Triple
T10348796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romorantin-Lanthenay |
E243825
|
entity |
| Predicate | historicalRegion |
P915
|
FINISHED |
| Object | Sologne |
E572844
|
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: Sologne | Statement: [Romorantin-Lanthenay, historicalRegion, Sologne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sologne Context triple: [Romorantin-Lanthenay, historicalRegion, Sologne]
-
A.
Sologne
chosen
Sologne is a rural region in central France known for its forests, lakes, and hunting estates.
-
B.
Les Landes
Les Landes is a region in southwestern France known for its vast Atlantic coastline, extensive pine forests, and rural landscapes.
-
C.
Dombes
Dombes is a historic rural region in eastern France known for its many ponds, wetlands, and traditional fish farming.
-
D.
Touraine
Touraine is a historic region in central France, famed for its Loire Valley châteaux, wine production, and role as a former royal heartland.
-
E.
Coye-la-Forêt
Coye-la-Forêt is a small commune in the Oise department of northern France, known for its forested surroundings and residential character near the Chantilly area.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e946cbb881909b88536d0107995d |
completed | April 7, 2026, 11:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7951e65948190a25e559ba94be3c7 |
completed | April 9, 2026, 12:01 p.m. |
Created at: April 6, 2026, 11:56 a.m.