Image
Caption

An outdoors top-down view of an orange toy basketball, a yellow toy volleyball, and a water football floating in that order from left to right on the surface of a pool within inches of each other in a U-shaped arch. The volleyball is the smallest, with thin indention lines creating the panels of the ball. The orange basketball medium is textured with small dots. The football has an oval-shaped logo with black text upside down that reads, "COOP" on its left side facing the volleyball. The still light blue pool water reflects the overhanging treetops and the light blue sky above. Daytime.

Scene Graph
    Scene Graph
QA + verification programs
Q: Which ball is the smallest and how can you recognize it?
A: The yellow toy volleyball is the smallest, and it has thin indentation lines creating the panels of the ball.
  0 def smallest_ball_description(scene_graph):                                 
  1     entities = scene_graph.get_entities()                                   
  2     for entity in entities:                                                 
  3         if 'volleyball' in entity:                                          
  4             attributes = scene_graph.get_attributes(entity)                 
  5             if attributes.get('size') == 'smallest':                        
  6                 indentations = scene_graph.get_attributes("volleyball's     
    indention lines")                                                           
  7                 return f"The {entity} is {attributes['size']} with features 
    of {indentations.get('shape', 'Unknown features')}."                        
  8     return None                                                             
  9                                                                             

gpt-4o: The smallest ball in the image is the yellow ball in the middle. You can recognize it as the smallest because it has a smaller diameter compared to the orange ball on the left and the elongated shape of the ball on the right.
hscore: 68.66%
• <color (volleyball, yellow)> == <color (yellow ball, yellow)> (80.0%)
• <state (volleyball, toy)> == <state (yellow ball, middle)> (70.0%)
• <whole (volleyball)> == <whole (yellow ball)> (60.0%)
tscore: 73.29%
• <color (orange ball, orange)"> == <color (basketball, orange)> (80.0%)
• <color (yellow ball, yellow)> == <color (volleyball, yellow)> (80.0%)
• <shape (elongated ball, elongated)> == <image> (70.0%)

Q: In which direction are the objects floating on the pool surface arranged?
A: The objects are floating from left to right in a U-shaped arch on the pool surface.
  0 def direction_of_floating_objects(scene_graph):                             
  1     volleyball_relations = scene_graph.get_outgoing_relations('volleyball') 
  2     basketball_relations = scene_graph.get_outgoing_relations('basketball') 
  3     direction = None                                                        
  4     if 'football' in volleyball_relations:                                  
  5         direction = volleyball_relations['football'].get('spatial')         
  6     if 'volleyball' in basketball_relations:                                
  7         direction = basketball_relations['volleyball'].get('spatial')       
  8     return direction                                                        
  9                                                                             

gpt-4o: The objects floating on the pool surface are arranged in a diagonal line from the bottom left to the top right.
hscore: 66.4%
• <spatial (objects, pool surface, on)> == <spatial (objects, pool surface, on)> (100.0%)
• <direction (objects, left to right)> == <direction (diagonal line, from bottom left to top right)"> (60.0%)
• <spatial (objects, U> == <spatial (objects, diagonal line, in)> (60.0%)
tscore: 65.3%
• <spatial (objects, pool surface, on)> == <image> (70.0%)
• <spatial (objects, diagonal line, in)> == <image> (70.0%)
• <direction (diagonal line, from bottom left to top right)"> == <spatial (basketball, volleyball, left to right)> (40.0%)
Image
Caption

An overhead view looking down at brick tiles in the ground. Pieces of dirt and leaves are in between the cracks of the tile. Grass stains are on some of the tiles as well. The color of the tiles are maroon and gray and are running vertically and horizontally on the ground. Black letters are in the middle of the image on a horizontal maroon tile that reads "BASS".

Scene Graph
    Scene Graph
QA + verification programs
Q: What are the predominant colors of the tiles, and how are these colors arranged?
A: The predominant colors of the tiles are maroon and gray, arranged vertically and horizontally.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'tiles' in entity:                                               
  3             attributes = scene_graph.get_attributes(entity)                 
  4             return f"{attributes.get('color', '')}, {attributes.get('state',
    '')}"                                                                       
  5     return None                                                             
  6                                                                             

gpt-4o: The predominant colors of the tiles are red and light gray. The tiles are arranged in an alternating pattern, with red and light gray tiles placed next to each other in a grid-like formation. The red tile in the center has the word "BASS" inscribed on it.
hscore: 80.27%
• <color (tiles, gray)> == <color (tiles, light gray)> (90.0%)
• <color (tiles, maroon)> == <color (tiles, red)> (80.0%)
• <arrangement (tiles, horizontal)> == <state (tiles, arranged in pattern)> (70.0%)
tscore: 84.27%
• <whole (tiles)> == <whole (tiles)> (100.0%)
• <color (tiles, light gray)> == <color (tiles, gray)> (90.0%)
• <text rendering (center tile, \"BASS\")"> == <text rendering (tile, "BASS")> (80.0%)
Image
Caption

An outdoor, daytime, angled-down medium close-up three-quarter left side view of a young, cream-colored labradoodle wearing a transparent protective collar that is cone-shaped around its neck. The dogs fur is white and wavy, and has a thick pink tongue. The labradoodle is looking upward and slightly to its left. The labradoodle has a dark maroon nose and light green eyes. The dog is standing on a weathered down cement ground floor that has small chips and markings all throughout its surface. Behind the dog is a blue child's toy bicycle that is facing towards the left. There is a dark blue helmet hanging from the left handlebar. Behind the bike is a square wooden white support post. Further behind the post are some other toys in the shadows.

