Triple

T8772599
Position Surface form Disambiguated ID Type / Status
Subject Province of Lodi E208498 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object LO
LO is the vehicle registration code used on license plates for vehicles registered in the Province of Lodi in Italy.
 E757646 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: [Province of Lodi, vehicleRegistrationCode, LO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LO
Context triple: [Province of Lodi, 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. LO
    LO is the regional vehicle registration code assigned to the city of Vanadzor in Armenia.
  • D. Lo
    Lo is a dialect of the Lo-Toga language spoken on the Torres Islands in northern Vanuatu.
  • E. LOS
    LOS is the IATA airport code for Murtala Muhammed International Airport, the main international gateway serving Lagos, Nigeria.
  • 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: [Province of Lodi, vehicleRegistrationCode, LO]
Generated description
LO is the vehicle registration code used on license plates for vehicles registered in the Province of Lodi in Italy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LO
Target entity description: LO is the vehicle registration code used on license plates for vehicles registered in the Province of Lodi in Italy.
  • 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. LO
    LO is the regional vehicle registration code assigned to the city of Vanadzor in Armenia.
  • D. Lo
    Lo is a dialect of the Lo-Toga language spoken on the Torres Islands in northern Vanuatu.
  • E. LOS
    LOS is the IATA airport code for Murtala Muhammed International Airport, the main international gateway serving Lagos, Nigeria.
  • 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_69ca835edb4481909b4aafb616dc5eb7 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f2dc87c8190be0597a260d95a0b completed March 31, 2026, 11:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf51c0125c819098deb7a54688b125 completed April 3, 2026, 5:36 a.m.
NEDg Description generation batch_69cf5378c3f48190a4180c20aecb2260 completed April 3, 2026, 5:43 a.m.
NED2 Entity disambiguation (via description) batch_69cf5471e9c08190963b7f1d6c2ceffe completed April 3, 2026, 5:47 a.m.
Created at: March 30, 2026, 6:41 p.m.