Triple
T3097319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caterina |
E64628
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Caterine |
E64628
|
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: Caterine | Statement: [Caterina, hasVariant, Caterine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caterine Context triple: [Caterina, hasVariant, Caterine]
-
A.
Giovanna
Giovanna is an Italian feminine given name equivalent to English "Jane," commonly used in Italy and among Italian-speaking communities.
-
B.
Caterina
chosen
Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
-
C.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
-
D.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
E.
Bianca
Bianca is a courtesan in Shakespeare’s tragedy "Othello," romantically involved with Cassio and used as a pawn in Iago’s schemes.
- 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_69ad857dc98481909e585dc3372e3ed5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada23cbe3c8190b7ec5cfd464a1ca8 |
completed | March 8, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20378a1488190bd0fa7d3f4639220 |
completed | March 12, 2026, 12:06 a.m. |
Created at: March 8, 2026, 3:03 p.m.