Triple
T34260282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | College of Law (Silliman University) |
E879007
|
entity |
| Predicate | trainsForQualification |
P137368
|
FINISHED |
| Object | lawyer |
—
|
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: lawyer | Statement: [College of Law (Silliman University), trainsForQualification, lawyer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsForQualification Context triple: [College of Law (Silliman University), trainsForQualification, lawyer]
-
A.
resultedInQualificationFor
Indicates that one event, action, or condition caused or led directly to an entity becoming eligible for a particular qualification, status, or credential.
-
B.
trainsInDiscipline
chosen
Indicates that one entity undergoes training or instruction within a particular discipline, field, or area of expertise associated with another entity.
-
C.
qualifyingFor
Indicates that one entity meets the necessary conditions or criteria to be eligible for another entity, status, or action.
-
D.
maintainsTrainsFor
Indicates that one entity is responsible for servicing, repairing, or otherwise keeping trains operational for another entity.
-
E.
alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training 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_69f349b421cc8190b4b4655e1d612548 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f728a345488190bd7e6751b09ac591 |
completed | May 3, 2026, 10:51 a.m. |
| PD | Predicate disambiguation | batch_69f7283ef2608190a7a85d7e7f5332c0 |
completed | May 3, 2026, 10:49 a.m. |
Created at: May 1, 2026, 1:56 a.m.