Opening Played bythe Bishop (Dario Pasquini) 2026-10-28

Anatomy of Apple's Personal Knowledge Graph

Every Mac since macOS 13 (2022) ships a pair of daemons whose only purpose in life is to build a graph about who you are, who you know, how those people are related to you and to each other, how you communicate with them, where you go, what you do and when. In this post, we look into how this system works and give you a tool to peek into your own graph.

The position

A family fork: one knight on e2 attacks White's king, queen, rook and bishop at once. White's pieces are the household the graph is about; the knight is the store, one move from every one of them. White's own last move, 18.e3, emptied the square it stands on. White is in check and has one legal reply, Kh1; then the knight takes the queen. The check falls on the one who accepted the terms. What is lost is someone else.

3r1rk1/pp2qppp/8/3p1b2/8/1NQ1P1B1/PP2nPPP/2R2RK1 w - - 1 19

Finding

Target: Apple Intelligence / Personal Knowledge Graph
Affected: macOS 13+, Intel and Apple silicon
Class: Privacy
Personalized AI

What if I told you that every Mac since 2022 ships a pair of daemons whose only purpose in life is to build a graph about you and the people around you: who you are, who you know, how those people are related to you and to each other, how you communicate with them, where you go, with whom, what you do and when. This graph is built on device by cross-referencing pretty much everything on the machine: Contacts, Calendar, Photos, Messages, Mail, location, Screen Time and a long tail of behavioral signals. And, more importantly, it is held as a queryable store that LLMs retrieve from (allegedly). Would you believe me?

Well, that's what is happening, and interestingly enough, there is little documentation of how this whole mechanism works on your Mac. The closest thing to an official description is the man page (man 8 knowledgeconstructiond), and this is all you get:

man 8 knowledgeconstructiondmacOS 26.6.2, build 25G83

NAME
     knowledgeconstructiond – A daemon that analyzes content on the
     device to build a general purpose knowledge graph.

DESCRIPTION
   knowledgeconstructiond analyzes the content on this device to help
   build a knowledge graph representing the device and the user. It
     works together with intelligenceplatformd, performing some of the
     heavy processing tasks, to generate the knowledge graph.

Previous work: To be fair, Apple's own researchers did publish a paper on the techniques behind this graph construction, back in 2023. But there is next to nothing on how it is integrated into macOS: which versions run it, and the exact list of what is collected and inferred. And not a word about the "kinship" inference discussed below. Also, we personally ran into it while doing some security research on the Apple Intelligence bundle in macOS 26 in August 2026; but a few months before, Nic Lydon came across the same database and wrote it up in I Built a Personal Knowledge Graph. Apple Had Already Built One on My Laptop.

But we go a bit deeper.

This blog post is an account of some analysis work we carried out on what the knowledge graph actually does, so that you (the user) can get a better understanding of how this mechanism works, and some clarity on what the graph holds about you and your family and friends. Everything in it was collected on macOS 26 (26.6.2 at the time of writing).

To this end, we open-source a local dashboard (iKnow) you can use to read the content of your personal knowledge graph on your Mac.

Finally, we derail a bit and look into what we do best: weaponizing and exploiting. We run a thought experiment (one that applies to any such personalized system) and show what happens if such a knowledge base ends up in the wrong hands, and how a "privacy single point of failure" can be exploited to threaten you in ways you may not have considered before (when combined with an abliterated LLM).

What's in the personal knowledge graph?

The two daemons keep their work in ~/Library/IntelligencePlatform, a directory of SQLite databases (fifty-one databases) stored in plaintext but located in a zone protected by TCC Full Disk Access.

Likely, the most interesting is graph.db. It is a triple store written in a vocabulary of 181 entity classes and 278 predicates: every row is a subject, a predicate and an object. The subject is an entity (a person, an app, an event); the object is either a plain value (a name, a handle, a date) or another entity, which is where the graph gets its edges. The predicate is the interesting part: it says what kind of relationship the system believes holds between the two.

an edge, and the rows it is stored as

One edge of the graph (synthetic data): a person and the organization the store holds as their employer. PS500 is the id predicate.json gives employer, and its range is not an organization but a relationship type — so one edge is two rows sharing a relationshipId, one naming the kind of relationship and one pointing at the other entity. The forty-six relationship type classes in the table below exist to carry exactly that, which is what lets an edge hold a confidence of its own and be retracted later.

Most of what the two daemons do is populate those predicates, and the tables kept beside them, from everything the OS will let them read. Into the graph go Contacts; Photos (faces); Calendar (titles and attendees); Family Sharing (including the age of every member); the Medical ID emergency contact in Health; events and contacts Siri Suggestions finds in Mail and Messages; people donated by apps through App Intents; and installed apps. Beside it, in tables of their own, sit who appears with whom in your photos; the communication log (who, when, which app, which direction; no content), which also pulls from your other devices; location and movement; and a long tail of device signals (screen locked, Wi-Fi, Bluetooth, charging, ambient light, and a stream named ContextualUnderstanding.PhysicalSocial whose record format lists people by name, phone and email, each with a start and an end time; it reads like a log of who was physically around you, though we never saw it filled in).

