Triple
T18566146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IFRS 15 Revenue from Contracts with Customers |
E453764
|
entity |
| Predicate | introducesModel |
P100347
|
FINISHED |
| Object | Five-step revenue recognition 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: Five-step revenue recognition model | Statement: [IFRS 15 Revenue from Contracts with Customers, introducesModel, Five-step revenue recognition model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: introducesModel Context triple: [IFRS 15 Revenue from Contracts with Customers, introducesModel, Five-step revenue recognition model]
-
A.
introducedAsModel
Indicates that one entity is presented or identified to others in the role or capacity of a model.
-
B.
introducedForModel
Indicates that one entity was created, proposed, or brought into use specifically for application within a particular model.
-
C.
introducesSystem
chosen
Indicates that an entity presents, brings into use, or makes known a particular system to others.
-
D.
introducedModelFamily
Indicates that an entity (such as a person or organization) is responsible for first presenting or launching a particular model family.
-
E.
firstModel
Indicates that an entity is the initial or earliest model/version in a sequence or series of models.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e53afe3ee081909eeee62c889948f4 |
completed | April 19, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69e478c16e0c8190b03966aa23c395a6 |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:43 a.m.