Triple

T21633424
Position Surface form Disambiguated ID Type / Status
Subject Eudoxie Mbouguiengue E533891 entity
Predicate givenName P17 FINISHED
Object Eudoxie
Eudoxie is a Gabonese-born model and philanthropist best known as the wife of American rapper and actor Ludacris.
 E1494301 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: Eudoxie | Statement: [Eudoxie Mbouguiengue, givenName, Eudoxie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Eudoxie
Context triple: [Eudoxie Mbouguiengue, givenName, Eudoxie]
  • A. Eudoxia
    Eudoxia was a Bulgarian princess of the early 20th century, known as the daughter of Tsar Ferdinand I and a member of the last ruling dynasty of the Kingdom of Bulgaria.
  • B. Katerina
    Katerina is a novel by Israeli writer Aharon Appelfeld that explores themes of memory, identity, and Jewish–Gentile relations in pre- and post-Holocaust Eastern Europe.
  • C. Anastasie
    Anastasie is the given name of Anastasie de Lafayette, a French noblewoman associated with the influential Lafayette family.
  • D. Tatyana
    Tatyana is a feminine given name of Slavic origin, particularly common in Russian-speaking countries.
  • E. Delphine
    Delphine is an epistolary novel by Madame de Staël that explores themes of love, social convention, and women's independence in late 18th-century French society.
  • 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: Eudoxie
Triple: [Eudoxie Mbouguiengue, givenName, Eudoxie]
Generated description
Eudoxie is a Gabonese-born model and philanthropist best known as the wife of American rapper and actor Ludacris.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Eudoxie
Target entity description: Eudoxie is a Gabonese-born model and philanthropist best known as the wife of American rapper and actor Ludacris.
  • A. Eudoxia
    Eudoxia was a Bulgarian princess of the early 20th century, known as the daughter of Tsar Ferdinand I and a member of the last ruling dynasty of the Kingdom of Bulgaria.
  • B. Katerina
    Katerina is a novel by Israeli writer Aharon Appelfeld that explores themes of memory, identity, and Jewish–Gentile relations in pre- and post-Holocaust Eastern Europe.
  • C. Anastasie
    Anastasie is the given name of Anastasie de Lafayette, a French noblewoman associated with the influential Lafayette family.
  • D. Tatyana
    Tatyana is a feminine given name of Slavic origin, particularly common in Russian-speaking countries.
  • E. Delphine
    Delphine is an epistolary novel by Madame de Staël that explores themes of love, social convention, and women's independence in late 18th-century French society.
  • 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_69e0c465ae7481908577b7209fdb2a77 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef52185dbc819096ad2fc5b7d953f8 completed April 27, 2026, 12:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a0f99a1b4819093788a4052d16b27 completed May 17, 2026, 6:57 p.m.
NEDg Description generation batch_6a0a113126808190ba002d405cc9a3d6 completed May 17, 2026, 7:04 p.m.
NED2 Entity disambiguation (via description) batch_6a0a119cb17c8190a8732930b78b60cc completed May 17, 2026, 7:06 p.m.
Created at: April 16, 2026, 6:35 p.m.