Triple
T3208527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Civil Services Academy Lahore |
E67220
|
entity |
| Predicate | trainingMode |
P16019
|
FINISHED |
| Object | classroom instruction |
—
|
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: classroom instruction | Statement: [Civil Services Academy Lahore, trainingMode, classroom instruction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingMode Context triple: [Civil Services Academy Lahore, trainingMode, classroom instruction]
-
A.
trainingUse
Indicates that something is used for training purposes, such as preparing, educating, or improving the skills or performance of an entity.
-
B.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
C.
trainingVersion
Indicates that one entity is a specific training iteration, release, or configuration derived from or associated with another entity.
-
D.
trainingParadigm
Indicates the specific methodological framework or approach used to train an entity (such as a model, system, or agent).
-
E.
trainingMethod
chosen
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaa59888481908bdaefa1968d7f04 |
completed | March 8, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69ad9e078f7c8190813d9fcb4f5071fb |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:07 p.m.