Triple
T415164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auvergne-Rhône-Alpes |
E9576
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Grenoble
Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
|
E91863
|
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: Grenoble | Statement: [Auvergne-Rhône-Alpes, containsCity, Grenoble]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grenoble Context triple: [Auvergne-Rhône-Alpes, containsCity, Grenoble]
-
A.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
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.
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.
-
D.
Évian-les-Bains
Évian-les-Bains is a French spa and resort town in the Alps renowned worldwide for its mineral water and scenic lakeside setting.
-
E.
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.
- 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: Grenoble Triple: [Auvergne-Rhône-Alpes, containsCity, Grenoble]
Generated description
Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Grenoble Target entity description: Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
-
A.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
-
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.
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.
-
D.
Évian-les-Bains
Évian-les-Bains is a French spa and resort town in the Alps renowned worldwide for its mineral water and scenic lakeside setting.
-
E.
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.
- 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_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_69a66d8a3b408190a3851c79aee5e972 |
completed | March 3, 2026, 5:11 a.m. |
| NEDg | Description generation | batch_69a66df8ed688190863df45bc46ef4ba |
completed | March 3, 2026, 5:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a66e9e2e1c81909591b01ee1984909 |
completed | March 3, 2026, 5:16 a.m. |
Created at: Feb. 28, 2026, 1:09 p.m.