Triple

T207928
Position Surface form Disambiguated ID Type / Status
Subject Miryam E4648 entity
Predicate hasVariant P455 FINISHED
Object Marie
Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
 E27948 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: Marie | Statement: [Miryam, hasVariant, Marie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marie
Context triple: [Miryam, hasVariant, Marie]
  • A. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • B. Estelle
    Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
  • C. Marguerite De La Motte
    Marguerite De La Motte was an American silent film actress best known for her leading roles in early 1920s adventure and drama films.
  • D. Pierrette
    Pierrette is a French feminine given name, traditionally considered the female form of Pierre.
  • 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: Marie
Triple: [Miryam, hasVariant, Marie]
Generated description
Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marie
Target entity description: Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • A. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • B. Estelle
    Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
  • C. Marguerite De La Motte
    Marguerite De La Motte was an American silent film actress best known for her leading roles in early 1920s adventure and drama films.
  • D. Pierrette
    Pierrette is a French feminine given name, traditionally considered the female form of Pierre.
  • 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_69a25737567c81908f9c505300239181 completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c071fac81908f706d1384281182 completed Feb. 28, 2026, 3:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69a34764cb5c8190b9095a38866387d9 completed Feb. 28, 2026, 7:52 p.m.
NEDg Description generation batch_69a348107f8c81908102ecab4fafbffe completed Feb. 28, 2026, 7:54 p.m.
NED2 Entity disambiguation (via description) batch_69a3485cf79881909e9576240f7408a5 completed Feb. 28, 2026, 7:56 p.m.
Created at: Feb. 28, 2026, 2:51 a.m.