Triple

T10531896
Position Surface form Disambiguated ID Type / Status
Subject Lolita E248462 entity
Predicate alsoKnownAs P39 FINISHED
Object Lo
Lo is a nickname for Lolita, most famously associated with the provocative young heroine of Vladimir Nabokov’s novel "Lolita."
 E869746 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: [Lolita, alsoKnownAs, Lo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lo
Context triple: [Lolita, alsoKnownAs, Lo]
  • A. Lo
    Lo is a dialect of the Lo-Toga language spoken on the Torres Islands in northern Vanuatu.
  • B. LO
    LO is Norway’s largest and most influential trade union confederation, representing a broad spectrum of workers across multiple sectors.
  • C. 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.
  • D. LO
    LO is the vehicle registration code used on license plates for vehicles registered in the Province of Lodi in Italy.
  • E. LO
    LO is the regional vehicle registration code assigned to the city of Vanadzor in Armenia.
  • 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: [Lolita, alsoKnownAs, Lo]
Generated description
Lo is a nickname for Lolita, most famously associated with the provocative young heroine of Vladimir Nabokov’s novel "Lolita."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lo
Target entity description: Lo is a nickname for Lolita, most famously associated with the provocative young heroine of Vladimir Nabokov’s novel "Lolita."
  • A. Lo
    Lo is a dialect of the Lo-Toga language spoken on the Torres Islands in northern Vanuatu.
  • B. LO
    LO is Norway’s largest and most influential trade union confederation, representing a broad spectrum of workers across multiple sectors.
  • C. 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.
  • D. LO
    LO is the regional vehicle registration code assigned to the city of Vanadzor in Armenia.
  • E. LO
    LO is the vehicle registration code used on license plates for vehicles registered in the Province of Lodi in Italy.
  • 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_69d381c5c7448190bec34bee7ec72bac completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d50a17f23081909f3372e160e21670 completed April 7, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d90e471e9c8190b134249073b289bd completed April 10, 2026, 2:50 p.m.
NEDg Description generation batch_69d9107f488481908845aef0fdf6d60d completed April 10, 2026, 3 p.m.
NED2 Entity disambiguation (via description) batch_69d911790010819093fc50952502fd59 completed April 10, 2026, 3:04 p.m.
Created at: April 6, 2026, 12:30 p.m.