Triple
T17497512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Mint American Women Quarters Program |
E426102
|
entity |
| Predicate | featuresOnReverse |
P80690
|
FINISHED |
| Object | notable American women |
—
|
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: notable American women | Statement: [U.S. Mint American Women Quarters Program, featuresOnReverse, notable American women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresOnReverse Context triple: [U.S. Mint American Women Quarters Program, featuresOnReverse, notable American women]
-
A.
featuresInversion
Indicates that one entity exhibits or incorporates an inversion of another entity, such as a reversed, mirrored, or otherwise inverted form or structure.
-
B.
reverseFeature
Indicates that one feature is the inverse or opposite counterpart of another feature in a given context.
-
C.
hasReverseFeature
Indicates that one entity possesses a feature that functions in the opposite or reverse manner of another related feature.
-
D.
featuresReimaginedVersionOf
Indicates that something includes or presents a newly interpreted or updated version of another existing work or element.
-
E.
featuresIn
chosen
Indicates that an entity appears or plays a role within another entity, such as a person or element being included in a work, event, or context.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4520f6790819092c36e0e4ecc4cd3 |
completed | April 19, 2026, 3:54 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f5fbcc8190a6ea9639bf5650da |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:48 a.m.