Triple
T6404202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laetitia Sadier |
E144137
|
entity |
| Predicate | givenName |
P17
|
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: [Laetitia Sadier, givenName, Laetitia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laetitia Context triple: [Laetitia Sadier, givenName, 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.
Armande
Armande is a French given name historically associated with figures in the performing arts, notably in 17th-century France.
-
C.
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.
-
D.
Liliane
Liliane is a feminine given name of French origin, notably borne by French heiress and businesswoman Liliane Bettencourt.
-
E.
Émilie
Émilie is the given first name of the French-born American actress Claudette Colbert, a major Hollywood star of the 1930s and 1940s.
- 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_69c008dc56fc81908d43ffcc11d73bdd |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c068b0950c819091169aa1a3be0e88 |
completed | March 22, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c638aaed0c8190939e751af7c3fa80 |
completed | March 27, 2026, 7:58 a.m. |
Created at: March 22, 2026, 4:35 p.m.