Triple
T20752670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riccardina |
E510766
|
entity |
| Predicate | etymologicalOriginOf |
P5801
|
FINISHED |
| Object | Ricarda |
—
|
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: Ricarda | Statement: [Riccardina, etymologicalOriginOf, Ricarda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ricarda Context triple: [Riccardina, etymologicalOriginOf, Ricarda]
-
A.
Ricarda
chosen
Ricarda is a feminine given name, primarily used in German- and Spanish-speaking countries, derived from the male name Richard.
-
B.
Catharina
Catharina of Württemberg was a 19th-century German princess who became Queen consort of Westphalia through her marriage to Jérôme Bonaparte, Napoleon’s youngest brother.
-
C.
Catharina
Catharina is a feminine given name of Greek origin, commonly used in various European cultures and often associated with historical and religious figures.
-
D.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
-
E.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
- 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_69e0b4c909ec8190b05987f1639513f6 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c22be6588190b137193cb3184fc0 |
completed | April 21, 2026, 12:17 a.m. |
Created at: April 16, 2026, 12:34 p.m.