Triple
T1352620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brittany |
E28915
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Vannes
Vannes is a historic coastal city in northwestern France known for its well-preserved medieval old town and harbor on the Gulf of Morbihan.
|
E162998
|
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: Vannes | Statement: [Brittany, hasMajorCity, Vannes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vannes Context triple: [Brittany, hasMajorCity, Vannes]
-
A.
Quimper
Quimper is a historic city in western France known for its medieval old town, Gothic cathedral, and traditional Breton culture.
-
B.
Rennes
Rennes is the capital city of France’s Brittany region, known for its historic medieval center, vibrant student population, and role as a major cultural and economic hub in western France.
-
C.
Nantes
Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
-
D.
Lorient
Lorient is a port city in the Brittany region of northwestern France, known for its maritime heritage and annual Interceltic Festival.
-
E.
Villeneuve d’Ascq
Villeneuve d’Ascq is a suburban city in northern France near Lille, known for its universities, technology parks, and modernist urban planning.
- 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: Vannes Triple: [Brittany, hasMajorCity, Vannes]
Generated description
Vannes is a historic coastal city in northwestern France known for its well-preserved medieval old town and harbor on the Gulf of Morbihan.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vannes Target entity description: Vannes is a historic coastal city in northwestern France known for its well-preserved medieval old town and harbor on the Gulf of Morbihan.
-
A.
Quimper
Quimper is a historic city in western France known for its medieval old town, Gothic cathedral, and traditional Breton culture.
-
B.
Rennes
Rennes is the capital city of France’s Brittany region, known for its historic medieval center, vibrant student population, and role as a major cultural and economic hub in western France.
-
C.
Nantes
Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
-
D.
Lorient
Lorient is a port city in the Brittany region of northwestern France, known for its maritime heritage and annual Interceltic Festival.
-
E.
Villeneuve d’Ascq
Villeneuve d’Ascq is a suburban city in northern France near Lille, known for its universities, technology parks, and modernist urban planning.
- 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_69a498571d248190a0ac9eb02d97097f |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c26d0c4481908fddda89242a57b3 |
completed | March 1, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad014e33488190b50469c727b32639 |
completed | March 8, 2026, 4:55 a.m. |
| NEDg | Description generation | batch_69ad0216e9988190beb96b9d85e2dfce |
completed | March 8, 2026, 4:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad026fc2cc81908fa03bfa6e17c391 |
completed | March 8, 2026, 5 a.m. |
Created at: March 1, 2026, 7:56 p.m.