Triple
T3685617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dordogne |
E78217
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Libourne |
E262008
|
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: Libourne | Statement: [Dordogne, passesThrough, Libourne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Libourne Context triple: [Dordogne, passesThrough, Libourne]
-
A.
Libourne
chosen
Libourne is a commune in southwestern France’s Gironde department, known as a wine-trading center and gateway to the Bordeaux wine region.
-
B.
Lorient
Lorient is a port city in the Brittany region of northwestern France, known for its maritime heritage and annual Interceltic Festival.
-
C.
Saint-Jean-de-Luz
Saint-Jean-de-Luz is a historic fishing port and seaside resort town on France’s Basque coast, known for its picturesque bay and well-preserved old town.
-
D.
Cherbourg
Cherbourg is a major French port city on the Cotentin Peninsula, known for its strategic naval harbor and cross-Channel ferry connections.
-
E.
Saint-Malo
Saint-Malo is a historic walled port city in Brittany, northwestern France, known for its maritime heritage, privateering past, and dramatic coastal setting on the English Channel.
- 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_69ad85e285a081908f8cbfa9e2ed9b75 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc4c49b408190bc800bcf9745fe4f |
completed | March 8, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4c3bd473c8190b814689f3c76cada |
completed | March 14, 2026, 2:11 a.m. |
Created at: March 8, 2026, 3:26 p.m.