Triple
T14165643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Director of the United States Patent and Trademark Office |
E351065
|
entity |
| Predicate | responsibleFor |
P636
|
FINISHED |
| Object | reducing patent and trademark application backlogs |
—
|
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: reducing patent and trademark application backlogs | Statement: [Director of the United States Patent and Trademark Office, responsibleFor, reducing patent and trademark application backlogs]
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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b207cc8190b85b1ff0910b54da |
completed | April 14, 2026, 3:48 p.m. |
Created at: April 10, 2026, 1 a.m.