Triple

T1624723
Position Surface form Disambiguated ID Type / Status
Subject District of Leipzig E35114 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object L
L is the vehicle registration code used on license plates for the German city and district of Leipzig.
 E185209 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: L | Statement: [District of Leipzig, vehicleRegistrationCode, L]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: L
Context triple: [District of Leipzig, vehicleRegistrationCode, L]
  • A. L
    L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
  • B. L
    The L is a Chicago 'L' rapid transit line that serves the city’s West Side and western suburbs as part of the Chicago Transit Authority system.
  • C. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • D. LV
    LV is the two-letter ISO 3166-1 alpha-2 country code representing Latvia.
  • E. LO
    LO was the New York Stock Exchange ticker symbol for Lorillard Tobacco Company, a major American tobacco manufacturer best known for brands like Newport.
  • 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: L
Triple: [District of Leipzig, vehicleRegistrationCode, L]
Generated description
L is the vehicle registration code used on license plates for the German city and district of Leipzig.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: L
Target entity description: L is the vehicle registration code used on license plates for the German city and district of Leipzig.
  • A. L
    L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
  • B. L
    The L is a Chicago 'L' rapid transit line that serves the city’s West Side and western suburbs as part of the Chicago Transit Authority system.
  • C. Le
    Le is a common Vietnamese surname shared by many notable figures in the country’s history and culture.
  • D. LV
    LV is the two-letter ISO 3166-1 alpha-2 country code representing Latvia.
  • E. LO
    LO was the New York Stock Exchange ticker symbol for Lorillard Tobacco Company, a major American tobacco manufacturer best known for brands like Newport.
  • 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_69a886023194819080a3fccd6e325d0e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909d0586c81909e399b636e130ff5 completed March 5, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad58ccc80c819088ecd91f0a99a247 completed March 8, 2026, 11:09 a.m.
NEDg Description generation batch_69ad5a619da481908d66837ea94c91cf completed March 8, 2026, 11:15 a.m.
NED2 Entity disambiguation (via description) batch_69ad5b41a68c8190ba293d8e8c35521b completed March 8, 2026, 11:19 a.m.
Created at: March 4, 2026, 7:28 p.m.