Triple
T415163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auvergne-Rhône-Alpes |
E9576
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Lyon |
E15889
|
NE FINISHED |
How this triple was built (2 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: [Auvergne-Rhône-Alpes, containsCity, Lyon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyon Context triple: [Auvergne-Rhône-Alpes, containsCity, Lyon]
-
A.
Lyon
chosen
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.
-
B.
Clermont-Ferrand
Clermont-Ferrand is a central French city known for its historic cathedral built of black volcanic stone and as the longtime headquarters of the tire company Michelin.
-
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.
Saint-Étienne
Saint-Étienne is an industrial city in central France known for its historic manufacturing heritage, football culture, and role as one of the host cities for major international sporting events.
-
E.
Toulouse
Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ee8d835881908403ea23901e52b3 |
completed | Feb. 28, 2026, 1:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a66660d36481909b938cf569337df9 |
completed | March 3, 2026, 4:41 a.m. |
Created at: Feb. 28, 2026, 1:09 p.m.