Triple
T43835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | France |
E861
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object |
Lyon
Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
|
E15889
|
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: Lyon | Statement: [France, majorCity, Lyon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyon Context triple: [France, majorCity, Lyon]
-
A.
Marseille
Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
-
B.
Strasbourg
Strasbourg is a major French city on the Rhine known for hosting key European institutions, including the European Parliament and the Council of Europe.
-
C.
Bordeaux
Bordeaux is a renowned wine-producing region in southwestern France, famous for its prestigious red blends and long winemaking tradition.
-
D.
Paris
Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
-
E.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
- 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: Lyon Triple: [France, majorCity, Lyon]
Generated description
Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lyon Target entity description: Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
-
A.
Marseille
Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
-
B.
Strasbourg
Strasbourg is a major French city on the Rhine known for hosting key European institutions, including the European Parliament and the Council of Europe.
-
C.
Bordeaux
Bordeaux is a renowned wine-producing region in southwestern France, famous for its prestigious red blends and long winemaking tradition.
-
D.
Paris
Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
-
E.
Reims
Reims is a historic city in northeastern France known for its Gothic cathedral, role in French coronations, and significance during both World Wars.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24ae36824819080e8336a3c9f9bf8 |
completed | Feb. 28, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2b01346cc8190bbe82bd00e0bfc05 |
completed | Feb. 28, 2026, 9:06 a.m. |
| NEDg | Description generation | batch_69a2b09fb0d481908e1fa9cf5618ba45 |
completed | Feb. 28, 2026, 9:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2b21936c881909e7bb27b948cc8b8 |
completed | Feb. 28, 2026, 9:15 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.