Triple
T11996522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rocky Mountain Fur Company |
E285543
|
entity |
| Predicate | usedBusinessModel |
P5635
|
FINISHED |
| Object | rendezvous system |
—
|
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: rendezvous system | Statement: [Rocky Mountain Fur Company, usedBusinessModel, rendezvous system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedBusinessModel Context triple: [Rocky Mountain Fur Company, usedBusinessModel, rendezvous system]
-
A.
usesBusinessModel
Indicates that one entity operates according to, or applies in practice, the business model defined or provided by another entity.
-
B.
laterBusinessModel
Indicates that one business model occurs or is adopted after another in time, representing a subsequent or successor business model in a sequence.
-
C.
usesBusinessSystem
Indicates that one entity makes use of a particular business system to perform its operations, processes, or functions.
-
D.
formerBusinessModel
chosen
Indicates that an entity previously operated under a particular business model, but no longer does so.
-
E.
businessModelType
Indicates the type or category of business model that characterizes how an entity creates, delivers, and captures value.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903c172788190b92042e9d10a48bf |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902b245cc8190af96a9c2bd9c6250 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.