Triple
T14523313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosanna Arquette |
E340706
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rosanna |
E1014318
|
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: Rosanna | Statement: [Rosanna Arquette, givenName, Rosanna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosanna Context triple: [Rosanna Arquette, givenName, Rosanna]
-
A.
Rosanna
Rosanna is a residential suburb in Melbourne, Australia, known for its leafy streets, family-friendly atmosphere, and proximity to parklands and public transport.
-
B.
Rosanna
chosen
Rosanna is a feminine given name of Latin origin, derived from a combination of "Rose" and "Anna."
-
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.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
E.
Carmen Luna
Carmen Luna is a fiercely ambitious and witty Latina maid and aspiring singer who navigates love, class, and career struggles in the TV series "Devious Maids."
- 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_69d822dac79c8190a84a073f3cbaced5 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dea04f16f88190ba357b0f8021b46b |
completed | April 14, 2026, 8:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a50324481909713bbf68295e839 |
completed | May 8, 2026, 5:53 a.m. |
Created at: April 10, 2026, 1:22 a.m.