Triple

T2799386
Position Surface form Disambiguated ID Type / Status
Subject Vanadzor E53114 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object LO
LO is the regional vehicle registration code assigned to the city of Vanadzor in Armenia.
 E299891 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: LO | Statement: [Vanadzor, vehicleRegistrationCode, LO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LO
Context triple: [Vanadzor, vehicleRegistrationCode, LO]
  • A. LO
    LO is Norway’s largest and most influential trade union confederation, representing a broad spectrum of workers across multiple sectors.
  • B. 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.
  • C. LOS
    LOS is the IATA airport code for Murtala Muhammed International Airport, the main international gateway serving Lagos, Nigeria.
  • D. L
    L is the vehicle registration code used on license plates for the German city and district of Leipzig.
  • E. 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.
  • 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: LO
Triple: [Vanadzor, vehicleRegistrationCode, LO]
Generated description
LO is the regional vehicle registration code assigned to the city of Vanadzor in Armenia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LO
Target entity description: LO is the regional vehicle registration code assigned to the city of Vanadzor in Armenia.
  • A. LO
    LO is Norway’s largest and most influential trade union confederation, representing a broad spectrum of workers across multiple sectors.
  • B. 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.
  • C. LOS
    LOS is the IATA airport code for Murtala Muhammed International Airport, the main international gateway serving Lagos, Nigeria.
  • D. L
    L is the vehicle registration code used on license plates for the German city and district of Leipzig.
  • E. 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.
  • 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_69ab495a90788190941b6917e1eca3a6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abddf31eec8190a898713e53d1aa5c completed March 7, 2026, 8:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc66ad9cc81908af6d3816a840c75 completed March 10, 2026, 7:21 a.m.
NEDg Description generation batch_69afc80ba2148190ae0c1f6425d52dfd completed March 10, 2026, 7:28 a.m.
NED2 Entity disambiguation (via description) batch_69afc97fc45481908aa72ac8607c34ac completed March 10, 2026, 7:34 a.m.
Created at: March 6, 2026, 9:58 p.m.