkusen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LEV
Target entity description: LEV is the vehicle registration code used on license plates for the German city of Leverkusen.
  • A. lev
    The lev is the official national currency of Bulgaria, used for everyday transactions and financial operations throughout the country.
  • B. LV
    LV is the two-letter ISO 3166-1 alpha-2 country code representing Latvia.
  • C. LE
    LE is the abbreviation for Les Engagés, a centrist political party in Belgium that emerged from the reform of the former Humanist Democratic Centre (cdH).
  • D. LEM
    LEM is the original abbreviation for the Apollo Lunar Module, the spacecraft used by NASA astronauts to land on and ascend from the Moon during the Apollo missions.
  • E. LER
    LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • 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_69d6ada0640c81908c061d7fb3d47786 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d6efd748190a5d9396a343e41e1 completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f634933b9881909fd592ede7c3e49c completed May 2, 2026, 5:29 p.m.
NEDg Description generation batch_69f6356c21908190b34d1324da8f8052 completed May 2, 2026, 5:33 p.m.
NED2 Entity disambiguation (via description) batch_69f63693f5c881909a9683a0c6a68739 completed May 2, 2026, 5:38 p.m.
Created at: April 8, 2026, 9:55 p.m.