Entity Types Reference
A market tick, a neuron, a protein structure, a network flow, a denoised speech segment, an abstract-reasoning grid — every one of these compresses to the same temporal-semantic representation and is queried by the same primitives (regime detection, pattern replay, cross-domain correlation). The breadth catalogued below is not a set of separate products: it is one coupling-intelligence engine proving that behavioral intelligence is shape, and shape does not care what the data is about. The same engine that detects market regimes also denoises speech and embeds protein structures.
Info
Entity types tell the coupling-intelligence engine how to compress and index your data — each carries compression sensitivity tuned to its data characteristics. Use the type that best matches your source. This page covers the major live families; the catalog spans 200+ entity types across 25+ domains and grows continuously. Type names are case-sensitive and round-trip exactly: copy any name below verbatim into entity_type and it deserializes to that type. Unrecognized names deserialize to OTHER (never an error) rather than the type you intended — so use the exact strings shown here.
Core Types
| Type Name |
Description |
OTHER |
Default type for unclassified temporal vectors. Any unrecognized entity_type string also deserializes here — zero data loss across version boundaries. |
Autonomous Vehicles & Mobility
| Type Name |
Description |
VOXEL_OCCUPANCY |
Occupancy grid cells with occupancy + semantic class (LiDAR/3D occupancy grids) |
TRACKED_OBJECT |
Tracked 3D bounding boxes — pedestrians, vehicles, cyclists, radar-tracked objects |
MAP_ELEMENT |
HD map features — lane geometry, signs, signals, road boundaries |
SENSOR_FRAME |
Full sensor frame embeddings (camera, multi-modal) for scene retrieval |
Film & VFX
| Type Name |
Description |
MOCAP_MARKER |
Motion capture marker or skeletal joint position vectors (120-240Hz) |
VIRTUAL_CAMERA |
Virtual camera path — position, rotation, focal length, aperture |
VFX_ELEMENT |
VFX element state — particle system, fluid sim volume, light source |
Aerial & Defense
| Type Name |
Description |
AIRCRAFT |
Aircraft and unmanned-aerial-vehicle state vectors (ADS-B: position, heading, velocity) |
VESSEL |
Maritime vessel tracking vectors (AIS) |
SATELLITE |
Satellite position and telemetry vectors |
3D Scenes
| Type Name |
Description |
GAUSSIAN_STATIC |
Static environment Gaussians — buildings, terrain, sky |
GAUSSIAN_DYNAMIC |
Moving-object Gaussians — people, vehicles |
GAUSSIAN_TRANSIENT |
Ephemeral Gaussians — particles, lighting effects |
ANCHOR_POINT |
Spatial hierarchy anchor point for scene alignment |
Industrial & IoT
| Type Name |
Description |
SENSOR_READING |
Generic IoT / wearable sensor telemetry — temperature, pressure, vibration, biometric, motion (8-dim) |
DEVICE_STATE |
Device status for robotic arms, warehouse robots, and field devices — online/offline, firmware, battery, signal (8-dim) |
TRACKED_OBJECT |
Tracked 3D pose for mobile robots and end-effectors |
Networking & CDN
| Type Name |
Description |
NETWORK_FLOW |
Network traffic flow vectors (NetFlow/sFlow/IPFIX) |
ROUTE_STATE |
Network routing table state vectors |
LINK_METRIC |
Network link performance metrics (latency, throughput, loss) |
CDN_EDGE |
CDN edge node behavioral state vectors |
DNS_QUERY |
DNS query pattern and resolution vectors |
Financial Markets
| Type Name |
Description |
MARKET_TICK |
Market tick data vectors (price, volume, spread) |
ORDER_FLOW |
Order flow imbalance and execution vectors |
PORTFOLIO_STATE |
Portfolio composition and risk state vectors |
MARKET_REGIME |
Market regime classification state vectors |
Bio-Computing & Neural Interfaces
| Type Name |
Description |
NEURAL_UNIT |
Individual neuron or neural unit firing pattern vectors |
POPULATION_STATE |
Neural population ensemble state vectors |
STIMULATION_EVENT |
Electrical/optical stimulation parameter vectors |
ORGANOID_STATE |
