Triple
T3853250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lot |
E85346
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Dordogne |
E78217
|
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: Dordogne | Statement: [Lot, borders, Dordogne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dordogne Context triple: [Lot, borders, Dordogne]
-
A.
Dordogne
chosen
Dordogne is a major river in southwestern France known for flowing through the Dordogne valley, a region famed for its picturesque landscapes, historic towns, and prehistoric cave art.
-
B.
Aveyron
Aveyron is a rural department in southern France known for its rugged landscapes, medieval villages, and traditional gastronomy including Roquefort cheese.
-
C.
Gironde
Gironde is a department in southwestern France that encompasses much of the Bordeaux wine region, including renowned appellations such as Graves.
-
D.
Rouergue
Rouergue is a historic cultural region in southern France, centered around the present-day Aveyron department and known for its rural landscapes, medieval towns, and Occitan heritage.
-
E.
Touraine
Touraine is a historic region in central France, famed for its Loire Valley châteaux, wine production, and role as a former royal heartland.
- 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_69aed936de1c81908f91bed80f70abb2 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeec01f7b48190ba1ec89328b3fccb |
completed | March 9, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51231608c8190bbc5dc990fba1606 |
completed | March 14, 2026, 7:45 a.m. |
Created at: March 9, 2026, 3:19 p.m.