Triple
T3558296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luma |
E75273
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object |
Rosalina
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.
|
E370306
|
NE FINISHED |
How this triple was built (4 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: [Luma, associatedWith, Rosalina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosalina Context triple: [Luma, associatedWith, Rosalina]
-
A.
Rosalinda
Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
-
B.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
-
C.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
D.
Blanca
Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
-
E.
Mariquita
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rosalina Triple: [Luma, associatedWith, Rosalina]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rosalina Target entity description: 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.
-
A.
Rosalinda
Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
-
B.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
-
C.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
D.
Blanca
Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
-
E.
Mariquita
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
- F. None of above. chosen
Provenance (5 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_69ad85d45090819086f34fb85d850a1e |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0881d50819092332491b9527c9d |
completed | March 8, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bb9983f48190bda2749d93c74a8d |
completed | March 13, 2026, 7:24 a.m. |
| NEDg | Description generation | batch_69b3bcae84a48190b085f253773cd14f |
completed | March 13, 2026, 7:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3f90df47c81908855021f68ca7ec8 |
completed | March 13, 2026, 11:46 a.m. |
Created at: March 8, 2026, 3:20 p.m.