Brain organoid developmental state vectors |
GAME_STATE |
Behavioral game/task performance state vectors |
CLOSED_LOOP_CYCLE |
Closed-loop stimulation-response cycle vectors |
Gaming & NPC AI
| Type Name |
Description |
PLAYER_STATE |
Player behavioral state — position, actions, attention, performance |
GAME_ENTITY |
Game entity (NPC, projectile, interactive object) with state vector |
WORLD_REGION |
Game world region — chunk occupancy, resource state, weather |
NPC_AGENT |
NPC state with OCEAN personality, emotional state, and neural output (32-dim) |
NPC_FACTION |
Faction-level state: cohesion, resources, power structure |
SOCIAL_BOND |
Relationship state between two NPCs: trust, familiarity, loyalty |
IoT & Sensors
| Type Name |
Description |
SENSOR_READING |
Sensor measurement with signal quality and metadata (8-dim) |
DEVICE_STATE |
Device status: online/offline, firmware, battery, signal strength (8-dim) |
TELEMETRY_AGGREGATE |
Aggregated telemetry summary per gateway or zone (8-dim) |
ACTUATOR_COMMAND |
Valve, motor, relay control commands (4-dim) |
DEVICE_ALERT |
Threshold breach, device failure, geofence violation (4-dim) |
Social Dynamics
| Type Name |
Description |
SOCIAL_AGENT |
Individual social agent with personality, emotions, and social drives (48-dim) |
SOCIAL_GROUP |
Group-level health, resources, power structure, culture (24-dim) |
SOCIAL_INTERACTION |
Pairwise interaction with outcome and context (12-dim) |
CULTURAL_MEME |
Cultural norm or strategy with spread and fitness metrics (8-dim) |
SOCIAL_PRESSURE |
Environmental or resource pressure on social dynamics (8-dim) |
Geospatial
| Type Name |
Description |
AIRCRAFT |
ADS-B aircraft position: lat, lon, altitude, speed, heading (8-dim) |
VESSEL |
AIS vessel tracking: lat, lon, speed, course, heading (8-dim) |
SATELLITE |
Orbital element state: lat, lon, altitude, velocity, inclination (8-dim) |
WEATHER_STATION |
Weather observation: temperature, humidity, pressure, wind (8-dim) |
EARTHQUAKE |
Seismic event: lat, lon, depth, magnitude (8-dim) |
FIRE |
Fire detection: lat, lon, area, intensity, spread rate (8-dim) |
CYBER_ATTACK |
Cyber attack event vectors with type, severity, and source indicators |
TRANSIT_VEHICLE |
Public transit position: lat, lon, speed, heading, route (8-dim) |
Genomics & DNA
| Type Name |
Description |
GENOMIC_REGION |
Genomic region embedding from DNA foundation models (gene, promoter, enhancer, variant locus) |
GENE_EXPRESSION |
Gene expression level over time (bulk RNA-seq, single-cell) |
VARIANT_CALL |
Variant call with effect prediction (SNP, indel, structural variant) |
CELL_STATE |
Cell state embedding (single-cell transcriptomics, epigenetic state) |
Bio-Foundation Models & Proteins
The same engine that detects market regimes embeds protein structures. Structure, sequence, and model-weight artifacts from AlphaFold and ESM3 ingest natively — each summarised into the coupling matrix so structural and sequence neighborhoods emerge from the same primitives every other domain uses. Adapters: alphafold, alphafold_code, alphafold_weights, esm3_sequence, esm3_weights, fasta.
| Type Name | Description |
|---|
ALPHA_FOLD_STRUCTURE | AlphaFold-predicted protein structure — backbone coordinates, confidence, sequence (raw .cif/.pdb travels as payload) |
ALPHA_FOLD_CODE | Source file from the AlphaFold code tree, summarised as an AST-shape feature |
ALPHA_FOLD_WEIGHTS | Chunk of an AlphaFold model-weight bundle with tensor-shape signature |
ESM3_SEQUENCE | Protein / training-corpus sequence record (amino-acid composition feature) |
ESM3_WEIGHTS | Chunk of an ESM3 / ESMC model-weight bundle with tensor-shape signature |
Audio, Speech & Language
The same engine that detects market regimes also denoises speech. Audio segments, speech units, and language-corpus records ingest as temporal-semantic vectors — clean/noise/mixed pairs drive speech denoising, and word, grammar, and dictionary embeddings carry linguistic structure into the coupling matrix. Adapter: rosetta.
