Triple
T22030703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liliane Bettencourt |
E544076
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Bettencourt |
—
|
NE NERFINISHED |
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: Bettencourt | Statement: [Liliane Bettencourt, familyName, Bettencourt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bettencourt Context triple: [Liliane Bettencourt, familyName, Bettencourt]
-
A.
Bettencourt
chosen
Bettencourt is a prominent French surname most famously associated with Liliane Bettencourt, the L'Oréal heiress and once one of the world's wealthiest women.
-
B.
Boucicaut
Boucicaut is a station on the Paris Métro serving the 15th arrondissement of Paris.
-
C.
Labouisse
Labouisse is a surname most notably associated with Henry Richardson Labouisse Jr., an American diplomat and former Executive Director of UNICEF.
-
D.
Caillaux
Caillaux is a French surname most notably associated with Joseph Caillaux, an influential early 20th-century French politician and statesman.
-
E.
Valette
Valette is a French surname most notably associated with Pierre-Adolphe Valette, an influential early 20th-century Impressionist painter and teacher in Manchester.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e2f98c8819083e11eab90942a78 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127ed0cb08190aead0838cc62934c |
completed | April 28, 2026, 9:34 p.m. |
Created at: April 16, 2026, 8:24 p.m.