Scene Graph
    Scene Graph
QA + verification programs
Q: What color is the dog's tongue, and what could this indicate about its health or mood?
A: The dog's tongue is thick and pink, possibly indicating it is healthy and relaxed.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'tongue' in entity:                                              
  3             color = scene_graph.get_attributes(entity).get('color', "")     
  4             shape = scene_graph.get_attributes(entity).get('shape', "")     
  5             return f'The tongue is {shape} and {color}, suggesting it is    
    healthy and relaxed.'                                                       
  6     return None                                                             
  7                                                                             

gpt-4o: The dog's tongue appears to be pink. A pink tongue generally indicates good health and proper oxygenation. It can also suggest that the dog is in a relaxed or happy mood. However, if the tongue were to appear pale, blue, or purple, it could indicate health issues such as poor oxygenation or circulation, and a veterinarian should be consulted.
hscore: 73.6%
• <color (dog's tongue, pink)> == <color (dog's tongue, pink)> (100.0%)
• <state (dog's tongue, healthy)> == <health (dog's tongue color, good)> (70.0%)
• <state (dog, relaxed)> == <mood (dog's tongue color, relaxed or happy)> (60.0%)
tscore: 85.2%
• <color (dog's tongue, pink)> == <color (dog's tongue, pink)> (100.0%)
• <part (dog's tongue)> == <part (dog's tongue)> (100.0%)
• <warning (dog's tongue color, health issues)"> == <image> (70.0%)

Q: What type of toy is near the dog, and where is it facing?
A: A blue child's toy bicycle is near the dog, facing towards the left.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if "bicycle" in entity:                                             
  3             description = scene_graph.get_attributes(entity).get('color',   
    "") + ' ' + scene_graph.get_attributes(entity).get('type', "") + ", "       
  4             state = scene_graph.get_attributes(entity).get('state', "") +   
    '.'                                                                         
  5             return description + state                                      
  6     return None                                                             
  7                                                                             

gpt-4o: The toy near the dog is a bicycle. It is facing towards the left side of the image.
hscore: 81.98%
• <spatial (child's toy bicycle, dog, near)> == <spatial (bicycle, dog, near)> (90.0%)
• <state (child's toy bicycle, near)> == <state (bicycle, toy)> (90.0%)
• <whole (child's toy bicycle)> == <whole (bicycle)> (80.0%)
tscore: 89.24%
• <whole (bicycle)> == <whole (bicycle)> (100.0%)
• <spatial (bicycle, dog, near)> == <spatial (bicycle, dog, behind)> (90.0%)
• <state (bicycle, facing towards the left side of the image)"> == <state (bicycle, face towards the left)> (90.0%)

Q: What material is the ground where the dog is standing on, and what does it look like?
A: The dog is standing on a weathered-down cement ground floor with small chips and markings.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if "ground floor" in entity:                                        
  3             material = scene_graph.get_attributes(entity).get('material',   
    "")                                                                         
  4             state = scene_graph.get_attributes(entity).get('state', "")     
  5             return f'The ground floor is {state} and made of {material}. '  
  6     return None                                                             
  7                                                                             

gpt-4o: The ground where the dog is standing appears to be made of concrete. It looks like a typical garage or outdoor concrete floor, with a rough, slightly textured surface. There are some small debris and dirt scattered around, which is common for such areas. The concrete has a light gray color and shows some signs of wear and use.
hscore: 63.6%
• <state (cement ground floor, standing on)> == <state (ground, dirt)> (60.0%)
• <whole (cement ground floor)> == <whole (ground)> (60.0%)
• <whole (dog)> == <whole (ground)> (60.0%)
tscore: 70.51%
• <material (ground, concrete)> == <material (ground floor, cement)> (90.0%)
• <state (ground, rough)> == <image> (70.0%)
• <whole (ground)> == <whole (ground floor)> (70.0%)
Image
Caption

An outdoor medium close up shot of a section of a green metal scoreboard where the white vinyl letters, "VISITOR" are visible above the square cut out for the visitor's score. The vinyl of the letters as well as a thin outline of the scoreboard are visibly peeling. The white metal panel above the letters has a sun faded illustration of a red circle and a coke bottle partially visible. A mesh screen is visible in front of the bulbs for the visitor's score. Green trees are visible in the background to the right, reflecting the bright sunlight.

Scene Graph
    Scene Graph
QA + verification programs
Q: What is the appearance of the letters on the scoreboard?
A: The letters on the scoreboard are white vinyl, visibly peeling, and display the word "VISITOR".
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if "letters" in entity:                                             
  3             attributes = scene_graph.get_attributes(entity)                 
  4             if '"VISITOR"' in attributes.get('text rendering', ""):         
  5                 description = f"{attributes.get('color', '')}               
    {attributes.get('material', '')}, visibly {attributes.get('state', '')}, and
    display the word 'VISITOR'"                                                 
  6                 return description                                          
  7     return None                                                             
  8                                                                             

gpt-4o: The letters on the scoreboard spell out "VISITOR" in a bold, white, sans-serif font. The paint on the letters is chipped and worn, giving them a distressed appearance.
hscore: 66.71%
• <text rendering (scoreboard, \VISITOR\)> == <text rendering (scoreboard, \"VISITOR\")> (90.0%)
• <color (scoreboard's letters, white)> == <color (text, white)> (70.0%)
• <part (scoreboard's letters)> == <whole (scoreboard)> (60.0%)
tscore: 80.87%
• <whole (scoreboard)> == <whole (scoreboard)> (100.0%)
• <text rendering (scoreboard, \"VISITOR\")> == <text rendering (scoreboard's letters, "VISITOR")> (90.0%)
• <state (text, distressed)"> == <image> (70.0%)

