Triple
T7147585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Violent Crime Control and Law Enforcement Act of 1994 |
E166606
|
entity |
| Predicate | containsProvision |
P1393
|
FINISHED |
| Object | funding for 100,000 new police officers |
—
|
LITERAL 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.
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: funding for 100,000 new police officers | Statement: [Violent Crime Control and Law Enforcement Act of 1994, containsProvision, funding for 100,000 new police officers]
Provenance (2 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_69c68886779c8190a8e3fbabffe68253 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e7d4f3388190941f03fd80b0c223 |
completed | March 27, 2026, 8:25 p.m. |
Created at: March 27, 2026, 2:46 p.m.