Triple
T10568898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Clémentine of Belgium |
E249426
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Clémentine |
E27357
|
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: Clémentine | Statement: [Princess Clémentine of Belgium, givenName, Clémentine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clémentine Context triple: [Princess Clémentine of Belgium, givenName, Clémentine]
-
A.
Clémentine
chosen
Clémentine is a feminine given name of French origin, commonly used in Francophone countries and beyond.
-
B.
Cécile
Cécile is the sensitive and central protagonist of the French film "Cible émouvante," around whom the story’s emotional and narrative developments revolve.
-
C.
Léa
Léa is a French feminine given name commonly used in Francophone countries.
-
D.
Bénédicte
Bénédicte is the given name of Louise Bénédicte de Bourbon, a French noblewoman of the House of Bourbon.
-
E.
Mélanie
Mélanie is a feminine given name of French origin commonly used in French-speaking 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5272ff53c8190ae7c399d49b585f5 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b4c26ec8190910efdf4a236d654 |
completed | April 10, 2026, 7:11 p.m. |
Created at: April 6, 2026, 12:37 p.m.