What is inferred

The complete list of the 181 entity classes:

the 181 entity classes in class.json

Class	Label	Group	Mechanism found?
`SB56`	entity	core	implemented
`SB104`	person	core	implemented
`SB152`	place	core	implemented
`nm_topic`	topic	core	implemented
`SB65`	event	core	implemented
`nm_document`	document	core	could not find mechanism
`SB473`	home	core	could not find mechanism
`SB10`	organization	core	implemented
`SB2`	group of humans	core	implemented
`SB144`	software	core	implemented
`SB193`	organism	core	implemented
`SB198`	agent	core	implemented
`SB83`	concept	core	implemented
`SB400`	occurrence	core	implemented
`SB60`	role	core	implemented
`SB197`	siribase type	core	implemented
`nm_knowledgeSource`	knowledge source	core	implemented
`SB170`	time	core	could not find mechanism
`SB175`	group	core	implemented
`SB470`	device primary user	person	could not find mechanism
`SB199`	musical artist	person	could not find mechanism
`SB14`	sports team	person	implemented
`SB417`	sports organization	person	could not find mechanism
`SB115`	sports league	person	could not find mechanism
`SB185`	athlete	person	could not find mechanism
`SB21`	person in the arts	person	could not find mechanism
`SB184`	musician	person	could not find mechanism
`nm_screenTimeSegment`	screen time segment	person	could not find mechanism
`CS8`	sex	attribute	might be implemented (unverified)
`CS4`	unit of measurement	attribute	implemented
`CS25`	type of profession	attribute	implemented
`CS37`	religion	attribute	could not find mechanism
`SB74`	language	attribute	could not find mechanism
`SB18`	political party	attribute	could not find mechanism
`SB461`	genre	attribute	implemented
`SB537`	mode of transportation	attribute	implemented
`SB544`	automobile model	attribute	implemented
`SB418`	make	attribute	implemented
`SB399`	content rating	attribute	could not find mechanism
`SB48`	populated place	place	implemented
`SB101`	state	place	might be implemented (unverified)
`SB163`	country	place	might be implemented (unverified)
`SB370`	time zone	place	might be implemented (unverified)
`SB131`	county	place	implemented
`SB471`	calendar	event	could not find mechanism
`SB472`	calendar event	event	implemented
`SB513`	activity event	event	implemented
`SB71`	sports game event	event	could not find mechanism
`SB77`	holiday event	event	implemented
`SB514`	transportation activity	activity	implemented
`SB515`	working activity	activity	implemented
`SB516`	meeting activity	activity	implemented
`SB517`	eating activity	activity	might be implemented (unverified)
`SB518`	watching TV activity	activity	might be implemented (unverified)
`SB519`	communicating activity	activity	implemented
`SB520`	shopping activity	activity	implemented
`SB523`	traveling activity	activity	might be implemented (unverified)
`SB524`	reading activity	activity	implemented
`SB525`	vacationing activity	activity	implemented
`SB526`	gaming activity	activity	implemented
`SB527`	socializing activity	activity	might be implemented (unverified)
`SB528`	waking up activity	activity	might be implemented (unverified)
`SB529`	going to bed activity	activity	might be implemented (unverified)
`SB522`	sleeping activity	activity	implemented
`SB530`	exercising activity	activity	implemented
`SB521`	mindfulness activity	activity	might be implemented (unverified)
`SB531`	on the phone activity	activity	implemented
`SB532`	facetime activity	activity	implemented
`SB533`	walking activity	activity	implemented
`SB534`	running activity	activity	implemented
`SB535`	bicycling activity	activity	implemented
`SB536`	hiking activity	activity	could not find mechanism
`SB722`	commute activity	activity	implemented
`SB723`	dining activity	activity	implemented
`SB726`	attending a performance activity	activity	implemented
`SB732`	stationary activity	activity	implemented
`SB764`	location visit activity	activity	implemented
`SB784`	driving activity	activity	could not find mechanism
`SB786`	behavior activity	activity	could not find mechanism
`SB394`	sports activity	activity	implemented
`SB116`	type of activity	activity type	might be implemented (unverified)
`SB545`	transportation activity type	activity type	could not find mechanism
`SB546`	working activity type	activity type	could not find mechanism
`SB547`	meeting activity type	activity type	could not find mechanism
`SB548`	eating activity type	activity type	could not find mechanism
`SB549`	watching media activity type	activity type	could not find mechanism
`SB550`	communicating activity type	activity type	could not find mechanism
`SB551`	shopping activity type	activity type	could not find mechanism
`SB552`	mindful activity type	activity type	could not find mechanism
`SB553`	sleep activity type	activity type	could not find mechanism
`SB554`	travel activity type	activity type	could not find mechanism
`SB555`	reading activity type	activity type	could not find mechanism
`SB556`	vacation activity type	activity type	could not find mechanism
`SB557`	social activity type	activity type	could not find mechanism
`SB558`	waking activity type	activity type	could not find mechanism
`SB559`	going to bed activity type	activity type	could not find mechanism
`SB560`	phone call activity type	activity type	could not find mechanism
`SB561`	facetime activity type	activity type	could not find mechanism
`SB562`	walking activity type	activity type	could not find mechanism
