Triple
T20187359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mario Burger |
E492896
|
entity |
| Predicate | inspiredBy |
P9
|
FINISHED |
| Object | Nintendo’s Mario character |
—
|
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: Nintendo’s Mario character | Statement: [Mario Burger, inspiredBy, Nintendo’s Mario character]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nintendo’s Mario character Context triple: [Mario Burger, inspiredBy, Nintendo’s Mario character]
-
A.
Super Mario video game franchise
The Super Mario video game franchise is Nintendo’s iconic platform game series starring Mario and his friends on adventures through the Mushroom Kingdom and beyond.
-
B.
Mario
Mario is an American R&B singer, songwriter, and occasional actor best known for his early-2000s hits like "Let Me Love You."
-
C.
Mario
chosen
Mario is a fictional Italian plumber and the iconic protagonist of Nintendo's long-running Super Mario video game franchise.
-
D.
Mario
Mario is the young, aspiring writer and romantic protagonist of Mario Vargas Llosa’s novel "Aunt Julia and the Scriptwriter."
-
E.
Mário
Mário is a masculine given name of Latin origin, widely used in Portuguese- and Italian-speaking countries.
- 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ad143c48190b9d52c331e8101d6 |
completed | April 20, 2026, 6:05 p.m. |
Created at: April 11, 2026, 11:36 p.m.