Triple
T2875030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raka |
E56856
|
entity |
| Predicate | studiedAs |
P770
|
FINISHED |
| Object | canonical text in Afrikaans literary canon |
—
|
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: canonical text in Afrikaans literary canon | Statement: [Raka, studiedAs, canonical text in Afrikaans literary canon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: studiedAs Context triple: [Raka, studiedAs, canonical text in Afrikaans literary canon]
-
A.
studiedUnder
Indicates that one entity received instruction, training, or mentorship from another, typically in an academic or apprenticeship context.
-
B.
isStudiedIn
chosen
Indicates that a subject (such as a topic, field, or phenomenon) is examined, researched, or learned about within a particular context, environment, or discipline.
-
C.
partOfStudy
Indicates that something is a component, segment, or subset within a larger study or research project.
-
D.
hasSubjectOfStudy
Indicates that an entity (such as a person or organization) focuses on, researches, or specializes in a particular field or topic of study.
-
E.
hasLanguageOfStudy
Indicates that an entity studies or is engaged in learning a particular language.
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abe004a64481908f1897d9054a7368 |
completed | March 7, 2026, 8:21 a.m. |
| PD | Predicate disambiguation | batch_69abdd142e4c8190b424cb0c5ff40d04 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:03 p.m.