Q: Describe what is located on the white metal panel?
A: A sun-faded illustration of a red circle and a coke bottle is partially visible on the white metal panel.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if "circle" in entity:                                              
  3             relations_out = scene_graph.get_outgoing_relations(entity)      
  4             if "panel" in relations_out and "on" in                         
    relations_out["panel"].get("spatial", ""):                                  
  5                 attributes = scene_graph.get_attributes(entity)             
  6                 return f"A sun-faded illustration of                        
    {attributes.get('color', 'red')} circle is partially visible on the panel." 
  7     return None                                                             
  8                                                                             

gpt-4o: The white metal panel in the image has the word "VISITOR" written on it in large, white, capital letters. The paint on the letters appears to be chipped and worn in some areas. The panel is part of a larger structure, likely a scoreboard, given the context and the presence of what appear to be display screens or lights below the text.
hscore: 49.71%
• <whole (illustration)> == <whole (panel)> (60.0%)
• <spatial (illustration, metal panel, on)> == <material (panel, metal)> (60.0%)
• <state (illustration, sun> == <state (text, worn)> (50.0%)
tscore: 81.57%
• <whole (panel)> == <whole (panel)> (100.0%)
• <color (panel, white)> == <color (panel, white)> (100.0%)
• <material (panel, metal)> == <material (panel, metal)> (100.0%)

Q: What surrounds the bulbs for the visitor's score?
A: A mesh screen is positioned in front of the bulbs for the visitor's score.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if "mesh screen" in entity:                                         
  3             out_relations = scene_graph.get_outgoing_relations(entity)      
  4             if "bulbs" in out_relations and "in front of" in                
    out_relations["bulbs"].get("spatial", ""):                                  
  5                 return "A mesh screen is positioned in front of the bulbs   
    for the visitor's score."                                                   
  6     return None                                                             
  7                                                                             

gpt-4o: The bulbs for the visitor's score on the scoreboard are surrounded by a protective mesh or grid. This mesh likely serves to protect the bulbs from damage and to ensure visibility.
hscore: 56.03%
• <spatial (mesh screen, bulbs, in front of)> == <spatial (protective mesh or grid, scoreboard's bulbs, around)> (60.0%)
• <whole (bulbs)> == <part (scoreboard's bulbs)> (60.0%)
• <spatial (mesh screen, visitor's score display, in front of)> == <spatial (protective mesh or grid, scoreboard's bulbs, around)> (50.0%)
tscore: 76.1%
• <part (scoreboard)> == <part (scoreboard's section)> (90.0%)
• <state (protective mesh or grid, protective)> == <image> (70.0%)
• <spatial (protective mesh or grid, scoreboard's bulbs, around)> == <image> (70.0%)
Image
Caption

A medium-close-up view of a giant square-shaped boulder sitting on top of a shallow lake that is clear. The water reflects the giant rock, and there are ripples all throughout the lake. Along the front part of the boulder, there is thin water falling. On the left side of the rock, there are white pebbles that are beginning to show up because of the shallow water. Along the near side of the rock, a lot of ripples are beginning to form. Behind the boulder, there is a thin white boulder that is sitting flat on the water. On the right side of the boulder, there is another boulder that is dark gray and is shaped like a rectangle. Behind the lake is a tall wall of stone that is made up of three layers that run horizontally. Growing in front of the wall and to the left are thin bushes.

Scene Graph
    Scene Graph
QA + verification programs
Q: What is the state of the shallow lake?
A: The lake is clear and has ripples forming throughout.
  0 def execute_command(scene_graph):                                           
  1     attributes = scene_graph.get_attributes('lake')                         
  2     state = attributes.get('state', '')                                     
  3     return state                                                            
  4                                                                             

gpt-4o: The shallow lake in the image appears to be in a calm state. The water is clear and still, with no visible signs of turbulence or strong currents. The presence of rocks and the reflection of the surroundings in the water suggest that the lake is relatively undisturbed. The surrounding vegetation and rock formations indicate a natural and serene environment.
hscore: 87.04%
• <whole (lake)> == <whole (lake)> (100.0%)
• <state (lake, has ripples)> == <state (lake, calm)> (80.0%)
• <state (lake, clear)> == <state (lake, calm)> (80.0%)
tscore: 78.48%
• <whole (lake)> == <whole (lake)> (100.0%)
• <spatial (rocks, lake, in)> == <spatial (boulder, lake, on top of)> (80.0%)
• <state (lake, shallow)> == <state (water, shallow)> (80.0%)

