Triple

T2328017
Position Surface form Disambiguated ID Type / Status
Subject Naomi E48334 entity
Predicate hasVariant P455 FINISHED
Object Noémie
Noémie is a French given name, equivalent to Naomi, commonly used for girls in Francophone countries.
 E262024 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: Noémie | Statement: [Naomi, hasVariant, Noémie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Noémie
Context triple: [Naomi, hasVariant, Noémie]
  • A. Laetitia
    Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
  • B. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • C. Emilie
    Emilie is a young French girl in Michael Morpurgo’s novel and its film adaptation "War Horse," who befriends and cares for the horses Joey and Topthorn during World War I.
  • D. Jeanne
    Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
  • E. Clémentine
    Clémentine is a feminine given name of French origin, commonly used in Francophone countries and beyond.
  • 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: Noémie
Triple: [Naomi, hasVariant, Noémie]
Generated description
Noémie is a French given name, equivalent to Naomi, commonly used for girls in Francophone countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Noémie
Target entity description: Noémie is a French given name, equivalent to Naomi, commonly used for girls in Francophone countries.
  • A. Laetitia
    Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
  • B. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • C. Emilie
    Emilie is a young French girl in Michael Morpurgo’s novel and its film adaptation "War Horse," who befriends and cares for the horses Joey and Topthorn during World War I.
  • D. Jeanne
    Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
  • E. Clémentine
    Clémentine is a feminine given name of French origin, commonly used in Francophone countries and beyond.
  • 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_69a88aa308a88190b0b86c011fda7fce completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc64c7f1881909b0d847f7782e803 completed March 7, 2026, 6:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3be86dc8190af185bac9554e7d6 completed March 9, 2026, 11:49 a.m.
NEDg Description generation batch_69aeb46f882881909294a3698ead865e completed March 9, 2026, 11:52 a.m.
NED2 Entity disambiguation (via description) batch_69aeb4c715a88190b1009a2cf1d95441 completed March 9, 2026, 11:53 a.m.
Created at: March 4, 2026, 7:50 p.m.