Triple
T7533561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RDF 1.0 |
E178086
|
entity |
| Predicate | typeOfTripleModel |
P29259
|
FINISHED |
| Object | subject–predicate–object triple model |
—
|
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: subject–predicate–object triple model | Statement: [RDF 1.0, typeOfTripleModel, subject–predicate–object triple model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfTripleModel Context triple: [RDF 1.0, typeOfTripleModel, subject–predicate–object triple model]
-
A.
triples
Indicates that one entity performs or achieves something three times or increases another entity to three times its original amount.
-
B.
isModelOf
Indicates that one entity serves as a representation or abstraction that captures the structure or behavior of another entity.
-
C.
typeOfNamedThing
chosen
Indicates that one entity is the specific type or category to which the named thing belongs.
-
D.
triskelionType
Indicates that one entity is classified as a specific type or variant of a triskelion.
-
E.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for 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_69c69f2acdbc8190b5a8320168c1d0ba |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8493964819086aeddfa4872a70b |
completed | March 27, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d8eedc81908c1ae421e0e63798 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:47 p.m.