`SB563`	running activity type	activity type	could not find mechanism
`SB564`	biking activity type	activity type	could not find mechanism
`SB565`	hiking activity type	activity type	could not find mechanism
`SB483`	type of physical exercise	activity type	could not find mechanism
`SB721`	commute activity type	activity type	could not find mechanism
`SB785`	driving activity type	activity type	could not find mechanism
`SB787`	behavior activity type	activity type	could not find mechanism
`SB425`	vehicle	transport	implemented
`SB543`	on foot	transport	could not find mechanism
`SB335`	automobile	transport	implemented
`SB538`	plane	transport	could not find mechanism
`SB539`	bicycle	transport	could not find mechanism
`SB358`	ship	transport	could not find mechanism
`SB541`	bus	transport	could not find mechanism
`SB542`	locomotive train	transport	could not find mechanism
`SB51`	work	media	implemented
`SB405`	media work	media	implemented
`SB44`	song	media	implemented
`SB138`	album	media	could not find mechanism
`SB85`	musical work	media	implemented
`SB183`	movie	media	could not find mechanism
`SB295`	television program	media	could not find mechanism
`SB49`	animal	animal	could not find mechanism
`nm_pet`	pet	animal	could not find mechanism
`nm_dog`	dog	animal	could not find mechanism
`nm_cat`	cat	animal	could not find mechanism
`nm_myPet`	my pet	animal	could not find mechanism
`nm_myDog`	my dog	animal	could not find mechanism
`nm_myCat`	my cat	animal	could not find mechanism
`nm_walletOrder`	wallet order	commerce	implemented
`nm_walletTransaction`	wallet transaction	commerce	could not find mechanism
`nm_payment`	payment	commerce	implemented
`nm_transaction`	transaction	commerce	implemented
`nm_email`	email	commerce	implemented
`nm_shippingInfo`	shippingInfo	commerce	could not find mechanism
`nm_shipmentFullFillmentInfo`	shipment full fillment info	commerce	implemented
`CS29`	location relationship type	edge	implemented
`CS97`	address relationship type	edge	implemented
`nm_topicRelationshipType`	topic relationship type	edge	might be implemented (unverified)
`CS1`	relationship type	edge	implemented
`nm_senderRelationshipType`	sender relationship type	edge	might be implemented (unverified)
`nm_calendarRelationshipType`	calendar relationship type	edge	implemented
`nm_organizerRelationshipType`	organizer relationship type	edge	could not find mechanism
`CS105`	text relationship type	edge	could not find mechanism
`CS101`	language relationship type	edge	could not find mechanism
`CS22`	identifier relationship type	edge	implemented
`CS2`	image relationship type	edge	might be implemented (unverified)
`CS13`	membership relationship type	edge	implemented
`CS165`	employment relationship type	edge	implemented
`CS154`	time zone relationship type	edge	might be implemented (unverified)
`CS3`	quantity relationship type	edge	implemented
`CS36`	religion relationship type	edge	could not find mechanism
`CS167`	interest relationship type	edge	could not find mechanism
`CS38`	participation relationship type	edge	implemented
`nm_calendarSourceRelationshipType`	calendar source relationship type	edge	implemented
`CS168`	agent affiliation relationship type	edge	implemented
`nm_dateRelationshipType`	date relationship type	edge	implemented
`nm_administratorRelationshipType`	administrator relationship type	edge	could not find mechanism
`nm_visualIdentifierRelationshipType`	visual identifier relationship type	edge	implemented
`nm_songRelationshipType`	song relationship type	edge	implemented
`nm_softwareRelationshipType`	software relationship type	edge	implemented
`CS173`	event relationship type	edge	could not find mechanism
`nm_localizedNameRelationshipType`	localized name relationship type	edge	implemented
`nm_confidenceRelationshipType`	confidence relationship type	edge	implemented
`CS31`	performance relationship type	edge	could not find mechanism
`CS15`	part whole relationship type	edge	could not find mechanism
`CS113`	rating relationship type	edge	could not find mechanism
`CS166`	group relationship type	edge	implemented
`nm_livingBeingRelationship`	living being relationship	edge	implemented
`nm_entityAliasRelationshipType`	entity alias relationship type	edge	implemented
`nm_extractionTagRelationshipType`	extraction tag relationship type	edge	implemented
`nm_orderEmailRelationshipType`	order email relationship type	edge	implemented
`nm_orderTransactionRelationshipType`	order transaction relationship type	edge	implemented
`nm_orderShippingInfoRelationshipType`	order shipping info relationship type	edge	implemented
`nm_entityOrganizationRelationshipType`	entity organization relationship type	edge	implemented
`nm_shipmentFullFillmentEmailRelationshipType`	shipment full fillment email relationship type	edge	implemented
`nm_orderShipmentFullFillmentRelationshipType`	order shipment full fillment relationship type	edge	implemented
`nm_orderPaymentRelationshipType`	order payment relationship type	edge	implemented
`nm_fromToEmailRelationshipType`	from to email relationship type	edge	could not find mechanism
`nm_orderPersonRelationshipType`	order person relationship type	edge	implemented
`nm_shipmentPersonRelationshipType`	shipment person relationship type	edge	implemented
`nm_emailInfoRelationshipType`	email info relationship type	edge	could not find mechanism

