Triple
T15046862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yvonne |
E379250
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Ivonne
Ivonne is a feminine given name, commonly considered a variant of Yvonne, used in various Spanish- and German-speaking countries.
|
E1136131
|
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: Ivonne | Statement: [Yvonne, hasVariant, Ivonne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ivonne Context triple: [Yvonne, hasVariant, Ivonne]
-
A.
Fabiola
Fabiola is a given name of Latin origin, historically associated with saints and European royalty.
-
B.
Yovanna
Yovanna is a fictional character played by actress Adria Arjona, known from her work in film and television.
-
C.
Marlen
Marlen is a village district of the town of Kehl in the German state of Baden-Württemberg.
-
D.
Marcela
Marcela is one of the given names of Alexia Juliana Marcela Laurentien, a member of the Dutch royal family.
-
E.
Rosa Elena
Rosa Elena is a Mexican public figure best known as the wife of former president Felipe Calderón and for her involvement in high-profile political and legal controversies.
- 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: Ivonne Triple: [Yvonne, hasVariant, Ivonne]
Generated description
Ivonne is a feminine given name, commonly considered a variant of Yvonne, used in various Spanish- and German-speaking countries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ivonne Target entity description: Ivonne is a feminine given name, commonly considered a variant of Yvonne, used in various Spanish- and German-speaking countries.
-
A.
Fabiola
Fabiola is a given name of Latin origin, historically associated with saints and European royalty.
-
B.
Yovanna
Yovanna is a fictional character played by actress Adria Arjona, known from her work in film and television.
-
C.
Marlen
Marlen is a village district of the town of Kehl in the German state of Baden-Württemberg.
-
D.
Marcela
Marcela is one of the given names of Alexia Juliana Marcela Laurentien, a member of the Dutch royal family.
-
E.
Rosa Elena
Rosa Elena is a Mexican public figure best known as the wife of former president Felipe Calderón and for her involvement in high-profile political and legal controversies.
- 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69deda8e64e48190873104a02a676ff3 |
completed | April 15, 2026, 12:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea5b96ae08190b15873634b67e8d9 |
completed | May 9, 2026, 3:10 a.m. |
| NEDg | Description generation | batch_69fea6dc14888190a80595c299dbaeac |
completed | May 9, 2026, 3:15 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69feaab0036c8190a486e4826e72925f |
completed | May 9, 2026, 3:32 a.m. |
Created at: April 10, 2026, 3 a.m.