Triple
T35297746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pontifical Catholic University of Puerto Rico |
E1019417
|
entity |
| Predicate | academicDegrees |
P6
|
FINISHED |
| Object | bachelor’s degrees |
—
|
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: bachelor’s degrees | Statement: [Pontifical Catholic University of Puerto Rico, academicDegrees, bachelor’s degrees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academicDegrees Context triple: [Pontifical Catholic University of Puerto Rico, academicDegrees, bachelor’s degrees]
-
A.
academicDegree
chosen
Indicates that an entity holds or has been awarded a specific academic degree.
-
B.
degreeRelatedTo
Indicates a relationship where one entity’s academic degree is connected or relevant to another entity, such as a person, field of study, or institution.
-
C.
academicStatus
Indicates the educational or scholarly standing or level an entity holds within an academic context.
-
D.
academicType
Indicates the specific academic category or classification associated with an entity (such as a work, program, or role).
-
E.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
- 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_69f76de7eedc8190a3bdc64ebbc05b42 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7901f599c8190941cb23c676c883d |
completed | May 3, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69f78e2f52e08190a77661223a96c601 |
completed | May 3, 2026, 6:04 p.m. |
Created at: May 3, 2026, 4:03 p.m.