Every class the shipped ontology declares, grouped rather than left in file order: the core types, then people, attributes, places and events, then the activity block, and last the forty-six relationship types, which are not things at all — each one reifies an edge so that the edge can carry facets of its own, a confidence among them. The ids and labels are Apple's; the grouping is ours. intValue runs 1 to 185 with four numbers missing — 18, 54, 169, 181 — and nothing on the disk says what they were.

Fifty-seven of the 181 describe an activity: sleeping, exercising, mindfulness, going to bed, commute, dining, most of them with a matching type class beside them, so nearly a third of the vocabulary went on what you are doing rather than on who or where you are. That said, it is not clear that every class is actually used; some may be placeholders that are declared and never filled. In the table, we flag as implemented the classes for which we could locate a mechanism that might be responsible for the class, but those are to be taken as inference work rather than ground truth.

And this is the complete list of predicates (same disclaimer applies):

the 278 predicates in predicate.json

Predicate	What it records	Data source	How it is obtained	Mechanism found?
`PS1`	what kind of thing this is	every source	Rule	implemented
`PS10`	genre	Apple world knowledge	Apple-shipped	implemented
`PS102`	this record is the same person/thing as that one	every source	Embedding model	implemented
`PS106`	duration	Calendar, Suggestions	Copied	could not find mechanism
`PS107`	has location	Calendar, location history	Copied	might be implemented (unverified)
`PS123`	part in part-whole relationship	Apple world knowledge	Apple-shipped	could not find mechanism
`PS132`	has religion	Contacts	Copied	could not find mechanism
`PS133`	religion	Apple world knowledge	Apple-shipped	could not find mechanism
`PS135`	area	Apple world knowledge	Apple-shipped	could not find mechanism
`PS137`	performed by	Apple world knowledge	Apple-shipped	could not find mechanism
`PS143`	attendee identifier	Calendar, Suggestions	Copied	might be implemented (unverified)
`PS144`	took part in an event	Calendar, Photos	Copied	could not find mechanism
`PS147`	population	Apple world knowledge	Apple-shipped	could not find mechanism
`PS16`	performer	Apple world knowledge	Apple-shipped	could not find mechanism
`PS194`	published by	Apple world knowledge	Apple-shipped	implemented
`PS2`	unit	Apple world knowledge	Apple-shipped	implemented
`PS225`	has location relationship	Calendar, location history	Copied	implemented
`PS232`	has attendee	Calendar, Suggestions	Copied	implemented
`PS248`	starting location	Calendar, location history	Copied	implemented
`PS249`	end location	Calendar, location history	Copied	implemented
`PS29`	start time	Calendar, Suggestions	Copied	implemented
`PS296`	URL	Calendar, apps, world knowledge	Copied	implemented
`PS3`	quantity	Apple world knowledge	Apple-shipped	implemented
`PS30`	end time	Calendar, Suggestions	Copied	implemented
`PS312`	first name	Contacts	Copied	implemented
`PS313`	username / handle	Contacts, Family Sharing	Copied	implemented
`PS314`	sport	Apple world knowledge	Apple-shipped	could not find mechanism
`PS315`	postal code	Contacts, order emails	Copied	implemented
`PS316`	full street address	Contacts, order emails	Copied	might be implemented (unverified)
`PS317`	has address	Contacts, order emails	Copied	implemented
`PS324`	has language	Contacts	Copied	could not find mechanism
`PS325`	languages spoken	Contacts	Copied	could not find mechanism
`PS33`	name	Contacts	Copied	implemented
`PS335`	text value	Calendar, apps, world knowledge	Copied	could not find mechanism
`PS336`	official name	Apple world knowledge	Apple-shipped	could not find mechanism
`PS34`	gender identity	Contacts	Copied	might be implemented (unverified)
`PS343`	demonym	Apple world knowledge	Apple-shipped	could not find mechanism
`PS35`	citizen of	Apple world knowledge	Apple-shipped	could not find mechanism
`PS358`	has rating	Apple world knowledge	Apple-shipped	could not find mechanism
`PS359`	rating	Apple world knowledge	Apple-shipped	could not find mechanism
`PS36`	birth name	Contacts	Copied	implemented
`PS37`	date of birth	Contacts	Copied	implemented
`PS374`	has member	Contacts, Family Sharing	Copied	implemented
`PS392`	last updated	Calendar, Suggestions	Copied	implemented
`PS396`	external identifier, its value, its type	every source	Joined	implemented
`PS4`	width	Apple world knowledge	Apple-shipped	might be implemented (unverified)
`PS406`	email address	Contacts, order emails	Copied	implemented
`PS407`	phone number	Contacts, order emails	Copied	implemented
`PS424`	member of political party	Apple world knowledge	Apple-shipped	could not find mechanism
`PS441`	family name	Contacts	Copied	implemented
`PS447`	has timezone	Calendar, location history	Copied	could not find mechanism
`PS448`	time zone	Calendar, location history	Copied	might be implemented (unverified)
`PS45`	member of	Contacts, Family Sharing	Copied	implemented
`PS450`	address country	Contacts, order emails	Copied	might be implemented (unverified)
`PS453`	city	Contacts, order emails	Copied	might be implemented (unverified)
`PS454`	country code ISO 3166-1 alpha-2	Contacts, order emails	Copied	might be implemented (unverified)
`PS455`	administrative district	Contacts, order emails	Copied	might be implemented (unverified)
`PS46`	group member identifier	Family Sharing	Joined	could not find mechanism
`PS484`	official language	Apple world knowledge	Apple-shipped	could not find mechanism
`PS486`	display image	Photos, Contacts	Copied	could not find mechanism
`PS5`	height	Apple world knowledge	Apple-shipped	might be implemented (unverified)
`PS500`	employer	Contacts	Copied	implemented
`PS51`	description	Calendar, apps, world knowledge	Copied	implemented
`PS515`	location within location	Apple world knowledge	Apple-shipped	could not find mechanism
