Triple
T35918070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidential $1 Coin Program |
E1038799
|
entity |
| Predicate | issueFrequency |
P171897
|
FINISHED |
| Object | up to four different designs per year |
—
|
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: up to four different designs per year | Statement: [Presidential $1 Coin Program, issueFrequency, up to four different designs per year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: issueFrequency Context triple: [Presidential $1 Coin Program, issueFrequency, up to four different designs per year]
-
A.
usualFrequency
chosen
Indicates how often an action, event, or relationship typically occurs within a given time frame.
-
B.
dealFrequency
Indicates how often a particular deal, transaction, or agreement occurs within a given period.
-
C.
inspectionFrequency
Indicates how often an entity is examined, checked, or reviewed within a given time period.
-
D.
isFrequently
Indicates that an action, state, or relationship occurs often or with high regularity between the related entities.
-
E.
complicationFrequency
Indicates how often complications occur in relation to a given procedure, condition, or intervention.
- 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_69f76e2320748190b7f5c4750d0cd0d3 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aaa7a89081909b1d7b3118c3d4b1 |
completed | May 3, 2026, 8:05 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d435288190b30b1991fb003121 |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:07 p.m.