Triple
T22263063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auvergne-Rhône-Alpes |
E550277
|
entity |
| Predicate | hasDepartment |
P35
|
FINISHED |
| Object |
Allier
Allier is a rural department in central France known for its historic towns, spa resorts, and gently rolling countryside.
|
E1527724
|
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: Allier | Statement: [Auvergne-Rhône-Alpes, hasDepartment, Allier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allier Context triple: [Auvergne-Rhône-Alpes, hasDepartment, Allier]
-
A.
Allier
Allier is a major river in central France that flows northward through the Massif Central before joining the Loire.
-
B.
Arpitanie
Arpitanie is a cultural and linguistic region in parts of France, Switzerland, and Italy where the Arpitan (Franco-Provençal) language and related traditions are historically rooted.
-
C.
L’Union
L’Union is a French regional newspaper known for covering local and national news in the Champagne-Ardenne and surrounding areas.
-
D.
L’Union
L’Union is a suburban commune in southwestern France, located just northeast of Toulouse and integrated into its metropolitan area.
-
E.
Franca
Franca is a city in the northeastern part of the Brazilian state of São Paulo, known historically for its leather and footwear industry.
- 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: Allier Triple: [Auvergne-Rhône-Alpes, hasDepartment, Allier]
Generated description
Allier is a rural department in central France known for its historic towns, spa resorts, and gently rolling countryside.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Allier Target entity description: Allier is a rural department in central France known for its historic towns, spa resorts, and gently rolling countryside.
-
A.
Allier
Allier is a major river in central France that flows northward through the Massif Central before joining the Loire.
-
B.
Arpitanie
Arpitanie is a cultural and linguistic region in parts of France, Switzerland, and Italy where the Arpitan (Franco-Provençal) language and related traditions are historically rooted.
-
C.
L’Union
L’Union is a French regional newspaper known for covering local and national news in the Champagne-Ardenne and surrounding areas.
-
D.
L’Union
L’Union is a suburban commune in southwestern France, located just northeast of Toulouse and integrated into its metropolitan area.
-
E.
Franca
Franca is a city in the northeastern part of the Brazilian state of São Paulo, known historically for its leather and footwear industry.
- 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_69e11e42adb8819087714772ea606709 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f141b94a688190b17c55477993a745 |
completed | April 28, 2026, 11:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0ab66c7f248190be0b13342f843eb8 |
completed | May 18, 2026, 6:49 a.m. |
| NEDg | Description generation | batch_6a0ab7b53aa881908227842536d7e8cc |
completed | May 18, 2026, 6:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ab82b5a988190a55dd87f10664821 |
completed | May 18, 2026, 6:56 a.m. |
Created at: April 16, 2026, 8:39 p.m.