Triple

T2426579
Position Surface form Disambiguated ID Type / Status
Subject Leonard E53541 entity
Predicate hasCognate P2525 FINISHED
Object Lennart
Lennart is a masculine given name, primarily used in Germanic and Scandinavian countries, that is a cognate of the name Leonard.
 E266135 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: Lennart | Statement: [Leonard, hasCognate, Lennart]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lennart
Context triple: [Leonard, hasCognate, Lennart]
  • A. Lennart Johansson
    Lennart Johansson was a Swedish football administrator best known for serving as UEFA president from 1990 to 2007 and overseeing the creation of the UEFA Champions League.
  • B. Svante
    Svante is the given name of Swedish geneticist Svante Pääbo, a Nobel Prize–winning pioneer in the field of paleogenomics.
  • C. Gustav
    Gustav is a masculine given name of German origin, borne by several notable historical figures including scientists, artists, and royalty.
  • D. Pehr
    Pehr is a masculine given name of Scandinavian origin, notably borne by Finnish statesman P. E. Svinhufvud.
  • E. Sven
    Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
  • 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: Lennart
Triple: [Leonard, hasCognate, Lennart]
Generated description
Lennart is a masculine given name, primarily used in Germanic and Scandinavian countries, that is a cognate of the name Leonard.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lennart
Target entity description: Lennart is a masculine given name, primarily used in Germanic and Scandinavian countries, that is a cognate of the name Leonard.
  • A. Lennart Johansson
    Lennart Johansson was a Swedish football administrator best known for serving as UEFA president from 1990 to 2007 and overseeing the creation of the UEFA Champions League.
  • B. Svante
    Svante is the given name of Swedish geneticist Svante Pääbo, a Nobel Prize–winning pioneer in the field of paleogenomics.
  • C. Gustav
    Gustav is a masculine given name of German origin, borne by several notable historical figures including scientists, artists, and royalty.
  • D. Pehr
    Pehr is a masculine given name of Scandinavian origin, notably borne by Finnish statesman P. E. Svinhufvud.
  • E. Sven
    Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
  • 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_69ab495c44d48190b7235b23719bc3f6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc99b95548190b77d36de9adfe3bb completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf637be4819096874a24e87f84ab completed March 9, 2026, 12:38 p.m.
NEDg Description generation batch_69aec7698e9881909c75137cb5cf2a0c completed March 9, 2026, 1:13 p.m.
NED2 Entity disambiguation (via description) batch_69aec87df0648190b8099b4eaa3d9b22 completed March 9, 2026, 1:17 p.m.
Created at: March 6, 2026, 9:42 p.m.