Triple
T12985631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | America the Beautiful Quarters |
E321759
|
entity |
| Predicate | reverseDesignsHonor |
P1603
|
FINISHED |
| Object | sites in each U.S. state |
—
|
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: sites in each U.S. state | Statement: [America the Beautiful Quarters, reverseDesignsHonor, sites in each U.S. state]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reverseDesignsHonor Context triple: [America the Beautiful Quarters, reverseDesignsHonor, sites in each U.S. state]
-
A.
reverseDesignSubject
Indicates that the subject is the entity for which a design or plan is derived by reversing or backtracking from an existing outcome or artifact.
-
B.
honourOf
Indicates that one entity is the source, bearer, or cause of another entity’s honor, prestige, or distinguished recognition.
-
C.
badgeReverseDesign
chosen
Indicates the design or imagery that appears on the reverse (back) side of a badge.
-
D.
isHonor
Indicates that one entity is regarded as an honor, distinction, or source of prestige for another entity.
-
E.
reversed
Indicates that the direction or order of a previously defined relationship or sequence between entities is inverted.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dbdd94c8190ac4bbecca02dc77b |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:40 p.m.