Triple
T20194460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volt Tackle |
E493045
|
entity |
| Predicate | learningMethod |
P94878
|
FINISHED |
| Object | breeding with Light Ball (earlier generations) |
—
|
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: breeding with Light Ball (earlier generations) | Statement: [Volt Tackle, learningMethod, breeding with Light Ball (earlier generations)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: learningMethod Context triple: [Volt Tackle, learningMethod, breeding with Light Ball (earlier generations)]
-
A.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
B.
usesLearningMechanism
chosen
Indicates that one entity employs or applies a particular learning mechanism or method in its functioning or behavior.
-
C.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
-
D.
learningRole
Indicates that one entity has a specific role or function within a learning or educational context in relation to another entity.
-
E.
structureLearning
Indicates a process in which an agent infers or constructs the underlying structure or dependency relationships within a set of variables, data, or a model.
- F. None of above.
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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ad7ed548190a893110fa2ffb144 |
completed | April 20, 2026, 6:05 p.m. |
| PD | Predicate disambiguation | batch_69e55b14c9d8819095453d0504d9222f |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:37 p.m.