Triple
T19785560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louisa |
E475254
|
entity |
| Predicate | hasAlternativeSpelling |
P457
|
FINISHED |
| Object | Luiza |
—
|
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: Luiza | Statement: [Louisa, hasAlternativeSpelling, Luiza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luiza Context triple: [Louisa, hasAlternativeSpelling, Luiza]
-
A.
Luisa
chosen
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
B.
Suzana
Suzana is the birth name of American actress Sasha Alexander, known for her roles in television series such as NCIS and Rizzoli & Isles.
-
C.
Elza
Elza is a feminine given name commonly used in Portuguese- and Spanish-speaking countries, often as a variant of Elsa.
-
D.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
E.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
- 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.