Triple
T682246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Durham Technical Community College |
E13206
|
entity |
| Predicate | hasLearningMode |
P7876
|
FINISHED |
| Object | on-campus 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: on-campus instruction | Statement: [Durham Technical Community College, hasLearningMode, on-campus instruction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLearningMode Context triple: [Durham Technical Community College, hasLearningMode, on-campus instruction]
-
A.
offersEducationMode
chosen
Indicates that an entity provides a particular mode or format in which education or instruction is delivered.
-
B.
hasWorkingMode
Indicates that an entity operates under or supports a particular mode or configuration of functioning.
-
C.
trainingMethod
Indicates the specific approach, technique, or procedure used to train an entity (such as a person, model, or system).
-
D.
hasLanguageModel
Indicates that an entity possesses, uses, or is associated with a particular language model.
-
E.
educationalModel
Indicates that one entity serves as an educational model, framework, or paradigm that guides or structures the teaching, learning, or training practices of 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_69a4933e0f98819097d22766c49b61b8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a06f9ee88190a2d757aacd8e3f5b |
completed | March 1, 2026, 8:24 p.m. |
| PD | Predicate disambiguation | batch_69a49d1f0ccc819088c1527beabcb718 |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.