Triple
T30338328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Embeddings from Language Models |
E771680
|
entity |
| Predicate | trainingDirection |
P164309
|
FINISHED |
| Object | forward |
—
|
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: forward | Statement: [Embeddings from Language Models, trainingDirection, forward]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingDirection Context triple: [Embeddings from Language Models, trainingDirection, forward]
-
A.
trainingOrientation
chosen
Indicates the direction or focus of training activities in relation to an entity or context.
-
B.
trainingPath
Indicates the sequence or structure of learning or training steps that guide an entity’s progression from a starting level to a targeted skill or competency.
-
C.
trainingUnder
Indicates that one entity is receiving instruction, guidance, or mentorship from another, typically in a subordinate or apprentice-like capacity.
-
D.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
E.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
- 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_69f2248aba24819095bb86480d55b23b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6b21e7e088190832a3db585daea1c |
completed | May 3, 2026, 2:25 a.m. |
| PD | Predicate disambiguation | batch_69f6b14faf608190a25b977c0740729c |
completed | May 3, 2026, 2:22 a.m. |
Created at: April 29, 2026, 7:55 p.m.