Triple
T2107664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nouvelle-Aquitaine |
E42430
|
entity |
| Predicate | formedByMergerOf |
P77
|
FINISHED |
| Object | Limousin |
E84116
|
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: Limousin | Statement: [Nouvelle-Aquitaine, formedByMergerOf, Limousin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Limousin Context triple: [Nouvelle-Aquitaine, formedByMergerOf, Limousin]
-
A.
Limousin
chosen
Limousin is a former administrative region in central France known for its rural landscapes, cattle breeding, and historic towns such as Limoges and Tulle.
-
B.
Charolais
Charolais is a historic rural region in eastern France renowned for its high-quality beef cattle and rich agricultural traditions.
-
C.
Bouvier
Bouvier is the maiden surname of Jacqueline Kennedy Onassis, associated with a prominent American socialite and political family.
-
D.
Mouton
Mouton is an academic publishing house known for its influential works in linguistics and related fields.
-
E.
White Park
White Park is a public recreational park in Morgantown, West Virginia, known for its green spaces, walking trails, and community amenities.
- 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_69a8871040f08190aac2e2d0ab6b47ad |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abbadf12b88190acc513d8512777b2 |
completed | March 7, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae306e040081909334f2a70036c26e |
completed | March 9, 2026, 2:29 a.m. |
Created at: March 4, 2026, 7:43 p.m.