`PS520`	has contact information	Contacts, order emails	Copied	implemented
`PS524`	native home	Apple world knowledge	Apple-shipped	could not find mechanism
`PS53`	part of	Apple world knowledge	Apple-shipped	could not find mechanism
`PS545`	name prefix	Contacts	Copied	implemented
`PS546`	middle name	Contacts	Copied	implemented
`PS547`	name suffix	Contacts	Copied	implemented
`PS548`	department	Contacts	Copied	implemented
`PS557`	topics a person is interested in	Personalization Portrait	Scored model	could not find mechanism
`PS558`	topics a person is interested in	Personalization Portrait	Scored model	could not find mechanism
`PS565`	abbreviation	Apple world knowledge	Apple-shipped	could not find mechanism
`PS566`	group has part	Apple world knowledge	Apple-shipped	could not find mechanism
`PS567`	seconds from GMT	Calendar, location history	Copied	could not find mechanism
`PS568`	sub administrative area	Contacts, order emails	Copied	implemented
`PS569`	sub locality	Contacts, order emails	Copied	implemented
`PS570`	sub thoroughfare	Contacts, order emails	Copied	implemented
`PS571`	latitude	Calendar, location history	Copied	implemented
`PS572`	longitude	Calendar, location history	Copied	implemented
`PS573`	has latitude/longitude	Calendar, location history	Copied	implemented
`PS574`	previous family name	Contacts	Copied	implemented
`PS578`	has organizer	Calendar, Suggestions	Copied	implemented
`PS579`	on calendar	Calendar, Suggestions	Copied	could not find mechanism
`PS580`	calendar event sender	Calendar, Suggestions	Copied	implemented
`PS581`	second address line	Contacts, order emails	Copied	implemented
`PS582`	who administers the home	HomeKit	Copied	could not find mechanism
`PS586`	status	—	Not established	implemented
`PS598`	a person is related to / associated with another	Contacts, Health, Family Sharing, Screen Time, Photos	XGBoost + rules + copied	implemented
`PS599`	a person is related to / associated with another	Contacts, Health, Family Sharing, Screen Time, Photos	XGBoost + rules + copied	could not find mechanism
`PS618`	has subevent	Calendar, Suggestions	Copied	could not find mechanism
`PS66`	source URL	Calendar, apps, world knowledge	Copied	implemented
`PS67`	date of death	Apple world knowledge	Apple-shipped	could not find mechanism
`PS69`	external identifier, its value, its type	every source	Joined	implemented
`PS7`	image file	Photos, Contacts	Copied	might be implemented (unverified)
`PS709`	has driver	CarPlay, Wallet	Copied	could not find mechanism
`PS710`	transportation type	Apple world knowledge	Apple-shipped	could not find mechanism
`PS711`	elevation gain	Apple world knowledge	Apple-shipped	could not find mechanism
`PS712`	terrain	Apple world knowledge	Apple-shipped	implemented
`PS713`	fuel consumed	Apple world knowledge	Apple-shipped	could not find mechanism
`PS714`	highway mileage	Apple world knowledge	Apple-shipped	could not find mechanism
`PS715`	city mileage	Apple world knowledge	Apple-shipped	could not find mechanism
`PS716`	make of automobile	CarPlay, Wallet	Copied	could not find mechanism
`PS717`	model of automobile	CarPlay, Wallet	Copied	could not find mechanism
`PS718`	two events happened together	Photos, Calendar	Rule	could not find mechanism
`PS719`	kind of activity	Health, motion sensors	Rule	could not find mechanism
`PS72`	external identifier, its value, its type	every source	Joined	implemented
`PS73`	occupation	Contacts, Apple world knowledge	Copied	implemented
`PS730`	sub premises	Contacts, order emails	Copied	might be implemented (unverified)
`PS731`	administrative area code	Contacts, order emails	Copied	might be implemented (unverified)
`PS733`	label the user gave a place or thing	Calendar, Suggestions	Copied	might be implemented (unverified)
`PS734`	participant obligation	Calendar, Suggestions	Copied	could not find mechanism
`PS740`	contact label	Contacts	Copied	implemented
`PS741`	has event	Calendar, Suggestions	Copied	could not find mechanism
`PS747`	behavior category	Behavioral history	Rule	could not find mechanism
`PS75`	has mother	—	Not established	could not find mechanism
`PS753`	group includes individual	Family Sharing	Copied	implemented
`PS76`	has father	—	Not established	could not find mechanism
`PS77`	spouse	—	Not established	could not find mechanism
`PS78`	has image	Photos, Contacts	Copied	could not find mechanism
`PS79`	has child	—	Not established	could not find mechanism
`PS83`	length	Apple world knowledge	Apple-shipped	could not find mechanism
`PS85`	role held	Apple world knowledge	Apple-shipped	could not find mechanism
`PS89`	another name the same person is known by	Contacts, Photos	Embedding model	implemented
`PS99`	net worth	Apple world knowledge	Apple-shipped	could not find mechanism
`nm_QID`	Wikidata id	Apple world knowledge	Apple-shipped	implemented
`nm_UMC_ID`	UMC ID	Apple Music	Copied	implemented
`nm_accountInstitution`	account institution	Wallet, order emails	Copied	implemented
`nm_accountLastFourDigits`	account last four digits	Wallet, order emails	Copied	implemented
`nm_accountType`	account type	Wallet, order emails	Copied	implemented
`nm_accuracy`	accuracy	Location history	Copied	could not find mechanism
`nm_activityCategory`	event type pulled out of text	Mail, Messages, app content	Rule	could not find mechanism
`nm_addressString`	address string	Calendar, Suggestions	Copied	implemented
`nm_age`	age	Family Sharing	Copied	implemented
`nm_albumIdentifier`	album identifier	Apple Music	Copied	implemented
`nm_albumName`	song album name	Apple Music, app usage	Copied	implemented
`nm_amount`	amount	Wallet, order emails	Copied	implemented
`nm_anniversary`	anniversary	Contacts	Copied	implemented
`nm_applicationIdentifier`	application identifier	Installed apps	Copied	implemented
`nm_artistIdentifier`	artist identifier	Apple Music	Copied	implemented
`nm_artistName`	song artist name	Apple Music, app usage	Copied	implemented
