Triple
T15430521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Letizia |
E369625
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Laetitia |
E236777
|
NE FINISHED |
How this triple was built (2 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: Laetitia | Statement: [Letizia, hasVariant, Laetitia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laetitia Context triple: [Letizia, hasVariant, Laetitia]
-
A.
Laetitia
chosen
Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
-
B.
Lucienne
Lucienne is a feminine given name of French origin, traditionally used in Francophone countries.
-
C.
Bénédicte
Bénédicte is the given name of Louise Bénédicte de Bourbon, a French noblewoman of the House of Bourbon.
-
D.
Léa
Léa is a French feminine given name commonly used in Francophone countries.
-
E.
Armande
Armande is a French given name historically associated with figures in the performing arts, notably in 17th-century France.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ed8ea888190bff8dc14859cca31 |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c3293dc819097f9e56963c333ee |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 3:21 a.m.