Triple
T8577189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PixelRNN |
E203075
|
entity |
| Predicate | probabilisticModel |
P29140
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [PixelRNN, probabilisticModel, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: probabilisticModel Context triple: [PixelRNN, probabilisticModel, yes]
-
A.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for another entity.
-
B.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
C.
definesProbability
Indicates that one entity specifies or assigns the probability value associated with another entity or event.
-
D.
stochastic
chosen
Indicates that the relationship or process involves randomness or probabilistic behavior rather than being fully deterministic.
-
E.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
- 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_69ca8328ebe481909a8c038fa79959b4 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbea97787481909ebbaa45f59cbdaa |
completed | March 31, 2026, 3:39 p.m. |
| PD | Predicate disambiguation | batch_69cbd11b13108190b07f8f161425a585 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:22 p.m.