Triple
T12373921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giovanna Bellelli |
E295072
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Giovanna |
E263455
|
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: Giovanna | Statement: [Giovanna Bellelli, hasGivenName, Giovanna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Giovanna Context triple: [Giovanna Bellelli, hasGivenName, Giovanna]
-
A.
Giovanna
chosen
Giovanna is an Italian feminine given name equivalent to English "Jane," commonly used in Italy and among Italian-speaking communities.
-
B.
Giovannina
Giovannina is an Italian feminine given name, typically used as a diminutive or affectionate form of Giovanna.
-
C.
Caterina
Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
-
D.
Giuliana
Giuliana is an Italian feminine given name, commonly considered the female form of Giuliano.
-
E.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93fa8ca7c8190b3f8e9c2ec23e837 |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65ea0a4c4819091a7a66c3b73d776 |
completed | May 2, 2026, 8:29 p.m. |
Created at: April 8, 2026, 9:54 p.m.