Input path: /home/debian/html/nutritwin/output_llm/666702abdf080/input.json
Output path: /home/debian/html/nutritwin/output_llm/666702abdf080/output.json
Input text: What proportion of meal is best for a teenager? 3 large meals over a day or a 6 small meals a day?
DB path: __deriveddata__/DerivedObjects/Data/KcalMeDB_fr.sl3
Picto path: __deriveddata__/DerivedObjects/Data/PictoMatcherNetNG_fr.json
Sport grounding path: __deriveddata__/DerivedObjects/Data/DerivedSportMET.json
==================================================================================================================================
Prompt from user: What proportion of meal is best for a teenager? 3 large meals over a day or a 6 small meals a day?
==================================================================================================================================
==================================== Prompt =============================================
Identify in this list of intents: ["Identify food consumption or declaration", "Identify the user physical activity", "Answer a nutrition question", "Other intent"], the intents of the prompt: ###What proportion of meal is best for a teenager? 3 large meals over a day or a 6 small meals a day?###.
Format the result in JSON format: {intents: []}.
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
{
"intents": ["Answer a nutrition question"]
}
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
{
"intents": ["Answer a nutrition question"]
}
```
------------------------------------------------------
------------------------ After simplification ------------------------
{ "intents": ["Answer a nutrition question"]}
----------------------------------------------------------------------
==================================== Prompt =============================================
Convert this natural language query : """What proportion of meal is best for a teenager? 3 large meals over a day or a 6 small meals a day?""" into an array in JSON of consumed foods and beverages.
Provide a solution without explanation.
Use only the ontology described in this Turtle/RDF model:
"""
@prefix food: .
@prefix rdfs: .
@prefix xsd: .
@prefix owl: .
@prefix prov: .
food: a owl:Ontology ;
rdfs:comment "Definition of the food archetype"@en .
food:name a owl:DatatypeProperty;
rdfs:label "name"@en;
rdfs:comment "Food or drink identifier, the name should not contain information related to quantity or container (like glass...). The cooking mode is not in the name. When the brand is very well-known (ex: Activia, Coca-Cola), the name is equal to the brand. Keep the same language"@en;
rdfs:range xsd:string.
food:quantity a owl:DatatypeProperty ;
rdfs:label "quantity"@en;
rdfs:comment "The quantity of food or drink that is or was consumed. Quantity examples in french: 'un quignon', 'un cornet', 'un verre', 'une tranche', 'une boule', 'un', 'deux', 'trois',... Keep the same language."@en;
rdfs:range xsd:string.
food:cookingMethod a owl:DatatypeProperty ;
rdfs:label "cooking method"@en;
rdfs:comment "The cooking method of food. Keep the same language"@en;
rdfs:range xsd:string.
food:type a owl:DatatypeProperty ;
rdfs:label "type of food"@en;
rdfs:comment "Identify the type of food."@en;
rdfs:range xsd:string.
food:food a food:type ;
rdfs:label "food" .
food:beverage a food:type ;
rdfs:label "beverage" .
food:timeOfTheDay a owl:DatatypeProperty ;
rdfs:label "time of the day"@en;
rdfs:comment "Time of the day when food or drink was consumed."@en;
rdfs:range xsd:string.
food:breakfast a food:timeOfTheDay ;
rdfs:label "breakfast" .
food:lunch a food:timeOfTheDay ;
rdfs:label "lunch" .
food:snacking a food:timeOfTheDay ;
rdfs:label "snacking" .
food:dinner a food:timeOfTheDay ;
rdfs:label "dinner" .
food:brand a owl:DatatypeProperty ;
rdfs:label "Brand"@en;
rdfs:comment "Food or beverage brand. The restaurants are not brand. When the 'brand' is not specified and, the food or beverage is very well-known (like 'Coca-Cola'), provide the brand name in 'brand', otherwise set 'brand' to ''."@en;
rdfs:range xsd:string.
