Triple
T9029570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zcash |
E216133
|
entity |
| Predicate | hasBlockRewardHalvingInterval |
P8888
|
FINISHED |
| Object | 840000 blocks |
—
|
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: 840000 blocks | Statement: [Zcash, hasBlockRewardHalvingInterval, 840000 blocks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBlockRewardHalvingInterval Context triple: [Zcash, hasBlockRewardHalvingInterval, 840000 blocks]
-
A.
blockRewardHalvingIntervalBlocks
chosen
Indicates the number of blocks that must be mined before the block reward is reduced (halved) according to the protocol’s schedule.
-
B.
secondHalvingYear
Indicates the calendar year in which the second halving event of a recurring, typically periodic, process or quantity occurs.
-
C.
thirdHalvingYear
Indicates the calendar year in which the third occurrence of a recurring halving event (such as a scheduled reduction in rate or quantity) takes place.
-
D.
fourthHalvingYear
Indicates the calendar year in which the fourth halving event of a recurring halving process occurs.
-
E.
firstHalvingYear
Indicates the calendar year in which the first halving event associated with an entity (such as a cryptocurrency’s block reward) occurs.
- 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_69ca83a5fa88819088144801b4dd7245 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6a9bcb508190b58751f1772407d4 |
completed | April 1, 2026, 12:45 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee3597c81908919cf866ae95c24 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:08 p.m.