Triple
T101754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massive Open Online Courses |
E2052
|
entity |
| Predicate | advantage |
P487
|
FINISHED |
| Object | increased access to education |
—
|
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: increased access to education | Statement: [Massive Open Online Courses, advantage, increased access to education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: advantage Context triple: [Massive Open Online Courses, advantage, increased access to education]
-
A.
benefits
chosen
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
B.
advises
Indicates that one entity provides guidance, recommendations, or counsel to another entity.
-
C.
hasAdvantage
Indicates that one entity possesses a benefit, edge, or favorable position over another in a given context.
-
D.
above
Indicates that one entity is positioned higher than another along a vertical axis, without implying direct contact.
-
E.
promotes
Indicates that one entity actively supports, advances, or encourages the growth, adoption, or success of another entity or outcome.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a25760af348190bf402089c240887d |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a2563921f8819087f720b1c803579f |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.