Triple
T7716814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaga Hyakumangoku |
E174905
|
entity |
| Predicate | approximateStipend |
P78311
|
FINISHED |
| Object | one million koku |
—
|
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: one million koku | Statement: [Kaga Hyakumangoku, approximateStipend, one million koku]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateStipend Context triple: [Kaga Hyakumangoku, approximateStipend, one million koku]
-
A.
estimatedCost
Indicates the predicted or calculated monetary amount expected to be required for something, such as a project, item, or action.
-
B.
approximateNumberAwarded
Indicates the estimated quantity of awards or recognitions given in a particular context or event.
-
C.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
D.
estimatedUsing
Indicates that one entity’s value, state, or outcome is derived by applying an estimation method, model, or procedure based on another entity.
-
E.
scholarshipEquivalency
Indicates that one scholarship is considered equal in value, coverage, or benefit to another scholarship or financial award.
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ebb7448190ae8d47fe0cbb0907 |
completed | March 27, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69c701683dec8190be9861e592aa8ce0 |
completed | March 27, 2026, 10:15 p.m. |
| PDg | Predicate description generation | batch_69c702e9a32081909a153190a62af426 |
completed | March 27, 2026, 10:21 p.m. |
Created at: March 27, 2026, 4:05 p.m.