food:company a owl:DatatypeProperty ;
rdfs:label "Company"@en;
rdfs:comment "Product company."@en;
rdfs:range xsd:string.
food:enumEvent a rdfs:Class .
food:event a owl:DatatypeProperty ;
rdfs:label "event"@en;
rdfs:comment "Event of eating or drinking. Each must have an event"@en;
rdfs:range food:enumEvent.
food:intent a food:enumEvent ;
rdfs:label "intent" .
rdfs:comment "When the event should happen"@en.
food:declaration a food:enumEvent ;
rdfs:label "declaration" .
rdfs:comment "When the event has already occured"@en.
food:unknownEvent a food:enumEvent ;
rdfs:label "unknown" ;
rdfs:comment "When the event is unknown in the day"@en.
"""
=========================================================================================
------------------------------ LLM Raw response -----------------------------
```json
[
{
"name": "meal",
"quantity": "3",
"cookingMethod": "",
"type": "food",
"timeOfTheDay": "breakfast",
"brand": "",
"company": "",
"event": "intent"
},
{
"name": "meal",
"quantity": "3",
"cookingMethod": "",
"type": "food",
"timeOfTheDay": "lunch",
"brand": "",
"company": "",
"event": "intent"
},
{
"name": "meal",
"quantity": "3",
"cookingMethod": "",
"type": "food",
"timeOfTheDay": "dinner",
"brand": "",
"company": "",
"event": "intent"
},
{
"name": "meal",
"quantity": "6",
"cookingMethod": "",
"type": "food",
"timeOfTheDay": "snacking",
"brand": "",
"company": "",
"event": "intent"
}
]
```
-----------------------------------------------------------------------------
----------------- Make it compliant ------------------
```json
[
{
"name": "meal",
"quantity": "3",
"cookingMethod": "",
"type": "food",
"timeOfTheDay": "breakfast",
"brand": "",
"company": "",
"event": "intent"
},
{
"name": "meal",
"quantity": "3",
"cookingMethod": "",
"type": "food",
"timeOfTheDay": "lunch",
"brand": "",
"company": "",
"event": "intent"
},
{
"name": "meal",
"quantity": "3",
"cookingMethod": "",
"type": "food",
"timeOfTheDay": "dinner",
"brand": "",
"company": "",
"event": "intent"
},
{
"name": "meal",
"quantity": "6",
"cookingMethod": "",
"type": "food",
"timeOfTheDay": "snacking",
"brand": "",
"company": "",
"event": "intent"
}
]
```
------------------------------------------------------
------------------------ After simplification ------------------------
[ { "name": "meal", "quantity": "3", "cookingMethod": "", "type": "food", "timeOfTheDay": "breakfast", "brand": "", "company": "", "event": "intent" }, { "name": "meal", "quantity": "3", "cookingMethod": "", "type": "food", "timeOfTheDay": "lunch", "brand": "", "company": "", "event": "intent" }, { "name": "meal", "quantity": "3", "cookingMethod": "", "type": "food", "timeOfTheDay": "dinner", "brand": "", "company": "", "event": "intent" }, { "name": "meal", "quantity": "6", "cookingMethod": "", "type": "food", "timeOfTheDay": "snacking", "brand": "", "company": "", "event": "intent" }]
----------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': [{'name': 'meal', 'quantity': '3', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': 'breakfast', 'brand': '', 'company': '', 'event': 'intent'}, {'name': 'meal', 'quantity': '3', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': 'lunch', 'brand': '', 'company': '', 'event': 'intent'}, {'name': 'meal', 'quantity': '3', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': 'dinner', 'brand': '', 'company': '', 'event': 'intent'}, {'name': 'meal', 'quantity': '6', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': 'snacking', 'brand': '', 'company': '', 'event': 'intent'}], 'cost': 0.0}