`nm_associationReason`	which mechanism named this face	Photos faces	Rule	implemented
`nm_availability`	availability	Calendar, Suggestions	Copied	could not find mechanism
`nm_birthdayContactID`	birthday contact id	Contacts	Joined	could not find mechanism
`nm_bundleID`	bundle id	Installed apps	Copied	implemented
`nm_calendarType`	calendar type	Calendar, Suggestions	Copied	could not find mechanism
`nm_carrierName`	carrier name	Wallet passes, Calendar	Copied	implemented
`nm_category`	topic scored from what you read and write	Personalization Portrait	Scored model	could not find mechanism
`nm_cnFavoriteActionType`	contact favorite action type	Contacts	Copied	implemented
`nm_confidence`	how sure the system is	whichever engine wrote the fact	Rule	implemented
`nm_confirmationConfidence`	how sure a name match is	Contacts, Photos	Embedding model	implemented
`nm_creationDate`	creation date	Calendar, Suggestions	Copied	implemented
`nm_dates`	dates	Calendar, Suggestions	Copied	implemented
`nm_documentID`	document ID	—	Not established	could not find mechanism
`nm_dynamicCategory`	topic scored from what you read and write	Personalization Portrait	Scored model	could not find mechanism
`nm_emailFrom`	email from	Order emails	Copied	implemented
`nm_emailIdentifier`	email identifier	Order emails	Joined	implemented
`nm_emailReplyTo`	email reply to	Order emails	Copied	implemented
`nm_emailTo`	email to	Order emails	Copied	could not find mechanism
`nm_emailType`	email type	Order emails	Copied	implemented
`nm_entityAliasRelationship`	link joining two names for one person	Contacts, Photos	Embedding model	implemented
`nm_exactVersion`	exact version	Installed apps	Copied	could not find mechanism
`nm_extensionHostId`	extension host id	Installed apps	Copied	could not find mechanism
`nm_familyMemberType`	family member type	Family Sharing	Copied	implemented
`nm_file`	file	Installed apps	Copied	could not find mechanism
`nm_flightNumber`	flight number	Wallet passes, Calendar	Copied	implemented
`nm_handleIdentifier`	handle identifier	Messages, Mail	Joined	implemented
`nm_handles`	the phone numbers and emails a person uses	Contacts	Copied	might be implemented (unverified)
`nm_hasAdministrator`	has administrator	HomeKit	Copied	could not find mechanism
`nm_hasCalendar`	has calendar	Calendar, Suggestions	Copied	could not find mechanism
`nm_hasConfidence`	per-source trust weight	Downloadable Apple asset	Apple-shipped	implemented
`nm_hasDate`	has date	Calendar, Suggestions	Copied	implemented
`nm_hasDomain`	which classes a predicate applies to	The ontology itself	Apple-shipped	could not find mechanism
`nm_hasEmail`	has email	Order emails	Copied	implemented
`nm_hasExtractionTags`	has extraction tags	Ingest lane	Rule	could not find mechanism
`nm_hasFavoriteSportsTeam`	has favorite sports team	App usage, Apple Music	Copied	could not find mechanism
`nm_hasField`	has field	—	Not established	implemented
`nm_hasLocalizedName`	has localized name	Apple world knowledge	Apple-shipped	implemented
`nm_hasMerchant`	has merchant	Wallet, order emails	Copied	implemented
`nm_hasPayment`	has payment	Wallet, order emails	Copied	implemented
`nm_hasPerson`	has person	Order emails	Copied	implemented
`nm_hasProfileImage`	has profile image	Contacts	Copied	implemented
`nm_hasRange`	which classes a predicate applies to	The ontology itself	Apple-shipped	implemented
`nm_hasSender`	has sender	Order emails	Copied	might be implemented (unverified)
`nm_hasShipmentFullFillment`	has shipment full fillment	Wallet, order emails	Copied	implemented
`nm_hasShippingInfo`	has shipping info	Wallet, order emails	Copied	could not find mechanism
`nm_hasSoftware`	has software	Installed apps	Copied	implemented
`nm_hasSong`	has song	Apple Music, app usage	Copied	implemented
`nm_hasSource`	has source	Calendar, Suggestions	Copied	could not find mechanism
`nm_hasTopic`	topic scored from what you read and write	Personalization Portrait	Scored model	might be implemented (unverified)
`nm_hasTransaction`	has transaction	Wallet, order emails	Copied	implemented
`nm_hasVisualIdentifier`	the face cluster for a person	Photos faces	Face clustering	implemented
`nm_homeTeam`	home team	Apple Music, app usage	Copied	could not find mechanism
`nm_iataCode`	iata code	Wallet passes, Calendar	Copied	implemented
`nm_image_bytes`	image bytes	Photos, Contacts	Copied	implemented
`nm_imputedAnniversary`	imputed anniversary	Contacts, behavior	Unknown model	implemented
`nm_imputedBirthday`	birthday filled in rather than told — and the one the system actually reads	Photos age estimate (candidate)	Unknown model	implemented
`nm_imputedCreationDate`	timestamp filled in or normalized	Calendar, Suggestions	Rule	implemented
`nm_imputedEndTime`	timestamp filled in or normalized	Calendar, Suggestions	Rule	implemented
`nm_imputedInferredBirthday`	birthday filled in rather than told — and the one the system actually reads	Photos age estimate (candidate)	Unknown model	implemented
`nm_imputedLastUpdated`	timestamp filled in or normalized	Calendar, Suggestions	Rule	implemented
`nm_imputedNonGregorianBirthday`	imputed non-gregorian birthday	Contacts, behavior	Unknown model	implemented
`nm_imputedOccurrenceDate`	timestamp filled in or normalized	Calendar, Suggestions	Rule	implemented
`nm_imputedStartTime`	timestamp filled in or normalized	Calendar, Suggestions	Rule	implemented
`nm_inferenceFeedbackID`	handle marking a machine-made claim	The kinship classifier	XGBoost	implemented
`nm_inferredBirthday`	birthday filled in rather than told — and the one the system actually reads	Photos age estimate (candidate)	Unknown model	implemented
`nm_inferredDeviceUser`	owner worked out rather than declared	Behavioral history	Unknown model	implemented
`nm_inferredSemanticLabel`	what a place is for, e.g. home, gym	Location history	Unknown model	implemented
`nm_intentPersonId`	person as an app referred to them	App donations	Joined	implemented
