Triple
T17497521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Mint American Women Quarters Program |
E426102
|
entity |
| Predicate | firstYearHonoreesCount |
P119394
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [U.S. Mint American Women Quarters Program, firstYearHonoreesCount, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstYearHonoreesCount Context triple: [U.S. Mint American Women Quarters Program, firstYearHonoreesCount, 5]
-
A.
numberOfHonoreesPerPeriod
chosen
Indicates the count of honorees associated with each defined time period.
-
B.
yearHonored
Indicates the specific year in which an entity received an honor, award, or formal recognition.
-
C.
maximumNumberOfLaureatesPerYear
Indicates the highest allowable or observed count of laureates associated with a given year.
-
D.
initialAwardYear
Indicates the year in which an entity first received a particular award.
-
E.
typicalNumberOfLaureatesPerYear
Indicates the usual or average number of laureates associated with a given award or context in a single year.
- 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.