Triple
T2122478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Langres Plateau |
E43955
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Langres |
E110891
|
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: Langres | Statement: [Langres Plateau, hasSettlement, Langres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Langres Context triple: [Langres Plateau, hasSettlement, Langres]
-
A.
Langres
chosen
Langres is a historic fortified town in northeastern France known for its well-preserved ramparts and as the birthplace of Enlightenment philosopher Denis Diderot.
-
B.
Tournus
Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
-
C.
Fougères
Fougères is a historic town in Brittany, northwestern France, known for its impressive medieval castle and well-preserved old quarter.
-
D.
Lisieux
Lisieux is a town and commune in the Calvados department of Normandy in northwestern France, known as a major Catholic pilgrimage site associated with Saint Thérèse of Lisieux.
-
E.
Yssingeaux
Yssingeaux is a commune in south-central France that serves as an administrative and service center in the Haute-Loire department.
- 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_69a88717cfe48190b7ecdd68c824848a |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abbb51e8088190a1aeafee4e8dff63 |
completed | March 7, 2026, 5:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc6251b848190911748bd72b3dc25 |
completed | March 10, 2026, 7:20 a.m. |
Created at: March 4, 2026, 7:44 p.m.