Triple
T3593992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mario Kart 8 |
E76091
|
entity |
| Predicate | includesCharacter |
P5716
|
FINISHED |
| Object | Rosalina |
E370306
|
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: Rosalina | Statement: [Mario Kart 8, includesCharacter, Rosalina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosalina Context triple: [Mario Kart 8, includesCharacter, Rosalina]
-
A.
Rosalina
chosen
Rosalina is a celestial princess and guardian of the cosmos in Nintendo's Super Mario series, known for caring for the star-like Lumas and piloting the Comet Observatory.
-
B.
Rosalinda
Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
-
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.
Blanca
Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
- 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_69ad85d8042081908af94a04c410dec0 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc15d3e308190b8352ef1f054f9c3 |
completed | March 8, 2026, 6:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b43307cb548190824bb6704e339bff |
completed | March 13, 2026, 3:53 p.m. |
Created at: March 8, 2026, 3:22 p.m.