Triple
T101697
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massive Open Online Courses |
E2052
|
entity |
| Predicate | typicalContent |
P4446
|
FINISHED |
| Object | university-level courses |
—
|
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: university-level courses | Statement: [Massive Open Online Courses, typicalContent, university-level courses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalContent Context triple: [Massive Open Online Courses, typicalContent, university-level courses]
-
A.
primaryContent
chosen
Indicates that one entity serves as the main or most important content associated with another entity.
-
B.
typicalActivity
Indicates that an entity is commonly or characteristically engaged in a particular activity.
-
C.
typicalBackground
Indicates that an entity has a usual or commonly expected background, context, or setting associated with it.
-
D.
centralText
Indicates that one text element is positioned or designated as the central or primary text relative to surrounding content or layout.
-
E.
popularFor
Indicates that something is widely liked, recognized, or favored specifically because of a particular feature, quality, or use.
- 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.