Triple
T10397657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Autoroute A36 |
E245061
|
entity |
| Predicate | crossesRiver |
P416
|
FINISHED |
| Object | Saône |
E18164
|
NE FINISHED |
How this triple was built (2 steps)
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: Saône | Statement: [Autoroute A36, crossesRiver, Saône]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saône Context triple: [Autoroute A36, crossesRiver, Saône]
-
A.
Saône River
chosen
The Saône River is a major waterway in eastern France that flows through cities like Lyon and Dijon before joining the Rhône River.
-
B.
Isère River
The Isère River is a significant waterway in southeastern France that flows through the Alps and the city of Grenoble before joining the Rhône.
-
C.
Drôme River
The Drôme River is a scenic waterway in southeastern France known for flowing through the Drôme department and the foothills of the Alps before joining the Rhône.
-
D.
Nièvre
Nièvre is a rural department in central France’s Bourgogne-Franche-Comté region, known for its rolling countryside, the Loire River, and its capital city Nevers.
-
E.
Doubs
Doubs is a department in the Bourgogne-Franche-Comté region of eastern France, known for its Jura mountains, rivers, and proximity to the Swiss border.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9d0de448190b0bfd4d6c87d47fa |
completed | April 7, 2026, 11:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f416ab88e48190b3089caab7987191 |
completed | May 1, 2026, 2:57 a.m. |
Created at: April 6, 2026, 12:07 p.m.