`nm_isAllDay`	is all day	Calendar, Suggestions	Copied	implemented
`nm_isCurrentUser`	this person is the phone's owner	Contacts me-card	Rule	implemented
`nm_isDetached`	is detached	Calendar, Suggestions	Copied	could not find mechanism
`nm_isFavorite`	is favorite	Contacts	Copied	implemented
`nm_isStarting`	is starting	Calendar, Suggestions	Copied	could not find mechanism
`nm_isSubscribed`	subscribed status	Calendar, Suggestions	Copied	could not find mechanism
`nm_launchReason`	launch reason	App usage, Screen Time	Copied	could not find mechanism
`nm_launchType`	launch type	App usage, Screen Time	Copied	could not find mechanism
`nm_league_name`	league name	Apple Music, app usage	Copied	implemented
`nm_listening_confidence`	per-language competence on a relationship	—	Not established	might be implemented (unverified)
`nm_localeIdentifier`	locale identifier	System settings	Copied	implemented
`nm_mappedTopic`	topic scored from what you read and write	Personalization Portrait	Scored model	could not find mechanism
`nm_merchantName`	merchant name	Wallet, order emails	Copied	implemented
`nm_naturalLanguageEventTypeIdentifier`	event type pulled out of text	Mail, Messages, app content	Rule	could not find mechanism
`nm_nonGregorianBirthday`	non-gregorian birthday	Contacts	Copied	implemented
`nm_occurrenceDate`	occurrence date	Calendar, Suggestions	Copied	implemented
`nm_orderContentType`	order content type	Wallet, order emails	Copied	implemented
`nm_orderNumber`	order number	Wallet, order emails	Copied	implemented
`nm_organizationIdentifier`	organization identifier	Contacts, Wallet	Joined	implemented
`nm_parentBundleId`	parent bundle id	Installed apps	Copied	could not find mechanism
`nm_paymentIdentifier`	payment identifier	Wallet	Joined	implemented
`nm_paymentMethodIsApplePay`	payment method is apple pay	Wallet, order emails	Copied	implemented
`nm_personIdentifier`	who a relationship points at	the other end of a relationship	Joined	implemented
`nm_personType`	what kind of thing Photos detected	Photos	Rule	implemented
`nm_phoneticFamilyName`	phonetic family name	Contacts	Copied	implemented
`nm_phoneticFirstName`	phonetic first name	Contacts	Copied	implemented
`nm_phoneticMiddleName`	phonetic middle name	Contacts	Copied	implemented
`nm_phoneticName`	phonetic name	Contacts	Copied	implemented
`nm_photosDetectionType`	what kind of thing Photos detected	Photos	Rule	implemented
`nm_poiType`	kind of place	Maps, location history	Copied	could not find mechanism
`nm_reading_confidence`	per-language competence on a relationship	—	Not established	might be implemented (unverified)
`nm_relationship`	a person is related to / associated with another	Contacts, Health, Family Sharing, Screen Time, Photos	XGBoost + rules + copied	implemented
`nm_relationshipLabel`	the relationship word, e.g. sister, coworker	Contacts, Health, Family Sharing	XGBoost + copied	implemented
`nm_role`	role	—	Not established	could not find mechanism
`nm_screenTimeSegmentPerson`	screen time segment person	Screen Time	Copied	could not find mechanism
`nm_senderDomain`	sender domain	Order emails	Copied	implemented
`nm_senderIdentifier`	sender identifier	Messages, Mail	Joined	might be implemented (unverified)
`nm_sgEventType`	event type worked out from an app or email	Suggestions	Rule	implemented
`nm_shipmentFullFillmentIdentifier`	shipment full fillment identifier	Wallet	Joined	implemented
`nm_shippingMethod`	shipping method	Wallet, order emails	Copied	implemented
`nm_shippingStatus`	shipping status	Wallet, order emails	Copied	implemented
`nm_shortVersion`	short version	Installed apps	Copied	could not find mechanism
`nm_signalLabel`	signal label	Photos	Rule	implemented
`nm_softwareIdentifier`	software identifier	Installed apps	Joined	might be implemented (unverified)
`nm_songIdentifier`	song identifier	Apple Music	Copied	could not find mechanism
`nm_sourceType`	source type	Ingest lane	Rule	implemented
`nm_subject`	subject	Order emails	Copied	implemented
`nm_suggestedName`	name guessed for an unnamed face	Photos faces + Messages text	Local LLM	implemented
`nm_suggestedRelationshipConfidence`	guessed relationship of a face to the phone's owner	Photos faces + Messages text	Local LLM	implemented
`nm_suggestedRelationshipToDeviceOwner`	guessed relationship of a face to the phone's owner	Photos faces + Messages text	Local LLM	implemented
`nm_teamIdentifier`	team identifier	Installed apps	Joined	implemented
`nm_toDisplayName`	display name on an email recipient	Email	Copied	implemented
`nm_toEmailAddress`	to email address	Email	Copied	implemented
`nm_topicIdentifier`	topic identifier	Personalization Portrait	Joined	might be implemented (unverified)
`nm_totalAmount`	total amount	Wallet, order emails	Copied	implemented
`nm_totalNotifications`	total notifications	App usage, Screen Time	Copied	could not find mechanism
`nm_totalPickups`	total pickups	App usage, Screen Time	Copied	could not find mechanism
`nm_trackingNumber`	tracking number	Wallet, order emails	Copied	implemented
`nm_transactionIdentifier`	transaction identifier	Wallet	Joined	implemented
`nm_transactionSource`	transaction source	Wallet, order emails	Copied	implemented
`nm_transactionStatus`	transaction status	Wallet, order emails	Copied	implemented
`nm_transactionType`	transaction type	Wallet, order emails	Copied	implemented
`nm_uncertainty`	uncertainty	Location history	Copied	could not find mechanism
`nm_unknownHandle`	unknown handle	Messages, Mail	Joined	implemented
`nm_unlocalizedRelationshipLabel`	the relationship word, e.g. sister, coworker	Contacts, Health, Family Sharing	XGBoost + copied	implemented
`nm_virtualConferenceURL`	virtual conference URL	Calendar, Suggestions	Copied	implemented
`nm_visualIdentifier`	the face cluster for a person	Photos faces	Face clustering	implemented
`nm_writing_confidence`	per-language competence on a relationship	—	Not established	might be implemented (unverified)

