Triple
T10114605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | What Works Cities |
E218325
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | data-driven government program |
C27440
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: data-driven government program Context triple: [What Works Cities, instanceOf, data-driven government program]
-
A.
government statistical program
A government statistical program is an organized, ongoing initiative by a public agency to systematically collect, process, analyze, and disseminate data on specific aspects of the population, economy, or environment to inform policy and public understanding.
-
B.
program of a state government agency
A program of a state government agency is an organized set of activities, services, and resources designed and managed by the agency to achieve specific public policy goals or deliver particular benefits to residents within the state.
-
C.
federal government program
A federal government program is an organized set of activities, services, or regulations funded and administered by the national government to achieve specific public policy goals.
-
D.
policy program
A policy program is a coordinated set of government or organizational initiatives, actions, and resources designed to achieve specific public or institutional goals through the implementation of formal policies.
-
E.
policy-relevant data compendium
A policy-relevant data compendium is a curated, structured collection of datasets and indicators organized to inform, support, and evaluate public policy decisions on specific issues or sectors.
- F. None of above. chosen
Provenance (1 batch)
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_69ca83da93fc8190b54e44bc2b34857c |
completed | March 30, 2026, 2:08 p.m. |
Created at: March 30, 2026, 9:04 p.m.