Triple
T10341029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Associated Countries to the European Research Area |
E243127
|
entity |
| Predicate | hasEffect |
P9
|
FINISHED |
| Object | reducing fragmentation of the European research landscape |
—
|
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 fragmentation of the European research landscape | Statement: [Associated Countries to the European Research Area, hasEffect, reducing fragmentation of the European research landscape]
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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e0a645348190af91450360a9abed |
completed | April 7, 2026, 10:47 a.m. |
Created at: April 6, 2026, 11:55 a.m.