Triple
T30407468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ZEC |
E773516
|
entity |
| Predicate | rewardDistributionChange |
P158882
|
FINISHED |
| Object | development fund after first halving |
—
|
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: development fund after first halving | Statement: [ZEC, rewardDistributionChange, development fund after first halving]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rewardDistributionChange Context triple: [ZEC, rewardDistributionChange, development fund after first halving]
-
A.
rewardRecipient
Indicates the entity that receives a reward as a result of some action, event, or decision.
-
B.
rewardMechanism
Indicates a relationship where an entity provides or defines a system of incentives or compensation in response to certain actions, behaviors, or outcomes.
-
C.
rewardSignal
Indicates that one entity provides a signal representing feedback or incentive (such as a reward or penalty) to guide another entity’s behavior or learning process.
-
D.
redistributionEvent
chosen
Indicates an event in which resources, assets, or benefits are reallocated from one set of entities to another according to some redistribution rule or policy.
-
E.
rewardModel
Indicates a relationship where one entity serves as a model or framework for assigning rewards or evaluating outcomes for another entity or process.
- 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_69f22490b8b48190ab10c886a8d58c89 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f686204b2c8190afea8470275fd875 |
completed | May 2, 2026, 11:17 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 8:04 p.m.