Triple
T10417055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Stéphanie of Belgium |
E245545
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Stéphanie |
E611321
|
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: Stéphanie | Statement: [Princess Stéphanie of Belgium, givenName, Stéphanie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stéphanie Context triple: [Princess Stéphanie of Belgium, givenName, Stéphanie]
-
A.
Stéphanie
chosen
Stéphanie is a Monegasque princess, singer, and fashion designer, best known as the youngest child of Prince Rainier III and Grace Kelly.
-
B.
Mélanie
Mélanie is a feminine given name of French origin commonly used in French-speaking countries.
-
C.
Sophie Dumond
Sophie Dumond is Arthur Fleck’s single-mother neighbor and tentative love interest in the 2019 film "Joker," representing his yearning for connection and normalcy amid his psychological unraveling.
-
D.
Léa
Léa is a French feminine given name commonly used in Francophone countries.
-
E.
Estelle
Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea1194e08190a18c3b3002147493 |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89f8a5b00819080c303bb0fc82f5a |
completed | April 10, 2026, 6:58 a.m. |
Created at: April 6, 2026, 12:11 p.m.