Triple
T14815634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Princess Luisa Carlotta of the Two Sicilies |
E348306
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Luisa |
E44829
|
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: Luisa | Statement: [Princess Luisa Carlotta of the Two Sicilies, givenName, Luisa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luisa Context triple: [Princess Luisa Carlotta of the Two Sicilies, givenName, Luisa]
-
A.
Luisa
chosen
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
B.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
C.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
-
D.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
-
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_69d822eb8f588190bf53445e730a934f |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decfe0e89c81908c0e1fe2bc3ebcfc |
completed | April 14, 2026, 11:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe64f8b4148190bc24f9a307178419 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:49 a.m.