Q: What detail can you provide about the boulder on the right side of the giant rock?
A: The boulder on the right side of the giant rock is dark gray and shaped like a rectangle.
   0 def execute_command(scene_graph):                                          
   1     for entity in scene_graph.get_entities():                              
   2         if 'boulder' in entity:                                            
   3             attributes = scene_graph.get_attributes(entity)                
   4             if 'dark gray' in attributes.get('color', '') and 'rectangle'  
     in attributes.get('shape', ''):                                            
   5                 relations = scene_graph.get_incoming_relations(entity)     
   6                 for source_entity in relations:                            
   7                     if 'boulder_1' in source_entity and 'right' in         
     relations[source_entity].get('spatial', ''):                               
   8                         return                                             
     scene_graph.describe(scene_graph.generate_subgraph([entity]))              
   9     return None                                                            
  10                                                                            

gpt-4o: The boulder on the right side of the giant rock in the image appears to be a large, rectangular-shaped rock. It is partially submerged in a shallow body of water, likely a stream or river. The boulder has a rough, textured surface with visible layers and cracks, indicating it may have been shaped by natural erosion processes over time. The water around the boulder is clear, allowing the smaller rocks and pebbles beneath the surface to be seen. The surrounding area includes additional rock formations and some vegetation, such as trees and shrubs, suggesting a natural, possibly forested environment.
hscore: 85.68%
• <shape (boulder, rectangular)> == <shape (boulder, rectangular)> (100.0%)
• <spatial (boulder, giant rock, on the right side)> == <spatial (boulder, giant rock, right side of)> (90.0%)
• <part (boulder)> == <whole (boulder)> (80.0%)
tscore: 78.76%
• <whole (boulder)> == <whole (boulder)> (100.0%)
• <shape (boulder, rectangular)> == <shape (boulder, rectangle)> (90.0%)
• <state (stream or river, shallow)> == <state (water, shallow)> (80.0%)

Q: What relationship does the water have with the giant rock?
A: The water reflects the giant rock and falls from it.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'water' in entity:                                               
  3             out_relations = scene_graph.get_outgoing_relations(entity)      
  4             if 'boulder' in out_relations and 'reflect' in                  
    scene_graph.get_attributes(entity).get('state', ''):                        
  5                 return 'reflects the giant rock and falls from it'          
  6     return None                                                             
  7                                                                             

gpt-4o: The water in the image appears to be interacting with the giant rock in a few ways: 1. **Erosion**: Over time, the water likely contributes to the erosion of the rock, gradually wearing it down and shaping its form. This process can create smooth surfaces and unique formations on the rock. 2. **Support**: The water provides a base for the rock, partially submerging it and possibly supporting its weight. The rock appears to be resting on smaller stones and the water's surface. 3. **Reflection**: The water reflects the image of the rock, creating a mirrored effect that enhances the visual appeal of the scene. 4. **Habitat**: The water around the rock can serve as a habitat for various aquatic plants and animals, contributing to the local ecosystem. Overall, the water and the rock have a dynamic relationship, with the water influencing the rock's physical characteristics and the rock contributing to the water's landscape.
hscore: 77.03%
• <whole (rock)> == <whole (rock)> (100.0%)
• <spatial (water, rock, reflects)> == <spatial (water, rock, interacting)> (80.0%)
• <spatial (water, rock, above)> == <spatial (water, rock, resting on smaller stones)> (80.0%)
tscore: 80.76%
• <whole (rock)> == <whole (rock)> (100.0%)
• <whole (water)> == <whole (water)> (100.0%)
• <(water, rock, reflect)> == <state (water, reflect rock)> (70.0%)
Image
Caption

An outdoor long shot view looking down a long railroad track that is straight, and curves slightly to the left. The railroad track is placed on top of a ground floor made up of white rocks. About twenty-five feet to the left of the railroad track are two gray electrical boxes that are placed side by side, parallel to the railroad track, with about seven feet between them. Behind the electrical boxes is a field made up of dry yellow grass. To the right of the railroad track is a large maroon colored building, and two more multi-colored buildings behind it. Further behind the buildings, and hovering above them, is a large rectangular billboard that is colored blue, and has white letters on it that read "Feeling Stuck?". Further in the distance, to the left and right of the railroad track is a tree line consisting of tall bushy green trees. The sky is partly cloudy, and is a light baby blue color.

Scene Graph
    Scene Graph
QA + verification programs
Q: What color is the billboard hovering above the buildings?
A: The billboard hovering above the buildings is blue.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'billboard' in entity:                                           
  3             attributes = scene_graph.get_attributes(entity)                 
  4             if 'above' in                                                   
    scene_graph.get_outgoing_relations(entity).get('buildings',                 
    {}).get('spatial', ''):                                                     
  5                 return attributes.get('color', '')                          
  6     return None                                                             
  7                                                                             

gpt-4o: The billboard hovering above the buildings is primarily blue with some orange and white text.
hscore: 79.57%
• <color (billboard, blue)> == <color (billboard's background, blue)> (90.0%)
• <spatial (billboard, buildings, above)> == <state (billboard's text, above buildings)"> (60.0%)
tscore: 79.76%
• <whole (billboard)> == <whole (billboard)> (100.0%)
• <color (billboard's background, blue)> == <color (billboard, blue)> (90.0%)
• <whole (billboard's background)> == <whole (billboard)> (80.0%)

Q: How many electrical boxes are there and where are they located?
A: There are two gray electrical boxes parallel to the railroad track, with about seven feet between them and located about twenty-five feet to the left of the rail track.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'electrical boxes' in entity:                                    
  3             description =                                                   
    scene_graph.describe(scene_graph.generate_subgraph([entity]))               
  4             return description                                              
  5     return None                                                             
  6                                                                             

gpt-4o: There are two electrical boxes in the image. They are located to the left side of the railroad tracks, near the edge of the gravel area.
hscore: 73.88%
• <count (electrical boxes, ==2)> == <count (electrical boxes, ==2)> (100.0%)
• <spatial (electrical boxes, railroad track, to the left of)> == <spatial (electrical boxes, railroad tracks, to the left of)> (90.0%)
• <spatial (electrical boxes, railroad track, parallel to)> == <spatial (electrical boxes, railroad tracks, to the left of)> (80.0%)
tscore: 93.87%
• <whole (electrical boxes)> == <whole (electrical boxes)> (100.0%)
• <count (electrical boxes, ==2)> == <count (electrical boxes, ==2)> (100.0%)
• <spatial (electrical boxes, railroad tracks, to the left of)> == <spatial (electrical boxes, railroad track, to the left of)> (90.0%)

