Triple
T18600049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IFRS 9 Financial Instruments |
E454594
|
entity |
| Predicate | impairmentModel |
P2006
|
FINISHED |
| Object | Expected credit loss |
—
|
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: Expected credit loss | Statement: [IFRS 9 Financial Instruments, impairmentModel, Expected credit loss]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impairmentModel Context triple: [IFRS 9 Financial Instruments, impairmentModel, Expected credit loss]
-
A.
hasImpairmentStatus
Indicates that an entity possesses a particular condition of functional limitation, disability, or impairment status.
-
B.
canImpair
Indicates that one entity has the potential or ability to weaken, damage, or reduce the normal function, quality, or effectiveness of another entity.
-
C.
hasImpairmentListing
Indicates that an entity is associated with a specific recognized category or listing of impairments.
-
D.
possibleModel
Indicates that one entity can serve as a potential or candidate model or template for another entity.
-
E.
model
chosen
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
- 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_69d8d38ae7e081908a98df1251842402 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5475018548190a2f497081af7ce55 |
completed | April 19, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69e478cf5e888190a0b1074b0c6525df |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:45 a.m.