Triple
T13491268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WTO Agreement on Preshipment Inspection |
E318636
|
entity |
| Predicate | purpose |
P79
|
FINISHED |
| Object | to prevent delays and unnecessary obstacles to trade caused by preshipment inspection |
—
|
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: to prevent delays and unnecessary obstacles to trade caused by preshipment inspection | Statement: [WTO Agreement on Preshipment Inspection, purpose, to prevent delays and unnecessary obstacles to trade caused by preshipment inspection]
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_69d806b6bfec819089222715b2e86c8e |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf4c66008190b287e0551889d7c8 |
completed | April 12, 2026, 2:42 p.m. |
Created at: April 9, 2026, 9:43 p.m.