| Type Name | Description |
|---|
AUDIO_CLEAN_SPEECH | Clean speech audio segment |
AUDIO_NOISE | Noise audio segment |
AUDIO_MIXED | Mixed audio segment (clean + noise) for denoising |
AUDIO_DENOISED | Denoised audio output |
SPEECH_UNIT | Phoneme or speech-unit embedding |
WORD_VECTOR | Word vector embedding (word2vec / GloVe / fastText) |
GRAMMAR_TREEBANK | Grammar treebank parse tree (constituency / dependency) |
DICTIONARY_ENTRY | Dictionary entry — lexicon, definitions, morphology |
LANGUAGE_CORPUS | Language corpus metadata — source, size, coverage |
Abstract Reasoning Grids
The substrate ingests and analyzes abstract-reasoning puzzles — ARC-style colour-grid frames, full puzzle records, and candidate solution grids — encoding each grid's spatial and colour structure into the same coupling matrix that indexes every other domain. This surfaces grid structure for analysis through the universal primitives; it is an ingestion-and-analysis pathway, not a benchmark result. Adapter: arc_grid.
| Type Name | Description |
|---|
ARC_GRID | A single abstract-reasoning grid frame (2-D integer colour cells) with row/column dimensions |
ARC_PUZZLE | A full puzzle record bundling training pairs with test inputs |
ARC_SOLUTION | A candidate or accepted solution grid for a puzzle's test input |
Containers & Inventory
Hierarchical container system for tracking items with full temporal history.
| Entity Type | Description |
|---|
CONTAINER | Logical container (backpack, chest, vault) with typed slots |
CONTAINER_SLOT | Typed slot within a container (weapon slot, potion slot) |
INVENTORY_ITEM | Item tracked within a container with quantity, weight, durability |
CONTAINER_MANIFEST | Snapshot of container contents at a point in time |
Cross-Tenancy Portals
World partitioning and portal-based cross-tenant access.
| Entity Type | Description |
|---|
WORLD | Logical world partition within a tenant |
PORTAL | Bridge connecting two worlds with action-scoped access |
PORTAL_TOKEN | HMAC-signed traversal token with TTL |
CROSS_TENANT_REF | Reference to an entity in another tenant/world |
BCI Cognitive Layer
Brain-computer interface signal processing and cognitive state tracking.
| Entity Type | Description |
|---|
BCI_SESSION | BCI recording/processing session with device metadata |
BCI_SIGNAL_FRAME | Multi-channel EEG signal frame with quality metrics |
BCI_DECODING | Decoded intention from BCI signals with confidence |
BCI_CONTROL | Control command sent via BCI to a target entity |
BCI_ADAPTATION_STATE | Decoder adaptation tracking (accuracy, ITR, calibration) |
BCI_ARTIFACT | Signal artifact detection (eye blink, muscle, electrode) |
Consent & Safety
Consent-gated modification tracking for BCI and neural interfaces.
| Entity Type | Description |
|---|
CONSENT_CONTRACT | Explicit informed consent record between subject, grantor, and operator |
CONSENT_REVOCATION | Revocation event with revert preference |
STEERING_BOUNDS | Bounds defining permitted neural modification |
Skill Distillation
Neural skill pattern extraction, validation, and transfer.
| Entity Type | Description |
|---|
SKILL_PACKAGE | Distilled, validated skill pattern with safety assessment |
SKILL_TRANSFER | Active or completed transfer session with feedback |
SKILL_FEEDBACK | Subject feedback during skill integration |
DISTILLATION_JOB | Distillation pipeline job tracking |
Immersive Reality
VR/AR sensory event tracking and spatial queries.
| Entity Type | Description |
|---|
HAPTIC_FEEDBACK | Haptic feedback event with position, intensity, frequency |
SPATIAL_AUDIO | 3D positioned audio source with frequency spectrum |
ENVIRONMENTAL_SENSE | Environmental sense data (temperature, wind, humidity, scent) |
Developer Intelligence
Code analysis, build tracking, and development session recording.
| Entity Type | Description |
|---|
CODE_SYMBOL | Function, class, or module embedding with change history |
DEV_SESSION | Developer session state with activity vectors |
TEST_RESULT | Test execution result with coverage and timing vectors |
BUILD_METRIC | Build pipeline metrics: duration, artifact size, dependency count |
Trust & Validation
Network trust verification and security monitoring.
| Entity Type | Description |
|---|
VALIDATION_PROBE | Trust validation probe with integrity score |
Usage Example
Specify the entity type when ingesting data to enable optimized compression and indexing:
{
"entity_id": "sensor-42",
"entity_type": "LINK_METRIC",
"vector": [0.1, 0.8, 0.3, 0.9],
"timestamp": "2025-06-01T12:00:00Z"
}
Tip
If you don't specify an entity type — or specify one the engine doesn't recognize — VectorScaleDB stores it as OTHER (never an error). For best compression and query performance, always copy the exact type name that matches your data source from the tables above.