Triple
T15185096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Departmental Council of Seine-et-Marne |
E362849
|
entity |
| Predicate | hasBudgetaryResponsibilityFor |
P29198
|
FINISHED |
| Object | construction and maintenance of collèges in Seine-et-Marne |
—
|
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: construction and maintenance of collèges in Seine-et-Marne | Statement: [Departmental Council of Seine-et-Marne, hasBudgetaryResponsibilityFor, construction and maintenance of collèges in Seine-et-Marne]
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_69d85a09a39c81908759f23268e2d408 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006674c088190ba635a78c30f5637 |
completed | April 15, 2026, 9:43 p.m. |
Created at: April 10, 2026, 3:09 a.m.