Q: What message is displayed on the billboard?
A: The message displayed on the billboard is "Feeling Stuck?"
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'billboard\'s letters' in entity:                                
  3             return scene_graph.get_attributes(entity).get('text rendering', 
    '').strip('"')                                                              
  4     return None                                                             
  5                                                                             

gpt-4o: The billboard displays the message: "Feeling Stuck? FREE College. No Debt."
hscore: 81.08%
• <text rendering (billboard, \Feeling Stuck?\)> == <text rendering (billboard, \"Feeling Stuck? FREE College. No Debt.\")> (80.0%)
tscore: 81.57%
• <whole (billboard)> == <whole (billboard)> (100.0%)
• <text rendering (billboard, \"Feeling Stuck? FREE College. No Debt.\")> == <text rendering (billboard's letters, "Feeling Stuck?")> (80.0%)
• <state (text, display on billboard)"> == <text rendering (billboard's letters, "Feeling Stuck?")> (60.0%)

Q: Can you describe the buildings in this scene?
A: There are three buildings: a large maroon-colored building to the right of the railroad track, and two multi-colored buildings behind it.
  0 def execute_command(scene_graph):                                           
  1     results = []                                                            
  2     for entity in scene_graph.get_entities():                               
  3         if 'building' in entity:                                            
  4             results.append(scene_graph.describe(scene_graph.generate_subgrap
    h([entity])))                                                               
  5     return ' '.join(results)                                                
  6                                                                             

gpt-4o: In the scene, there are a few buildings visible along the right side of the railroad tracks: 1. **Foreground Building**: This building is a single-story structure with a reddish-brown exterior. It has a few windows and appears to be a commercial or industrial building. There is some signage on the building, but the text is not fully legible. 2. **Background Buildings**: Further down the tracks, there are additional buildings that appear to be similar in style and function to the foreground building. They are also single-story structures with various colors and signage. 3. **Billboard**: Above the buildings, there is a large billboard with a blue background and white text. The billboard advertises a service related to education or career assistance. 4. **Utility Structures**: On the left side of the tracks, there are a couple of utility structures, possibly electrical or telecommunications boxes. They are white and have some text on them, indicating their purpose. The scene is set along a railroad track with gravel and a concrete crossing in the foreground. The area around the tracks is relatively open, with some grassy patches and a few trees in the distance.
hscore: 70.43%
• <whole (buildings)> == <whole (buildings)> (100.0%)
• <count (buildings, ==3)> == <count (buildings, > 2)> (90.0%)
• <whole (large building)> == <whole (buildings)> (80.0%)
tscore: 74.53%
• <whole (buildings)> == <whole (buildings)> (100.0%)
• <whole (trees)> == <whole (trees)> (100.0%)
• <color (billboard, blue)> == <color (billboard, blue)> (100.0%)

Q: What lies behind the electrical boxes?
A: Behind the electrical boxes is a field made up of dry yellow grass.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'field' in entity:                                               
  3             if 'electrical boxes' in                                        
    scene_graph.get_outgoing_relations(entity):                                 
  4                 return                                                      
    scene_graph.describe(scene_graph.generate_subgraph([entity]))               
  5     return None                                                             
  6                                                                             

gpt-4o: Behind the electrical boxes in the image, there appears to be a grassy area with some trees and a few buildings in the distance. The area seems to be relatively open and not heavily developed. There are also some vehicles visible further back, indicating a road or parking area nearby.
hscore: 48.25%
• <state (field, dry)> == <state (grassy area, open)> (60.0%)
• <spatial (field, electrical boxes, behind)> == <spatial (grassy area, behind electrical boxes)> (50.0%)
• <whole (field)> == <whole (grassy area)> (50.0%)
tscore: 68.9%
• <spatial (vehicles, grassy area, further back)> == <image> (70.0%)
• <part (image, electrical boxes)> == <whole (electrical boxes)> (70.0%)
• <state (grassy area, not heavily developed)> == <image> (70.0%)
Image
Caption

A low resolution outdoor shot of a tree stump protruding from the grass with an armadillo climbing over the protruding roots towards the camera. The armadillos head is slightly angled towards the left, with its two front legs holding its upper body up over the root. The dark brown bark of the tree is speckled with dry moss growth, with dry yellow grass visible in the foreground to the right. Green grass growth is visible in the background, reflecting the bright sunlight shining down onto it.

