Triple
T8898498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Laetitia Aikin |
E211865
|
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: [Anna Laetitia Aikin, givenName, Laetitia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laetitia Context triple: [Anna Laetitia Aikin, 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.
Bénédicte
Bénédicte is the given name of Louise Bénédicte de Bourbon, a French noblewoman of the House of Bourbon.
-
C.
Léa
Léa is a French feminine given name commonly used in Francophone countries.
-
D.
Armande
Armande is a French given name historically associated with figures in the performing arts, notably in 17th-century France.
-
E.
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.
- 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_69ca83918d3081909b326fa3750cb8c8 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc642618908190b3df50cbbabff93d |
completed | April 1, 2026, 12:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1cb724c8190bc080d2a7f751e60 |
completed | April 3, 2026, 1:34 p.m. |
Created at: March 30, 2026, 6:54 p.m.