Triple
T7203656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Françoise Bettencourt Meyers |
E148611
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Françoise |
E146513
|
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: Françoise | Statement: [Françoise Bettencourt Meyers, givenName, Françoise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Françoise Context triple: [Françoise Bettencourt Meyers, givenName, Françoise]
-
A.
Françoise
chosen
Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
-
B.
Armande
Armande is a French given name historically associated with figures in the performing arts, notably in 17th-century France.
-
C.
Jeanne-Françoise
Jeanne-Françoise is the given name of Juliette Récamier, the famed French socialite and salon hostess of the early 19th century.
-
D.
Renée
Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
-
E.
Valérie
Valérie is a French feminine given name commonly used in Francophone countries.
- 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_69c687e8cf188190b5f3ecffd681f04e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e94bfb2c81909ab492757435fce4 |
completed | March 27, 2026, 8:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cbecb7ec819080b12a63dfa56328 |
completed | March 28, 2026, 12:39 p.m. |
Created at: March 27, 2026, 2:52 p.m.