--------------------------------------------------------------------------------
----------- result to be analyzed -----------
{'name': 'meal', 'quantity': '3', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': 'breakfast', 'brand': '', 'company': '', 'event': 'intent'}
First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% meal %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
Second try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormAggr LIKE '% meal %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
Corn Meal - corn meal - - PepsiCo - 0 - 0030000030509 - 0030000030509 - OFF#35f70c74529089823bfe1590c50f3e41
Steam Meal Poulet Légumes Orientaux - steam meal poulet legume orientau - - Delhaize - 0 - 5400119518949 - 5400119518949 - OFF#47622a7fc6cbd80e32e764b32fec7eea
Lovely Meals - lovely meal - - Delhaize - 0 - 5400119512947 - 5400119512947 - OFF#ecaa0cb4d46ac08a832c07cd259aa51d
Sweet Potato Laksa Meal Soup - sweet potato laksa meal soup - - Marks & Spencer - 0 - 00256353 - 00256353 - OFF#91f14054664e4a75164719afbb17e7b7
Vemondo Vegan Ready Meal Lentil Curry - vemondo vegan ready meal lentil curry - - Lidl - 0 - 20934163 - 20934163 - OFF#9d19e6b306bc10adc64584c71e06a8fc
Lamb Shawarma Houmous Meal - lamb shawarma houmou meal - - Marks & Spencer - 0 - 00385008 - 00385008 - OFF#85ead96c91206b4e60e6e936d5687b0b
----------------------------------------------------
----------- result to be analyzed -----------
{'name': 'meal', 'quantity': '3', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': 'lunch', 'brand': '', 'company': '', 'event': 'intent'}
First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% meal %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
Second try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormAggr LIKE '% meal %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
Corn Meal - corn meal - - PepsiCo - 0 - 0030000030509 - 0030000030509 - OFF#35f70c74529089823bfe1590c50f3e41
Steam Meal Poulet Légumes Orientaux - steam meal poulet legume orientau - - Delhaize - 0 - 5400119518949 - 5400119518949 - OFF#47622a7fc6cbd80e32e764b32fec7eea
Lovely Meals - lovely meal - - Delhaize - 0 - 5400119512947 - 5400119512947 - OFF#ecaa0cb4d46ac08a832c07cd259aa51d
Sweet Potato Laksa Meal Soup - sweet potato laksa meal soup - - Marks & Spencer - 0 - 00256353 - 00256353 - OFF#91f14054664e4a75164719afbb17e7b7
Vemondo Vegan Ready Meal Lentil Curry - vemondo vegan ready meal lentil curry - - Lidl - 0 - 20934163 - 20934163 - OFF#9d19e6b306bc10adc64584c71e06a8fc
Lamb Shawarma Houmous Meal - lamb shawarma houmou meal - - Marks & Spencer - 0 - 00385008 - 00385008 - OFF#85ead96c91206b4e60e6e936d5687b0b
----------------------------------------------------
----------- result to be analyzed -----------
{'name': 'meal', 'quantity': '3', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': 'dinner', 'brand': '', 'company': '', 'event': 'intent'}
First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% meal %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
Second try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormAggr LIKE '% meal %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
Corn Meal - corn meal - - PepsiCo - 0 - 0030000030509 - 0030000030509 - OFF#35f70c74529089823bfe1590c50f3e41
Steam Meal Poulet Légumes Orientaux - steam meal poulet legume orientau - - Delhaize - 0 - 5400119518949 - 5400119518949 - OFF#47622a7fc6cbd80e32e764b32fec7eea
Lovely Meals - lovely meal - - Delhaize - 0 - 5400119512947 - 5400119512947 - OFF#ecaa0cb4d46ac08a832c07cd259aa51d
