Triple
T2792194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | gold dollar |
E61954
|
entity |
| Predicate | type1Years |
P43629
|
FINISHED |
| Object | 1849–1854 |
—
|
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: 1849–1854 | Statement: [gold dollar, type1Years, 1849–1854]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: type1Years Context triple: [gold dollar, type1Years, 1849–1854]
-
A.
hasYearType
Indicates a relationship where an entity is associated with a specific classification or category of year (such as calendar, fiscal, academic, or other year type).
-
B.
hasTypeOfYear
Indicates that a given year is classified as belonging to a specific type or category of year (e.g., fiscal, academic, leap).
-
C.
yearType
Indicates the classification or category assigned to a specific year (e.g., academic, fiscal, calendar, leap).
-
D.
serviceYears
Indicates the number of years an entity has provided service or been in a particular role, position, or organization.
-
E.
yearOfUse
Indicates the specific year during which something was in use or actively utilized.
- 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_69ab4b7f51d881908768300ebd2fbdae |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdeea881481908d759c72798a50fb |
completed | March 7, 2026, 8:16 a.m. |
| PD | Predicate disambiguation | batch_69abdd025c948190a97dd961a9592bac |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abdee94c2081908e5075e87e70780a |
completed | March 7, 2026, 8:16 a.m. |
Created at: March 6, 2026, 9:58 p.m.