Triple
T7091477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arrondissement of Caen |
E165203
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Évrecy
Évrecy is a small commune in the Calvados department of the Normandy region in northwestern France.
|
E640645
|
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: Évrecy | Statement: [Arrondissement of Caen, contains, Évrecy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Évrecy Context triple: [Arrondissement of Caen, contains, Évrecy]
-
A.
Charny
Charny is a French commune located in the Île-de-France region, known for its rural character within the greater Paris metropolitan area.
-
B.
Douaumont
Douaumont is a small commune in northeastern France best known for its World War I battlefield sites near Verdun, including major memorials and military cemeteries.
-
C.
L’Arbresle
L’Arbresle is a small commune in eastern France’s Auvergne-Rhône-Alpes region, known for its historic town center and proximity to Lyon.
-
D.
Orléat
Orléat is a small commune in central France’s Puy-de-Dôme department, known for its rural character within the Auvergne region.
-
E.
Fougères
Fougères is a historic town in Brittany, northwestern France, known for its impressive medieval castle and well-preserved old quarter.
- 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: Évrecy Triple: [Arrondissement of Caen, contains, Évrecy]
Generated description
Évrecy is a small commune in the Calvados department of the Normandy region in northwestern France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Évrecy Target entity description: Évrecy is a small commune in the Calvados department of the Normandy region in northwestern France.
-
A.
Charny
Charny is a French commune located in the Île-de-France region, known for its rural character within the greater Paris metropolitan area.
-
B.
Douaumont
Douaumont is a small commune in northeastern France best known for its World War I battlefield sites near Verdun, including major memorials and military cemeteries.
-
C.
L’Arbresle
L’Arbresle is a small commune in eastern France’s Auvergne-Rhône-Alpes region, known for its historic town center and proximity to Lyon.
-
D.
Orléat
Orléat is a small commune in central France’s Puy-de-Dôme department, known for its rural character within the Auvergne region.
-
E.
Fougères
Fougères is a historic town in Brittany, northwestern France, known for its impressive medieval castle and well-preserved old quarter.
- 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_69c6887e8c10819091cee237560d32da |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e53132288190b6da361d9c7218ab |
completed | March 27, 2026, 8:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7948cb3d48190993134d709924e3d |
completed | March 28, 2026, 8:42 a.m. |
| NEDg | Description generation | batch_69c7962ec0bc8190a9223ebb245d0914 |
completed | March 28, 2026, 8:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c79709ed808190a9f09f5350ba2ffd |
completed | March 28, 2026, 8:53 a.m. |
Created at: March 27, 2026, 2:41 p.m.