Triple
T19007070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | canton of Ussel |
E465114
|
entity |
| Predicate | containsAdministrativeTerritorialEntity |
P747
|
FINISHED |
| Object |
Valiergues
Valiergues is a small commune in the Corrèze department of central France.
|
E1355283
|
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: Valiergues | Statement: [canton of Ussel, containsAdministrativeTerritorialEntity, Valiergues]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valiergues Context triple: [canton of Ussel, containsAdministrativeTerritorialEntity, Valiergues]
-
A.
Sivergues
Sivergues is a small rural commune in southeastern France, known for its remote, picturesque setting in the Luberon region.
-
B.
Olliergues
Olliergues is a small commune in central France’s Puy-de-Dôme department, known for its rural setting in the Auvergne region.
-
C.
Valleraugue
Valleraugue is a small commune in southern France’s Cévennes region, known as a gateway to the nearby Mont Aigoual and its surrounding mountainous landscapes.
-
D.
Verrières
Verrières is a small French commune located within the Thiers arrondissement in the Puy-de-Dôme department of central France.
-
E.
Chauvigny
Chauvigny is a historic town in western France known for its medieval fortifications and picturesque setting in the Vienne department of the Nouvelle-Aquitaine region.
- 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: Valiergues Triple: [canton of Ussel, containsAdministrativeTerritorialEntity, Valiergues]
Generated description
Valiergues is a small commune in the Corrèze department of central France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Valiergues Target entity description: Valiergues is a small commune in the Corrèze department of central France.
-
A.
Sivergues
Sivergues is a small rural commune in southeastern France, known for its remote, picturesque setting in the Luberon region.
-
B.
Olliergues
Olliergues is a small commune in central France’s Puy-de-Dôme department, known for its rural setting in the Auvergne region.
-
C.
Valleraugue
Valleraugue is a small commune in southern France’s Cévennes region, known as a gateway to the nearby Mont Aigoual and its surrounding mountainous landscapes.
-
D.
Verrières
Verrières is a small French commune located within the Thiers arrondissement in the Puy-de-Dôme department of central France.
-
E.
Chauvigny
Chauvigny is a historic town in western France known for its medieval fortifications and picturesque setting in the Vienne department of the Nouvelle-Aquitaine region.
- 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_69d8dd01a56c81909694a128c66b21d7 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6a561848190bd957f248471c191 |
completed | April 20, 2026, 7:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a05be4c71588190abb73abc2da57187 |
completed | May 14, 2026, 12:21 p.m. |
| NEDg | Description generation | batch_6a05c0318e84819094180a7edfe3ce86 |
completed | May 14, 2026, 12:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a05c0ccda288190bba6278e3c8f8109 |
completed | May 14, 2026, 12:32 p.m. |
Created at: April 10, 2026, 12:01 p.m.