Triple
T853839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catholic University of America Columbus School of Law |
E18445
|
entity |
| Predicate | accreditsDegree |
P17719
|
FINISHED |
| Object | professional law 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: professional law degree | Statement: [Catholic University of America Columbus School of Law, accreditsDegree, professional law degree]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accreditsDegree Context triple: [Catholic University of America Columbus School of Law, accreditsDegree, professional law degree]
-
A.
accreditation
Indicates that an authoritative body has formally recognized an entity as meeting specified standards or requirements.
-
B.
academicDegree
Indicates that an entity holds or has been awarded a specific academic degree.
-
C.
hasDegree
Indicates that an entity possesses or has been awarded a specific academic or professional degree.
-
D.
accreditingBody
chosen
Indicates that an entity serves as the official organization that grants accreditation or formal recognition to another entity.
-
E.
scopeOfAccreditation
Indicates the specific range, domains, or activities for which an entity’s accreditation or formal recognition is valid and applicable.
- 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac389a44819093396a58d2afa700 |
completed | March 1, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69a4aa81ef348190b067f817574e9efe |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.