Triple

T6908632
Position Surface form Disambiguated ID Type / Status
Subject Borkum E159874 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object LER
LER is the vehicle registration code assigned to the German island municipality of Borkum.
 E627972 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: LER | Statement: [Borkum, vehicleRegistrationCode, LER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LER
Context triple: [Borkum, vehicleRegistrationCode, LER]
  • A. LR
    LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • B. LR
    LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • C. 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.
  • D. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • E. Lu
    Lu is the traditional abbreviation and historical name used to refer to China’s Shandong province.
  • 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: LER
Triple: [Borkum, vehicleRegistrationCode, LER]
Generated description
LER is the vehicle registration code assigned to the German island municipality of Borkum.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LER
Target entity description: LER is the vehicle registration code assigned to the German island municipality of Borkum.
  • A. LR
    LR is a German vehicle registration code assigned to the Ortenaukreis district in the state of Baden-Württemberg.
  • B. LR
    LR is the stock ticker symbol for Legrand, a global specialist in electrical and digital building infrastructure.
  • C. 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.
  • D. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • E. Lu
    Lu is the traditional abbreviation and historical name used to refer to China’s Shandong province.
  • 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_69c68839ccb88190b4aa5cc1aca3448f completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6d9be98748190b5cb698e66e3aa42 completed March 27, 2026, 7:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69c749076f6c819088b0b40dd3e208b0 completed March 28, 2026, 3:20 a.m.
NEDg Description generation batch_69c74c274258819099913ac5610730ac completed March 28, 2026, 3:33 a.m.
NED2 Entity disambiguation (via description) batch_69c74cca47b88190867550802db43ef0 completed March 28, 2026, 3:36 a.m.
Created at: March 27, 2026, 2:25 p.m.