Sweet Potato Laksa Meal Soup - sweet potato laksa meal soup - - Marks & Spencer - 0 - 00256353 - 00256353 - OFF#91f14054664e4a75164719afbb17e7b7
Vemondo Vegan Ready Meal Lentil Curry - vemondo vegan ready meal lentil curry - - Lidl - 0 - 20934163 - 20934163 - OFF#9d19e6b306bc10adc64584c71e06a8fc
Lamb Shawarma Houmous Meal - lamb shawarma houmou meal - - Marks & Spencer - 0 - 00385008 - 00385008 - OFF#85ead96c91206b4e60e6e936d5687b0b
----------------------------------------------------
----------- result to be analyzed -----------
{'name': 'meal', 'quantity': '6', 'cookingMethod': '', 'type': 'food', 'timeOfTheDay': 'snacking', 'brand': '', 'company': '', 'event': 'intent'}
First try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormName LIKE '% meal %' AND (V_NormTrademark = '' OR V_NormTrademark IS NULL)
Second try:
SELECT V_Name,V_Comment,V_NormName,V_NormComment,V_PackType,V_GTIN,V_GTINRef,V_ID,V_GlobalCount,V_NormTrademark,V_Trademark,V_NormAggr FROM KCALME_TABLE WHERE V_NormAggr LIKE '% meal %' AND V_NormTrademark LIKE '%%'
------------- Found solution (max 20) --------------
Corn Meal - corn meal - - PepsiCo - 0 - 0030000030509 - 0030000030509 - OFF#35f70c74529089823bfe1590c50f3e41
Steam Meal Poulet Légumes Orientaux - steam meal poulet legume orientau - - Delhaize - 0 - 5400119518949 - 5400119518949 - OFF#47622a7fc6cbd80e32e764b32fec7eea
Lovely Meals - lovely meal - - Delhaize - 0 - 5400119512947 - 5400119512947 - OFF#ecaa0cb4d46ac08a832c07cd259aa51d
Sweet Potato Laksa Meal Soup - sweet potato laksa meal soup - - Marks & Spencer - 0 - 00256353 - 00256353 - OFF#91f14054664e4a75164719afbb17e7b7
Vemondo Vegan Ready Meal Lentil Curry - vemondo vegan ready meal lentil curry - - Lidl - 0 - 20934163 - 20934163 - OFF#9d19e6b306bc10adc64584c71e06a8fc
Lamb Shawarma Houmous Meal - lamb shawarma houmou meal - - Marks & Spencer - 0 - 00385008 - 00385008 - OFF#85ead96c91206b4e60e6e936d5687b0b
----------------------------------------------------
BOB.w30,BOL.w50
BOB.w30,BOL.w50
BOB.w30,BOL.w50
BOB.w30,BOL.w50
==================================== Prompt =============================================
Here is all known information:
For "Corn Meal", here are the nutrition values:
name: Corn Meal
GTIN: 0030000030509
brand: PepsiCo
calorie: 333.0Kcal per 100ml
reference volume for a unity: 400ml
salt: 0.0g per 100ml
sugar: 0.0g per 100ml
NutriScore: A
EcoScore: B
allergens: none
allergen traces: none
data source: Open Food Facts
For "Corn Meal", here are the nutrition values:
name: Corn Meal
GTIN: 0030000030509
brand: PepsiCo
calorie: 333.0Kcal per 100ml
reference volume for a unity: 400ml
salt: 0.0g per 100ml
sugar: 0.0g per 100ml
NutriScore: A
EcoScore: B
allergens: none
allergen traces: none
data source: Open Food Facts
For "Corn Meal", here are the nutrition values:
name: Corn Meal
GTIN: 0030000030509
brand: PepsiCo
calorie: 333.0Kcal per 100ml
reference volume for a unity: 400ml
salt: 0.0g per 100ml
sugar: 0.0g per 100ml
NutriScore: A
EcoScore: B
allergens: none
allergen traces: none
data source: Open Food Facts
For "Corn Meal", here are the nutrition values:
name: Corn Meal
GTIN: 0030000030509
brand: PepsiCo
calorie: 333.0Kcal per 100ml
reference volume for a unity: 400ml
salt: 0.0g per 100ml
sugar: 0.0g per 100ml
NutriScore: A
EcoScore: B
allergens: none
allergen traces: none
data source: Open Food Facts
Answer in less than 50 words to this question with a short explanation if needed: "What proportion of meal is best for a teenager? 3 large meals over a day or a 6 small meals a day?"
