Triple
T21843571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tui shou |
E539316
|
entity |
| Predicate | trainingMethodFor |
P16019
|
FINISHED |
| Object | application of taijiquan techniques |
—
|
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: application of taijiquan techniques | Statement: [Tui shou, trainingMethodFor, application of taijiquan techniques]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingMethodFor Context triple: [Tui shou, trainingMethodFor, application of taijiquan techniques]
-
A.
trainingMethod
chosen
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
B.
trainingUnder
Indicates that one entity is receiving instruction, guidance, or mentorship from another, typically in a subordinate or apprentice-like capacity.
-
C.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
D.
trainingModality
Indicates the method or format through which training or instruction is delivered or conducted.
-
E.
trainingParadigm
Indicates the specific methodological framework or approach used to train an entity (such as a model, system, or agent).
- 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_69e0c476c3c88190a92d08ebb59a128a |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0bd5248f08190ba208512eafbe7ad |
completed | April 28, 2026, 1:59 p.m. |
| PD | Predicate disambiguation | batch_69e6be8c14748190bdcc44a14d50bea4 |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 6:55 p.m.