Triple
T13328683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Modeling image patches with a directed hierarchy of Markov random fields |
E317508
|
entity |
| Predicate | typeOfModel |
P2390
|
FINISHED |
| Object | probabilistic image 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: probabilistic image model | Statement: [Modeling image patches with a directed hierarchy of Markov random fields, typeOfModel, probabilistic image model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfModel Context triple: [Modeling image patches with a directed hierarchy of Markov random fields, typeOfModel, probabilistic image model]
-
A.
typeOf
Indicates that one entity is a specific kind, class, or category instance of another more general entity.
-
B.
hasModelType
chosen
Indicates that an entity is associated with or classified under a specific model type.
-
C.
isModelOf
Indicates that one entity serves as a representation or abstraction that captures the structure or behavior of another entity.
-
D.
usesModelsType
Indicates that one entity employs or relies on a specific type or category of models in its operation or behavior.
-
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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f6babd88190a5d529df9584b9a4 |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:30 p.m.