Triple
T650163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RAFAT |
E11327
|
entity |
| Predicate | trainingActivity |
P8834
|
FINISHED |
| Object | winter training for display season |
—
|
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: winter training for display season | Statement: [RAFAT, trainingActivity, winter training for display season]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingActivity Context triple: [RAFAT, trainingActivity, winter training for display season]
-
A.
trainingBase
Indicates that one entity serves as the training base, site, or facility where another entity receives training or instruction.
-
B.
activityType
chosen
Indicates the specific kind or category of action or event that an entity is engaged in or associated with.
-
C.
trainingGround
Indicates a location or context where entities engage in practice, drills, or preparation activities to develop or improve skills.
-
D.
activityTime
Indicates the time period during which an activity occurs or is scheduled to take place.
-
E.
training
Indicates that one entity is teaching, coaching, or otherwise helping another entity acquire or improve a skill, behavior, or capability.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f31e70c81909a2ac1d939f7ec07 |
completed | March 1, 2026, 8:18 p.m. |
| PD | Predicate disambiguation | batch_69a49d0eade081909c47e85ed55f808d |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.