Scene Graph
    Scene Graph
QA + verification programs
Q: Where is the dry yellow grass located?
A: The dry yellow grass is visible in the foreground to the right.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         attributes = scene_graph.get_attributes(entity)                     
  3         relations_out = scene_graph.get_outgoing_relations(entity)          
  4         if "tree's grass" in entity and 'foreground' in relations_out:      
  5             return relations_out['foreground'].get('spatial', '')           
  6     return None                                                             
  7                                                                             

gpt-4o: In the image, there is no dry yellow grass visible. The area appears to be covered with green grass and vegetation. The armadillo is standing on a patch of ground near a tree trunk, and the surrounding area is predominantly green.
hscore: 61.94%
• <whole (grass)> == <whole (vegetation)> (80.0%)
• <color (grass, yellow)> == <color (vegetation, green)> (70.0%)
• <state (grass, dry)> == <absence (dry yellow grass, visible)"> (50.0%)
tscore: 79.39%
• <whole (armadillo)> == <whole (armadillo)> (100.0%)
• <whole (tree trunk)> == <whole (tree stump)> (90.0%)
• <state (armadillo, stand)> == <state (armadillo's head, slightly angled towards the left)> (80.0%)

Q: What color is the tree bark in the image?
A: The color of the tree bark is dark brown.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         attributes = scene_graph.get_attributes(entity)                     
  3         if "tree's bark" in entity:                                         
  4             return attributes.get('color', '')                              
  5     return None                                                             
  6                                                                             

gpt-4o: The tree bark in the image appears to be dark brown.
hscore: 92.03%
• <color (tree bark, dark brown)> == <color (tree bark, dark brown)"> (90.0%)
• <whole (tree bark)> == <part (tree bark)> (80.0%)
tscore: 95.96%
• <color (tree bark, dark brown)"> == <color (tree's bark, dark brown)> (90.0%)
• <part (tree bark)> == <part (tree's bark)> (90.0%)

Q: Who is climbing over the protruding roots in the scene?
A: The armadillo is climbing over the protruding roots.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         attributes = scene_graph.get_attributes(entity)                     
  3         if 'armadillo' in entity and 'climb over the protruding roots' in   
    attributes.get('state', ''):                                                
  4             return entity                                                   
  5     return None                                                             
  6                                                                             

gpt-4o: The creature climbing over the protruding roots in the scene is an armadillo.
hscore: 70.53%
• <whole (armadillo)> == <whole (armadillo)> (100.0%)
• <(armadillo, roots, climb over)> == <state (armadillo, climbing)"> (50.0%)
• <state (roots, protruding)> == <spatial (armadillo, roots, over)> (50.0%)
tscore: 83.84%
• <whole (armadillo)> == <whole (armadillo)> (100.0%)
• <state (armadillo, climbing)"> == <state (armadillo, climb towards the camera)> (80.0%)
• <whole (creature)> == <whole (armadillo)> (70.0%)
Image
Caption

An above view of a white cat with black ears and black around its right eye looking up and laying on a blue couch. It has a "House Beautiful" magazine laying over its side and there is a red plaid blanket laying to the right and up against its chest. The magazine has a picture of a living room with a white fireplace on the cover of it. There is sun shining on the left corner of the back of the blue couch and on the beige carpet.

Scene Graph
    Scene Graph
QA + verification programs
Q: Where is the sun shining in the scene?
A: The sun is shining on the left corner of the back of the couch and on the beige carpet.
   0 def execute_command(scene_graph):                                          
   1     for entity in scene_graph.get_entities():                              
   2         if 'sun' in entity:                                                
   3             attrs = scene_graph.get_outgoing_relations(entity)             
   4             regions = []                                                   
   5             for region, spatial_info in attrs.items():                     
   6                 if 'on' in spatial_info['spatial']:                        
   7                     regions.append(region)                                 
   8             return ', '.join(regions)                                      
   9     return None                                                            
  10                                                                            

gpt-4o: In the scene, the sun is not shining. The lighting appears to be artificial, likely from an indoor light source, as the overall ambiance is dim and there are no visible shadows or bright sunlight.
hscore: 36.84%
• <state (sun, shining)> == <state (sun, not shining)> (90.0%)
• <spatial (sun, left corner of the back of the couch, on)> == <state (lighting, indoor)> (40.0%)
• <spatial (sun, carpet, on)> == <state (sun, not shining)> (40.0%)
tscore: 60.19%
• <state (sun, not shining)> == <state (sun, shine)> (80.0%)
• <state (sunlight, none visible)"> == <state (sun, shine)> (70.0%)
• <state (lighting, artificial)> == <state (sun, shine)> (50.0%)

Q: Which item is located to the right and up against the cat's chest?
A: A red plaid blanket is up against the cat's chest on the right.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'chest' in entity:                                               
  3             for obj, rel in                                                 
    scene_graph.get_incoming_relations(entity).items():                         
  4                 if 'blanket' in obj and 'up against' in rel['spatial']:     
  5                     attributes = scene_graph.get_attributes(obj)            
  6                     return f"A {attributes.get('color', '')}                
    {attributes.get('texture', '')} blanket"                                    
  7     return None                                                             
  8                                                                             

gpt-4o: The item located to the right and up against the cat's chest is a plaid blanket.
hscore: 78.33%
• <whole (blanket)> == <whole (plaid blanket)> (90.0%)
• <spatial (blanket, cat's chest, on)> == <spatial (plaid blanket, cat, right and up against the chest of)> (80.0%)
• <spatial (blanket, cat, right of)> == <spatial (plaid blanket, cat, right and up against the chest of)> (80.0%)
tscore: 82.08%
• <whole (plaid blanket)> == <whole (blanket)> (90.0%)
• <spatial (plaid blanket, cat, right and up against the chest of)> == <spatial (blanket, chest, up against)> (80.0%)
• <state (plaid blanket, located)> == <image> (70.0%)

