Triple
T18949109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Law School of Shenzhen University |
E463596
|
entity |
| Predicate | trainsStudentsIn |
P40765
|
FINISHED |
| Object | various fields of law |
—
|
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: various fields of law | Statement: [Law School of Shenzhen University, trainsStudentsIn, various fields of law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsStudentsIn Context triple: [Law School of Shenzhen University, trainsStudentsIn, various fields of law]
-
A.
alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training another entity.
-
B.
providesTrainingFor
chosen
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
C.
maintainsTrainsFor
Indicates that one entity is responsible for servicing, repairing, or otherwise keeping trains operational for another entity.
-
D.
leadsToTrainingAt
Indicates that one entity causes, results in, or serves as a pathway to another entity undergoing training.
-
E.
usesStudentsFor
Indicates that one entity employs or exploits students as a resource or means to carry out its activities or achieve its objectives.
- 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_69d8dcfec90481909e926be9767e5779 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d541ef18819080b2e253dd23835d |
completed | April 20, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69e4a2efec5c8190840704016bf547a1 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, noon