Triple
T1763648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clinical Hospital of the University of Chile |
E38713
|
entity |
| Predicate | usedForTrainingOf |
P98
|
FINISHED |
| Object | medical students of the University of Chile |
—
|
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: medical students of the University of Chile | Statement: [Clinical Hospital of the University of Chile, usedForTrainingOf, medical students of the University of Chile]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedForTrainingOf Context triple: [Clinical Hospital of the University of Chile, usedForTrainingOf, medical students of the University of Chile]
-
A.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
B.
usedFor
chosen
Indicates that one entity serves a purpose, function, or role in accomplishing, enabling, or supporting another entity or activity.
-
C.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
D.
trainingDataIncludes
Indicates that one entity’s training dataset contains or incorporates the other entity as part of its data.
-
E.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by 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_69a8862d562481908d7025a1c1f67c0d |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab173936b4819097332ee185996bbd |
completed | March 6, 2026, 6:04 p.m. |
| PD | Predicate disambiguation | batch_69aa61c9e06c819085489e00cfe72153 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.