Triple
T19785566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louisa |
E475254
|
entity |
| Predicate | hasRelatedName |
P3889
|
FINISHED |
| Object | Ludovica |
—
|
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: Ludovica | Statement: [Louisa, hasRelatedName, Ludovica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ludovica Context triple: [Louisa, hasRelatedName, Ludovica]
-
A.
Ludovica
chosen
Ludovica is an Italian feminine given name, traditionally associated with nobility and derived from the same Germanic roots as names like Louise and Ludwig.
-
B.
Guglielma
Guglielma is an Italian feminine given name, serving as the female variant of Guglielmo (William).
-
C.
Lodoletta
Lodoletta is an opera in three acts by Italian composer Pietro Mascagni, known for its verismo style and tragic love story.
-
D.
Nicoletta
Nicoletta is an Italian given name commonly used for women, derived from the name Nicola.
-
E.
Lorenza
Lorenza is a Chilean actress and model best known for her roles in films like "Knock Knock" and "Once Upon a Time in Hollywood."
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65387d3348190a31f9c2f9bc1c6d9 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:49 p.m.