Triple
T26663551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugenio María de Hostos Law School |
E672116
|
entity |
| Predicate | associatedWithDegreeType |
P134801
|
FINISHED |
| Object | Juris Doctor |
—
|
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: Juris Doctor | Statement: [Eugenio María de Hostos Law School, associatedWithDegreeType, Juris Doctor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithDegreeType Context triple: [Eugenio María de Hostos Law School, associatedWithDegreeType, Juris Doctor]
-
A.
hasDegree
Indicates that an entity possesses or has been awarded a specific academic or professional degree.
-
B.
eligibleDegree
Indicates that an academic degree qualifies its holder to be considered eligible for a particular program, position, or requirement.
-
C.
associatedWithAcademicProgram
Indicates that an entity has a formal connection or linkage to a specific academic program.
-
D.
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.
-
E.
typeOfDegreeHandled
chosen
Indicates the specific academic degree type that an entity is responsible for managing or processing.
- 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_69eecda00a9c8190b2691f4d89db03b6 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69ff5b233e9c8190adc06cca0758986b |
completed | May 9, 2026, 4:04 p.m. |
| PD | Predicate disambiguation | batch_69ff5a5682108190a006b23c4fcdcc7c |
completed | May 9, 2026, 4:01 p.m. |
Created at: April 27, 2026, 3:07 a.m.