Q: What kind of magazine is on the cat's side?
A: The magazine is titled 'House Beautiful'.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'magazine' in entity:                                            
  3             return scene_graph.get_attributes(entity).get('text rendering', 
    '')                                                                         
  4     return None                                                             
  5                                                                             

gpt-4o: The magazine next to the cat is "House Beautiful." It appears to be an issue focused on color, as indicated by the text on the cover. "House Beautiful" is a magazine that typically features home decor, interior design, and lifestyle content.
hscore: 88.54%
• <text rendering (magazine, 'House Beautiful')> == <text rendering (magazine's cover, text)> (80.0%)
tscore: 74.44%
• <whole (cat)> == <whole (cat)> (100.0%)
• <whole (magazine)> == <whole (magazine)> (100.0%)
• <text rendering (magazine's cover, text)> == <text rendering (magazine, "House Beautiful")> (80.0%)
Image
Caption

A top down view of a kids pencil drawing on a green colored surface , there is light shining on the left side of the green surface. The drawing is of a human head with abnormally large eyes and smiling mouth that is represented by a crooked single line, the person hair is made up of nine single strands. Underneath the human head are scribbles and rough, sketchy lines also drawn with a pencil.

Scene Graph
    Scene Graph
QA + verification programs
Q: What can you say about the scribbles beneath the human head?
A: The scribbles beneath the human head are rough and drawn with a pencil.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'scribbles' in entity:                                           
  3             attributes = scene_graph.get_attributes(entity)                 
  4             if 'rough' in attributes.get('state', ''):                      
  5                 return                                                      
    scene_graph.describe(scene_graph.generate_subgraph([entity]))               
  6     return None                                                             
  7                                                                             

gpt-4o: The scribbles beneath the human head in the drawing appear to represent the body and possibly the arms of the figure. The lines are irregular and overlapping, which is typical in children's drawings. The overall style suggests a playful and imaginative approach to depicting a person.
hscore: 77.03%
• <state (scribbles, rough)> == <state (scribbles, irregular)> (80.0%)
• <state (scribbles, drawn)> == <state (scribbles, irregular)> (80.0%)
• <material (scribbles, pencil)> == <spatial (scribbles, human head, beneath)> (60.0%)
tscore: 77.44%
• <spatial (scribbles, human head, beneath)> == <spatial (scribbles, human head, below)> (90.0%)
• <state (scribbles, irregular)> == <state (scribbles, rough)> (80.0%)
• <part (human head)> == <part (human head's hair)> (80.0%)

Q: What attribute distinguishes the eyes in the human head drawing?
A: The eyes in the human head drawing are abnormally large.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if "human head's eyes" in entity:                                   
  3             attributes = scene_graph.get_attributes(entity)                 
  4             return attributes.get('state', '')                              
  5     return None                                                             
  6                                                                             

gpt-4o: The eyes in the drawing are distinguished by their large size relative to the head. They are also depicted with circular shapes inside, possibly representing pupils, and are positioned prominently in the center of the face.
hscore: 74.43%
• <part (human head drawing's eyes)> == <part (eyes)> (70.0%)
• <state (human head drawing's eyes, abnormally large)> == <state (eyes, large size)> (70.0%)
tscore: 77.25%
• <state (eyes, large size)> == <state (human head's eyes, abnormally large)> (80.0%)
• <part (eyes)> == <part (human head's eyes)> (80.0%)
• <relation (eyes, head, relative size)> == <image> (70.0%)
Image
Caption

An outdoor wide-angled view of a large tree and glass Darrell K. Royal Texas Memorial Stadium at the University of Texas Austin with tall bushy trees covering the lower sections in the foreground. The upper right side of the stadium has extended pillars holding a horizontal concrete bar for extra lighting. The far left side of the building is bright as the bright sunshine shines down onto the hard surfaces. The sky is blue but mostly covered by large stratocumulus clouds. An asphalt road leads to a bridge in the lower left corner through the tree line. The central section of the stadium partially visible behind the tree line is mostly covered with glass windows.

Scene Graph
    Scene Graph
QA + verification programs
Q: What is covering the lower sections of the foreground?
A: Tall bushy trees cover the lower sections of the foreground.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'lower sections' in entity:                                      
  3             relations_in = scene_graph.get_incoming_relations(entity)       
  4             for source_entity in relations_in:                              
  5                 if 'cover' in relations_in[source_entity].get('spatial',    
    ''):                                                                        
  6                     return                                                  
    scene_graph.get_attributes(source_entity).get('texture', '') + ' ' +        
    source_entity                                                               
  7     return None                                                             
  8                                                                             

gpt-4o: The lower sections of the foreground in the image are covered by trees and other greenery. The trees obscure part of the view of the building behind them.
hscore: 84.93%
• <spatial (trees, foreground, cover lower sections of)> == <spatial (trees, foreground, cover)> (90.0%)
• <state (trees, bushy)> == <state (trees, greenery)> (80.0%)
• <whole (trees)> == <part (trees)> (80.0%)
tscore: 78.28%
• <spatial (trees, foreground, cover)> == <spatial (trees, foreground, in)> (90.0%)
• <part (trees)> == <whole (trees)> (80.0%)
• <spatial (trees, building, obscure)> == <spatial (trees, foreground, in)> (70.0%)

