Triple
T7878698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amway |
E182922
|
entity |
| Predicate | hasBusinessModelFeature |
P59753
|
FINISHED |
| Object | multi-level compensation plan |
—
|
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: multi-level compensation plan | Statement: [Amway, hasBusinessModelFeature, multi-level compensation plan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBusinessModelFeature Context triple: [Amway, hasBusinessModelFeature, multi-level compensation plan]
-
A.
hasBusinessModelCharacteristic
chosen
Indicates that a business model possesses or exhibits a specific feature, quality, or attribute.
-
B.
usesBusinessModel
Indicates that one entity operates according to, or applies in practice, the business model defined or provided by another entity.
-
C.
hasFeatureCode
Indicates that an entity is associated with a specific feature identifier or code that characterizes one of its properties or attributes.
-
D.
businessModelType
Indicates the type or category of business model that characterizes how an entity creates, delivers, and captures value.
-
E.
hasBusinessNode
Indicates that an entity is associated with, linked to, or represented by a specific business-related node within a business structure or network.
- 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_69ca828a17248190b46defe758bc5ad3 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb39bd64e481909f699e7dd2818b8f |
completed | March 31, 2026, 3:04 a.m. |
| PD | Predicate disambiguation | batch_69cae928e1b88190b0620f4c4f03bc7d |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:57 p.m.