Triple
T16196316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mario vs. Donkey Kong series |
E393068
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Mario |
E31492
|
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: Mario | Statement: [Mario vs. Donkey Kong series, featuresCharacter, Mario]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mario Context triple: [Mario vs. Donkey Kong series, featuresCharacter, Mario]
-
A.
Mario
chosen
Mario is a fictional Italian plumber and the iconic protagonist of Nintendo's long-running Super Mario video game franchise.
-
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.
Mário
Mário is a masculine given name of Latin origin, widely used in Portuguese- and Italian-speaking countries.
-
D.
Spring Mario
Spring Mario is a special power-up form of Mario that encases him in a spring, allowing him to bounce to great heights and reach otherwise inaccessible areas.
-
E.
Hotel Mario
Hotel Mario is a 1994 Philips CD-i puzzle-platform video game based on the Mario franchise, infamous for its poor quality and awkward full-motion video cutscenes.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222d91130819080da4e2611612e27 |
completed | April 17, 2026, 12:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffff0f352081908324783743e47029 |
completed | May 10, 2026, 3:44 a.m. |
Created at: April 10, 2026, 5:02 a.m.