Triple
T24826344
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quinnipiac University School of Law |
E621200
|
entity |
| Predicate | offersProfessionalTrainingFor |
P40765
|
FINISHED |
| Object | attorneys |
—
|
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: attorneys | Statement: [Quinnipiac University School of Law, offersProfessionalTrainingFor, attorneys]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersProfessionalTrainingFor Context triple: [Quinnipiac University School of Law, offersProfessionalTrainingFor, attorneys]
-
A.
providesTrainingFor
chosen
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
B.
offersApprenticeshipTraining
Indicates that one entity provides apprenticeship-based training opportunities or programs to another entity.
-
C.
trainingIn
Indicates that one entity is undergoing or receiving training within the context, program, or domain specified by another entity.
-
D.
offersEducationIn
Indicates that an entity provides or delivers educational programs, courses, or instruction in a specified field, subject, or area.
-
E.
offersEducationTo
Indicates that one entity provides educational services, instruction, or learning opportunities to another entity.
- 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_69e2fac0c3b881909110e5a56c6fa46f |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f55e519978819087a1676564a74630 |
completed | May 2, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69f4a0edd10c81908a052ab864d57c54 |
completed | May 1, 2026, 12:47 p.m. |
Created at: April 18, 2026, 5:05 a.m.