Triple
T12713232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Britta |
E303772
|
entity |
| Predicate | isVariantOf |
P455
|
FINISHED |
| Object | Brigitte |
E169899
|
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: Brigitte | Statement: [Britta, isVariantOf, Brigitte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brigitte Context triple: [Britta, isVariantOf, Brigitte]
-
A.
Brigitte
chosen
Brigitte is a French former teacher best known as the wife of Emmanuel Macron, the President of France.
-
B.
Gisèle
Gisèle is a feminine given name of French origin, commonly used in Francophone countries and beyond.
-
C.
Mireille
Mireille is a five-act French opera by Charles Gounod, based on Frédéric Mistral’s Provençal poem "Mirèio."
-
D.
Catherine Brelet
Catherine Brelet is a French film producer best known as the wife and longtime collaborator of acclaimed Swedish actor Max von Sydow.
-
E.
Renée
Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
- 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9620a7554819083784897ff690652 |
completed | April 10, 2026, 8:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671bad5108190915d14c3ec3d2e27 |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:23 p.m.