Triple
T4426004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E-Rate program |
E95210
|
entity |
| Predicate | fundingCapType |
P55924
|
FINISHED |
| Object | annual funding cap |
—
|
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: annual funding cap | Statement: [E-Rate program, fundingCapType, annual funding cap]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fundingCapType Context triple: [E-Rate program, fundingCapType, annual funding cap]
-
A.
funderType
Indicates the category or kind of organization or individual that provides funding in the relationship.
-
B.
fundingModel
Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
-
C.
fundingContext
Indicates the circumstances, purpose, or conditions under which funding is provided or used in a given relationship or action.
-
D.
fundingSpeed
Indicates how quickly financial resources are provided or disbursed within a given funding relationship or process.
-
E.
fundingPolicy
Indicates the rules or guidelines governing how financial resources are allocated, distributed, or managed within a given context.
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3554e40ec8190982acc0948da2f42 |
completed | March 13, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69b34f5eabe88190a12b244ea71e46d6 |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b3505a87b4819083fbbd58870e520b |
completed | March 12, 2026, 11:46 p.m. |
Created at: March 12, 2026, 11:30 p.m.