Triple
T12445414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Protection of Lawful Commerce in Arms Act |
E297384
|
entity |
| Predicate | effect |
P374
|
FINISHED |
| Object | limits civil lawsuits against gun manufacturers and dealers for criminal misuse of firearms by third parties |
—
|
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: limits civil lawsuits against gun manufacturers and dealers for criminal misuse of firearms by third parties | Statement: [Protection of Lawful Commerce in Arms Act, effect, limits civil lawsuits against gun manufacturers and dealers for criminal misuse of firearms by third parties]
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_69d6ada166c48190b902972cd2408fa3 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d90f18c819083a36ff4b9be4a20 |
completed | April 10, 2026, 7:20 p.m. |
Created at: April 8, 2026, 9:55 p.m.