Triple
T16484608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franklin half dollar |
E400405
|
entity |
| Predicate | reverseLegendPlacement |
P122963
|
FINISHED |
| Object | UNITED STATES OF AMERICA around top |
—
|
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: UNITED STATES OF AMERICA around top | Statement: [Franklin half dollar, reverseLegendPlacement, UNITED STATES OF AMERICA around top]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reverseLegendPlacement Context triple: [Franklin half dollar, reverseLegendPlacement, UNITED STATES OF AMERICA around top]
-
A.
regionOfLegend
Indicates the geographic area or locale with which a particular legend or myth is associated.
-
B.
legendLocation
Indicates the place or setting where a particular legend, myth, or traditional story is situated or associated.
-
C.
reverseFeature
Indicates that one feature is the inverse or opposite counterpart of another feature in a given context.
-
D.
ribbonPosition
Indicates the spatial or ordered placement of a ribbon relative to a reference object or coordinate system.
-
E.
colorPlacement
Indicates the spatial or contextual position where a particular color is applied or appears in relation to an entity.
- 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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e05bf448190947b9da15fd29d0a |
completed | April 18, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69e22706b0588190a48a951c5211a617 |
completed | April 17, 2026, 12:26 p.m. |
| PDg | Predicate description generation | batch_69e24556c1348190902a4d116c3137d9 |
completed | April 17, 2026, 2:36 p.m. |
Created at: April 10, 2026, 5:13 a.m.