Triple
T384392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legum Magister |
E8747
|
entity |
| Predicate | degreeCategory |
P6
|
FINISHED |
| Object | Master's degree |
—
|
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: Master's degree | Statement: [Legum Magister, degreeCategory, Master's degree]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: degreeCategory Context triple: [Legum Magister, degreeCategory, Master's degree]
-
A.
academicDegree
chosen
Indicates that an entity holds or has been awarded a specific academic degree.
-
B.
university
Indicates that an educational institution of higher learning is associated with or attended by a given entity.
-
C.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
D.
academicFocus
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
-
E.
academicStatus
Indicates the educational or scholarly standing or level an entity holds within an academic context.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec422b808190b6ddf747ef939151 |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e967d84c8190a6b647f78d95d4e4 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.