Triple
T6142921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doubs River |
E137004
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Besançon |
E64224
|
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: Besançon | Statement: [Doubs River, passesThrough, Besançon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Besançon Context triple: [Doubs River, passesThrough, Besançon]
-
A.
Besançon
chosen
Besançon is a historic city in eastern France, known for its well-preserved Vauban fortifications, rich cultural heritage, and role as a regional administrative and educational center.
-
B.
Épinal
Épinal is a historic town in northeastern France, known for its traditional image-printing industry and picturesque setting in the Vosges region.
-
C.
Brioude
Brioude is a historic town in south-central France known for its Romanesque Basilica of Saint-Julien and its location in the Haute-Loire department of the Auvergne region.
-
D.
Bourg-en-Bresse
Bourg-en-Bresse is a historic town in eastern France known as the capital of the Ain department, noted for its Renaissance architecture and the royal monastery of Brou.
-
E.
Mulhouse
Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
- 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_69c008a2c6308190a56519b22d55d083 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05cb50cb0819081ac64becf7aaf55 |
completed | March 22, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c669d473388190a6e998956dd48e7a |
completed | March 27, 2026, 11:28 a.m. |
Created at: March 22, 2026, 4:16 p.m.