Triple
T97013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scratch |
E1953
|
entity |
| Predicate | educationalUse |
P177
|
FINISHED |
| Object | K–12 computer science education |
—
|
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: K–12 computer science education | Statement: [Scratch, educationalUse, K–12 computer science education]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educationalUse Context triple: [Scratch, educationalUse, K–12 computer science education]
-
A.
educationalApproach
Indicates the method, strategy, or philosophy used to guide teaching and learning within an educational context.
-
B.
educates
Indicates that one entity provides instruction, knowledge, or training to another entity.
-
C.
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.
-
D.
educationSystem
Indicates the relationship in which an entity is part of, governed by, or operates within a particular system or structure of education.
-
E.
educationType
chosen
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a250cb400c8190b56343bbe19b48c7 |
completed | Feb. 28, 2026, 2:19 a.m. |
| PD | Predicate disambiguation | batch_69a24ebd19c48190bab291fea0ecc0c2 |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.