Q: What supports the concrete bar on the right side of the stadium?
A: The extended pillars support the horizontal concrete bar on the right side of the stadium.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if "stadium's horizontal concrete bar" in entity:                   
  3             relations_in = scene_graph.get_incoming_relations(entity)       
  4             for relation_entity, relation_info in relations_in.items():     
  5                 if 'hold' in relation_info.get('spatial', ''):              
  6                     return                                                  
    scene_graph.get_attributes(relation_entity).get('state', '') + ' ' +        
    relation_entity                                                             
  7     return None                                                             
  8                                                                             

gpt-4o: The concrete bar on the right side of the stadium is supported by a series of vertical columns. These columns are evenly spaced and extend from the ground up to the concrete bar, providing structural support and stability.
hscore: 70.76%
• <part (horizontal concrete bar)> == <part (concrete bar)> (90.0%)
• <spatial (pillars, horizontal concrete bar, support)> == <spatial (vertical columns, concrete bar, up to)> (80.0%)
• <spatial (horizontal concrete bar, stadium, on the right side of)> == <location (concrete bar, right side of the stadium)> (70.0%)
tscore: 67.59%
• <whole (stadium)> == <whole (stadium)> (100.0%)
• <part (concrete bar)> == <part (stadium's horizontal concrete bar)> (80.0%)
• <function (vertical columns, structural support)> == <image> (70.0%)

Q: How does the road interact with the bridge?
A: The asphalt road leads to the bridge.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'road' in entity:                                                
  3             relations_out = scene_graph.get_outgoing_relations(entity)      
  4             for target_entity, relation_info in relations_out.items():      
  5                 if 'lead to' in relation_info.get('spatial', ''):           
  6                     return                                                  
    scene_graph.get_attributes(entity).get('material', '') + ' road leads to ' +
    target_entity                                                               
  7     return None                                                             
  8                                                                             

gpt-4o: In the image, there is no visible bridge interacting with the road. The road appears to curve around the area in front of the large stadium structure. The road is bordered by trees and landscaped areas, and it seems to provide access to the stadium. There is no indication of a bridge in the immediate vicinity of the road in this image.
hscore: 59.21%
• <spatial (road, bridge, leads to)> == <spatial (road, trees, bordered by)> (70.0%)
• <material (road, asphalt)> == <state (road, curve)> (40.0%)
tscore: 77.02%
• <whole (road)> == <whole (road)> (100.0%)
• <whole (stadium structure)> == <whole (stadium)> (80.0%)
• <spatial (road, stadium structure, in front of)> == <spatial (stadium, tree line, behind)> (80.0%)

Q: Describe the sky in the scene.
A: The sky is blue but mostly covered by large stratocumulus clouds.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'sky' in entity:                                                 
  3             return                                                          
    scene_graph.describe(scene_graph.generate_subgraph([entity]))               
  4     return None                                                             
  5                                                                             

gpt-4o: The sky in the scene is partly cloudy with a mix of light and darker clouds. There are patches of blue sky visible, particularly towards the right side of the image. The lighting suggests it might be early morning or late afternoon, as the clouds and the stadium are softly illuminated.
hscore: 66%
• <state (sky, blue)> == <state (sky, visible blue patches)> (80.0%)
• <state (sky, covered)> == <state (sky, partly cloudy)> (70.0%)
• <spatial (stratocumulus clouds, sky, mostly cover)> == <state (sky, mixed light and dark clouds)> (60.0%)
tscore: 81%
• <whole (stadium)> == <whole (stadium)> (100.0%)
• <whole (sky)> == <whole (sky)> (100.0%)
• <state (sky, visible blue patches)> == <image> (70.0%)

Q: What material is in the central section of the stadium?
A: The central section of the stadium is mostly covered with glass windows.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'central section of the stadium' in entity:                      
  3             return scene_graph.get_attributes(entity).get('material', '') + 
    ' windows'                                                                  
  4     return None                                                             
  5                                                                             

gpt-4o: The central section of the stadium appears to be made of concrete and metal. The structure includes large concrete pillars and beams, with metal railings and supports visible in the upper sections. The design is typical of many modern stadiums, which use these materials for their durability and strength.
hscore: 78.55%
• <material (central section of the stadium, glass)> == <material (stadium's central section, metal)> (80.0%)
• <state (central section of the stadium, covered with glass windows)> == <part (stadium's central section)> (60.0%)
tscore: 88.46%
• <whole (stadium)> == <whole (stadium)> (100.0%)
• <part (stadium's central section)> == <part (central section of the stadium)> (90.0%)
• <part (stadium's central section, pillars)> == <part (stadium's pillars)> (90.0%)

Q: What is visible on the far left side of the building in bright sunshine?
A: The far left side of the building is bright as the sunshine shines down onto the hard surfaces.
  0 def execute_command(scene_graph):                                           
  1     for entity in scene_graph.get_entities():                               
  2         if 'left side of the building' in entity:                           
  3             return                                                          
    scene_graph.describe(scene_graph.generate_subgraph([entity]))               
  4     return None                                                             
  5                                                                             

gpt-4o: On the far left side of the building in bright sunshine, you can see a section of the stadium with cylindrical structures and large windows. The sunlight is illuminating this part of the building, making it stand out against the rest of the structure.
hscore: 62.98%
• <spatial (building, left side, far)> == <spatial (building's cylindrical structures and large windows, building, on the far left side)"> (80.0%)
• <state (hard surfaces, bright)> == <state (sunlight, bright)> (70.0%)
• <state (sunshine, shines down)> == <state (sunlight, bright)> (60.0%)
tscore: 77.01%
• <whole (building)> == <whole (building)> (100.0%)
• <spatial (sunlight, building, on)> == <spatial (sunshine, building, shine down onto)> (90.0%)
• <state (sunlight, bright)> == <state (sunshine, bright)> (80.0%)