Triple
T1297259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puy-de-Dôme |
E27681
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Ambert
Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
|
E227407
|
NE FINISHED |
How this triple was built (4 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: Ambert | Statement: [Puy-de-Dôme, contains, Ambert]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ambert Context triple: [Puy-de-Dôme, contains, Ambert]
-
A.
Thoiry
Thoiry is a commune in the Ain department of eastern France, located near the Swiss border in the Pays de Gex region.
-
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.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
D.
Thonon-les-Bains
Thonon-les-Bains is a French spa and resort town in the Haute-Savoie region, known for its lakeside setting on Lake Geneva and views of the Alps.
-
E.
Chalon-sur-Saône
Chalon-sur-Saône is a historic city in eastern France’s Burgundy region, known as a former important river port and for its rich architectural and cultural heritage.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ambert Triple: [Puy-de-Dôme, contains, Ambert]
Generated description
Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ambert Target entity description: Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
-
A.
Thoiry
Thoiry is a commune in the Ain department of eastern France, located near the Swiss border in the Pays de Gex region.
-
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.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
D.
Thonon-les-Bains
Thonon-les-Bains is a French spa and resort town in the Haute-Savoie region, known for its lakeside setting on Lake Geneva and views of the Alps.
-
E.
Chalon-sur-Saône
Chalon-sur-Saône is a historic city in eastern France’s Burgundy region, known as a former important river port and for its rich architectural and cultural heritage.
- F. None of above. chosen
Provenance (5 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_69a496d6682881909ba658f1c1e0e2b0 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0f6bc90819094cad5d62550ea19 |
completed | March 1, 2026, 10:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae1fae5aa08190b6aa50b543a175b8 |
completed | March 9, 2026, 1:17 a.m. |
| NEDg | Description generation | batch_69ae204fe6148190915219beb27128bc |
completed | March 9, 2026, 1:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae20d09c748190aebbfb88f0eedbaa |
completed | March 9, 2026, 1:22 a.m. |
Created at: March 1, 2026, 7:51 p.m.