Triple
T22050677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Topic 326 Financial Instruments—Credit Losses |
E544872
|
entity |
| Predicate | establishesModel |
P88131
|
FINISHED |
| Object | current expected credit loss 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: current expected credit loss model | Statement: [Topic 326 Financial Instruments—Credit Losses, establishesModel, current expected credit loss model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: establishesModel Context triple: [Topic 326 Financial Instruments—Credit Losses, establishesModel, current expected credit loss model]
-
A.
registerModel
Indicates the action of adding or enrolling a model into a system or registry so it becomes recognized and available for use.
-
B.
appliesModel
Indicates that one entity uses or executes a specific model on another entity or data set.
-
C.
definesClientServerModel
Indicates that one entity specifies or establishes a client–server interaction pattern or architecture for another entity.
-
D.
adoptedModel
chosen
Indicates that one entity has formally chosen, accepted, or implemented another entity as its preferred model or standard.
-
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_69e11e32445c8190ab97089b48a130bb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1283386f081908b70df81f38a5b1c |
completed | April 28, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69e6f643ca74819083e8ab78e843f243 |
completed | April 21, 2026, 4 a.m. |
Created at: April 16, 2026, 8:26 p.m.