Triple
T100023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LL.M. |
E2019
|
entity |
| Predicate | degreeType |
P6
|
FINISHED |
| Object | second-cycle degree in law |
—
|
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: second-cycle degree in law | Statement: [LL.M., degreeType, second-cycle degree in law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: degreeType Context triple: [LL.M., degreeType, second-cycle degree in law]
-
A.
academicDegree
chosen
Indicates that an entity holds or has been awarded a specific academic degree.
-
B.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
C.
offersDegree
Indicates that an institution or program provides a specific academic degree as an available qualification.
-
D.
educatedAt
Indicates that an entity received education or formal training at a specified institution or place of learning.
-
E.
typeOfInstitution
Indicates the specific kind or category of institution that an entity belongs to or is classified as.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a253b95d4c81909d1f2bc37e799c44 |
completed | Feb. 28, 2026, 2:32 a.m. |
| PD | Predicate disambiguation | batch_69a24ebfc5a88190bdd1653b9fa541fe |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.