Triple
T22489692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Self VM |
E555979
|
entity |
| Predicate | objectModel |
P535
|
FINISHED |
| Object | prototype-based without classes |
—
|
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: prototype-based without classes | Statement: [Self VM, objectModel, prototype-based without classes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: objectModel Context triple: [Self VM, objectModel, prototype-based without classes]
-
A.
dataModel
chosen
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
-
B.
typicalObjectType
Indicates that something is a common or characteristic type of object typically associated with or involved in another entity or situation.
-
C.
ORM
Indicates a relationship where one entity is responsible for managing, representing, or mediating access to another entity, often abstracting underlying details or complexity.
-
D.
modeledWith
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
-
E.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
- 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_69e11e53897c819088863779f8c50bb0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15c3f3b4c819092bfe6495c61db46 |
completed | April 29, 2026, 1:17 a.m. |
| PD | Predicate disambiguation | batch_69e898b6eee08190ba673a0ee329e671 |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:49 p.m.