" + "Mention the data source in the response if it exists. The answer must be in the same language than the question
=========================================================================================
------------------------------ LLM Raw response -----------------------------
Para un adolescente, es mejor consumir 6 comidas pequeñas al día en lugar de 3 comidas grandes. Esto ayuda a mantener niveles de energía estables y puede mejorar la concentración y el rendimiento físico.
Fuente de datos: Open Food Facts.
-----------------------------------------------------------------------------
--------------------------------- LLM result -----------------------------------
{'response': 'Para un adolescente, es mejor consumir 6 comidas pequeñas al día en lugar de 3 comidas grandes. Esto ayuda a mantener niveles de energía estables y puede mejorar la concentración y el rendimiento físico. \n\nFuente de datos: Open Food Facts.', 'cost': 0.0}
--------------------------------------------------------------------------------
--------------------------------- final result -----------------------------------
{'prompt': 'What proportion of meal is best for a teenager? 3 large meals over a day or a 6 small meals a day?', 'intents': ['Answer a nutrition question'], 'model': 'gpt-4o-2024-05-13', 'solutions': {'nutrition': [{'name': 'Corn Meal', 'normName': ' corn meal ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#35f70c74529089823bfe1590c50f3e41', 'quantity': '3', 'quantityLem': '3', 'pack': ['BOB.w30', 'BOL.w50'], 'type': 'food', 'gtin': '0030000030509', 'gtinRef': '0030000030509', 'brand': 'PepsiCo', 'time': 'breakfast', 'event': 'intent', 'serving': 'BOL-300', 'posiNormName': 5}, {'name': 'Corn Meal', 'normName': ' corn meal ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#35f70c74529089823bfe1590c50f3e41', 'quantity': '3', 'quantityLem': '3', 'pack': ['BOB.w30', 'BOL.w50'], 'type': 'food', 'gtin': '0030000030509', 'gtinRef': '0030000030509', 'brand': 'PepsiCo', 'time': 'lunch', 'event': 'intent', 'serving': 'BOL-300', 'posiNormName': 5}, {'name': 'Corn Meal', 'normName': ' corn meal ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#35f70c74529089823bfe1590c50f3e41', 'quantity': '3', 'quantityLem': '3', 'pack': ['BOB.w30', 'BOL.w50'], 'type': 'food', 'gtin': '0030000030509', 'gtinRef': '0030000030509', 'brand': 'PepsiCo', 'time': 'dinner', 'event': 'intent', 'serving': 'BOL-300', 'posiNormName': 5}, {'name': 'Corn Meal', 'normName': ' corn meal ', 'comment': '', 'normComment': '', 'rank': 0, 'id': 'OFF#35f70c74529089823bfe1590c50f3e41', 'quantity': '6', 'quantityLem': '6', 'pack': ['BOB.w30', 'BOL.w50'], 'type': 'food', 'gtin': '0030000030509', 'gtinRef': '0030000030509', 'brand': 'PepsiCo', 'time': 'snacking', 'event': 'intent', 'serving': 'BOL-600', 'posiNormName': 5}], 'activity': [], 'response': {'type': 'text', 'data': 'Para un adolescente, es mejor consumir 6 comidas pequeñas al día en lugar de 3 comidas grandes. Esto ayuda a mantener niveles de energía estables y puede mejorar la concentración y el rendimiento físico. \n\nFuente de datos: Open Food Facts.'}}, 'cputime': 8.197281122207642}
----------------------------------------------------------------------------------
LLM CPU Time: 8.197281122207642