Triple
T20262815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Mario Land |
E498886
|
entity |
| Predicate | includesVehicleLevel |
P139437
|
FINISHED |
| Object | submarine level |
—
|
LITERAL 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: submarine level | Statement: [Super Mario Land, includesVehicleLevel, submarine level]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesVehicleLevel Context triple: [Super Mario Land, includesVehicleLevel, submarine level]
-
A.
hasVehicularActivityLevel
Indicates the degree or intensity of vehicular activity associated with an entity, such as traffic volume or frequency of vehicle use.
-
B.
basedOnVehicle
Indicates that one entity is derived from, modeled after, or otherwise conceptually or functionally based on a particular vehicle.
-
C.
intendedVehicleClass
Indicates that one entity is designed or specified to be used with, or is appropriate for, a particular class or category of vehicle.
-
D.
supportsVehicle
Indicates that one entity provides the necessary strength, stability, or structure to bear the weight of a vehicle.
-
E.
appliedToVehicleType
Indicates that something (such as a rule, restriction, or condition) is specifically applicable to a particular type or category of vehicle.
- F. None of above. chosen
Provenance (4 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674cba2748190a886ecd8316dc518 |
completed | April 20, 2026, 6:47 p.m. |
| PD | Predicate disambiguation | batch_69e55b1b23f88190bdcbe2f81dd226dd |
completed | April 19, 2026, 10:45 p.m. |
| PDg | Predicate description generation | batch_69e56702ad04819099c1c08f28d16809 |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 11, 2026, 11:41 p.m.