Triple
T27584443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OMT method |
E699655
|
entity |
| Predicate | functionalModelRepresents |
P113729
|
FINISHED |
| Object | data transformations |
—
|
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: data transformations | Statement: [OMT method, functionalModelRepresents, data transformations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: functionalModelRepresents Context triple: [OMT method, functionalModelRepresents, data transformations]
-
A.
componentRepresents
Indicates that one component stands in for, symbolizes, or models another entity or concept within a system or context.
-
B.
modeledWith
chosen
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
-
C.
entityRepresents
Indicates that one entity stands for, symbolizes, or serves as a representation of another entity.
-
D.
requiresModelingOf
Indicates that one entity depends on another entity being represented or simulated in a model in order for it to be properly defined, analyzed, or executed.
-
E.
isPartOfRepresentationSystem
Indicates that one entity functions as a component or element within a broader representational system (such as a notation, coding scheme, or symbolic framework).
- 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_69ef6a4cb8b881909b3a8d630fd89df2 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f66c5c13808190887180099745673b |
completed | May 2, 2026, 9:27 p.m. |
| PD | Predicate disambiguation | batch_69f66abddc448190a488852f8abdeb2c |
completed | May 2, 2026, 9:21 p.m. |
Created at: April 27, 2026, 2:03 p.m.