Triple

T2483835
Position Surface form Disambiguated ID Type / Status
Subject Nella Larsen E55879 entity
Predicate familyName P18 FINISHED
Object Larsen
Larsen is a surname of Scandinavian origin borne by numerous notable individuals across fields such as literature, music, and sports.
 E272819 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: Larsen | Statement: [Nella Larsen, familyName, Larsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Larsen
Context triple: [Nella Larsen, familyName, Larsen]
  • A. Nellallitea Larsen
    Nellallitea Larsen was an American novelist and key figure of the Harlem Renaissance, best known for her works exploring race, identity, and gender such as "Passing" and "Quicksand."
  • B. Hauke
    Hauke is a Germanic given name, particularly common in Northern Germany, that is cognate with the English name Hugh.
  • C. Lindeberg
    Lindeberg is a surname most notably associated with the Finnish mathematician Jarl Waldemar Lindeberg, known for his contributions to probability theory and the central limit theorem.
  • D. Nilsen
    Nilsen is a surname, primarily of Scandinavian origin, that serves as a variant spelling of Nelson.
  • E. Ahlström
    Ahlström is a Finnish industrial and design-oriented company known for its collaborations with prominent designers and its production of high-quality materials and products.
  • 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: Larsen
Triple: [Nella Larsen, familyName, Larsen]
Generated description
Larsen is a surname of Scandinavian origin borne by numerous notable individuals across fields such as literature, music, and sports.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Larsen
Target entity description: Larsen is a surname of Scandinavian origin borne by numerous notable individuals across fields such as literature, music, and sports.
  • A. Nellallitea Larsen
    Nellallitea Larsen was an American novelist and key figure of the Harlem Renaissance, best known for her works exploring race, identity, and gender such as "Passing" and "Quicksand."
  • B. Hauke
    Hauke is a Germanic given name, particularly common in Northern Germany, that is cognate with the English name Hugh.
  • C. Lindeberg
    Lindeberg is a surname most notably associated with the Finnish mathematician Jarl Waldemar Lindeberg, known for his contributions to probability theory and the central limit theorem.
  • D. Nilsen
    Nilsen is a surname, primarily of Scandinavian origin, that serves as a variant spelling of Nelson.
  • E. Ahlström
    Ahlström is a Finnish industrial and design-oriented company known for its collaborations with prominent designers and its production of high-quality materials and products.
  • 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_69ab49e670a88190b928e08302381710 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd1644b5881908d2931a1dfbbd03b completed March 7, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f8c02188190a7cf3e0531a66683 completed March 9, 2026, 7:29 p.m.
NEDg Description generation batch_69af20d8b8ec8190b0ec61d305fd362e completed March 9, 2026, 7:34 p.m.
NED2 Entity disambiguation (via description) batch_69af214cc4cc81908e0d1cc19726d60d completed March 9, 2026, 7:36 p.m.
Created at: March 6, 2026, 9:45 p.m.