Triple
T27638606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bitcoin Foundation |
E696528
|
entity |
| Predicate | hasDonorModel |
P168088
|
FINISHED |
| Object | membership fees |
—
|
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: membership fees | Statement: [Bitcoin Foundation, hasDonorModel, membership fees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDonorModel Context triple: [Bitcoin Foundation, hasDonorModel, membership fees]
-
A.
hasDonorFocus
Indicates that an entity is oriented toward, tailored to, or primarily concerned with the interests, needs, or priorities of donors.
-
B.
isDonorProgramOf
Indicates that one entity is a donor program that provides funding or resources to support another entity.
-
C.
hasModelledFor
Indicates that one entity has served as a model for another entity, typically in a professional or representational context such as art, photography, or fashion.
-
D.
hasCareModel
Indicates that one entity uses, follows, or is governed by a particular model or approach to providing care.
-
E.
hadModel
Indicates that an entity possessed, used, or was associated with a particular model (e.g., a product, design, or version) at some point in time.
- F. None of above. chosen
Provenance (4 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_69ef5909f3848190805f35b76833e722 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f673633d288190b52ceb9f8a057c44 |
completed | May 2, 2026, 9:57 p.m. |
| PD | Predicate disambiguation | batch_69f66ec3d3d48190ab2f2b71939e572e |
completed | May 2, 2026, 9:38 p.m. |
| PDg | Predicate description generation | batch_69f67256d064819094be04fc1bbbc635 |
completed | May 2, 2026, 9:53 p.m. |
Created at: April 27, 2026, 2:25 p.m.