Triple
T9386489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gironde |
E225917
|
entity |
| Predicate | hasSubprefecture |
P9697
|
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: [Gironde, hasSubprefecture, Libourne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Libourne Context triple: [Gironde, hasSubprefecture, 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.
Port of Lorient
The Port of Lorient is a major commercial and fishing harbor on France’s Atlantic coast, known for its maritime industry, naval facilities, and role in regional trade.
-
E.
Cherbourg
Cherbourg is a major French port city on the Cotentin Peninsula, known for its strategic naval harbor and cross-Channel ferry connections.
- 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_69ca842e9dcc8190a264119e683cfe04 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd50d3964c8190b0353f56df755db8 |
completed | April 1, 2026, 5:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1101e83e8819093fe8d25c819820b |
completed | April 4, 2026, 1:20 p.m. |
Created at: March 30, 2026, 7:45 p.m.