Every predicate the shipped ontology declares. The first column is the id as graph.db stores it; the last says how a value gets there: copied from a source, joined from another record, derived by a rule, shipped by Apple as world knowledge, or produced by a model (in bold). Seven we could not establish, and the table says so rather than guessing. The ids and labels are Apple's; the plain-language readings are ours.

How it is inferred

The methods run the whole range. Most of the vocabulary is filled by plain copying (a name from Contacts, an attendee from Calendar) and rules over the copies; the interesting rows come from models: a pair of XGBoost classifiers decides who is family from 144 mostly behavioral features, two embedding models are one of ten signals the system uses to decide when two records are the same person, twelve analyzers in Photos score partner, parent, child, coworker and friend from who appears in your pictures with you, on which trips and holidays, and Apple's on-device LLM reads your messages to put a name and a relationship on a face (if Apple Intelligence is active).

Whatever the method, every row lands in the graph with a confidence attached, a number between 0 and 1.

The family scores live apart. Two CoreML models ship next to the ontology, EntityTagging_Family.mlmodelc and EntityTagging_FamilyAndFriends.mlmodelc: binary XGBoost classifiers that take 144 features and return a class and a probability over it. Their scores do not go into the triple store but into a table of their own (entityTagging.db, one row per person, seventeen relationship columns from family to mother to coworker, recomputed every day), which is what the iKnow scorecard further down reads. Only two of those seventeen columns have a shipped model behind them; what fills the other fifteen, and how any of these probabilities would become a confidence in the graph, we could not trace.

The feature names are in each model's metadata.json, in plain text. They are the clearest statement of what this system is that I have found anywhere. All 144:

the 144 features in metadata.json

#	Feature	Group
0	`isVIP`	identity
1	`sharesWorkAddress`	identity
2	`hasSamePrivateEmailDomain`	identity
3	`hasBirthday`	identity
4	`hasSameEmailDomains`	identity
5	`sameLastName`	identity
6	`hasSamePublicEmailDomain`	identity
7	`photosPersonAge`	identity
8	`isFavorite`	identity
9	`hasSameEduEmailDomain`	identity
10	`hasSameWorkEmailDomain`	identity
11	`hasNickname`	identity
12	`allowListedNameMatch`	identity
13	`ageDifferenceCategory`	identity
14	`nameContainsEmoji`	identity
15	`nicknameContainsEmoji`	identity
16	`sharesHomeAddress`	identity
17	`allowListedNicknameMatch`	identity
18	`photosAgeDifferenceCategory`	identity
19	`iCloudFamilyMember`	identity
20	`personOverallPopularity`	communication
21	`personLongTermPopularity`	communication
22	`personPopularityGivenMonday`	communication
23	`personPopularityGivenTuesday`	communication
24	`personPopularityGivenWednesday`	communication
25	`personPopularityGivenThursday`	communication
26	`personPopularityGivenFriday`	communication
27	`personPopularityGivenSaturday`	communication
28	`personPopularityGivenSunday`	communication
29	`mondayPopularityGivenPerson`	communication
30	`tuesdayPopularityGivenPerson`	communication
31	`wednesdayPopularityGivenPerson`	communication
32	`thursdayPopularityGivenPerson`	communication
33	`fridayPopularityGivenPerson`	communication
34	`saturdayPopularityGivenPerson`	communication
35	`sundayPopularityGivenPerson`	communication
36	`personPosteriorProbabilityGivenMonday`	communication
37	`personPosteriorProbabilityGivenTuesday`	communication
38	`personPosteriorProbabilityGivenWednesday`	communication
39	`personPosteriorProbabilityGivenThursday`	communication
40	`personPosteriorProbabilityGivenFriday`	communication
41	`personPosteriorProbabilityGivenSaturday`	communication
42	`personPosteriorProbabilityGivenSunday`	communication
43	`personPopularityGivenMorning`	communication
44	`personPopularityGivenAfternoon`	communication
45	`personPopularityGivenEvening`	communication
46	`personPopularityGivenNight`	communication
47	`personPopularityGivenHome`	communication
48	`personPopularityGivenWork`	communication
49	`homePopularityGivenPerson`	communication
50	`workPopularityGivenPerson`	communication
51	`personPosteriorProbabilityGivenHome`	communication
52	`personPosteriorProbabilityGivenWork`	communication
53	`personPopularityGivenWeekday`	communication
54	`personPopularityGivenWeekend`	communication
55	`weekdayPopularityGivenPerson`	communication
56	`weekendPopularityGivenPerson`	communication
57	`top1PersonInteractionMechanism`	communication
58	`top2PersonInteractionMechanism`	communication
59	`top3PersonInteractionMechanism`	communication
60	`photosPersonOverallPopularity`	photos
61	`photosPersonLongTermPopularity`	photos
62	`photosPersonPopularityGivenMonday`	photos
63	`mondayPopularityGivenPhotosPerson`	photos
64	`photosPersonPosteriorProbabilityGivenMonday`	photos
65	`photosPersonPopularityGivenTuesday`	photos
66	`tuesdayPopularityGivenPhotosPerson`	photos
67	`photosPersonPosteriorProbabilityGivenTuesday`	photos
68	`photosPersonPopularityGivenWednesday`	photos
69	`wednesdayPopularityGivenPhotosPerson`	photos
70	`photosPersonPosteriorProbabilityGivenWednesday`	photos
71	`photosPersonPopularityGivenThursday`	photos
72	`thursdayPopularityGivenPhotosPerson`	photos
73	`photosPersonPosteriorProbabilityGivenThursday`	photos
74	`photosPersonPopularityGivenFriday`	photos
75	`fridayPopularityGivenPhotosPerson`	photos
76	`photosPersonPosteriorProbabilityGivenFriday`	photos
77	`photosPersonPopularityGivenSaturday`	photos
78	`saturdayPopularityGivenPhotosPerson`	photos
79	`photosPersonPosteriorProbabilityGivenSaturday`	photos
80	`photosPersonPopularityGivenSunday`	photos
81	`sundayPopularityGivenPhotosPerson`	photos
82	`photosPersonPosteriorProbabilityGivenSunday`	photos
83	`photosPersonPopularityGivenMorning`	photos
84	`morningPopularityGivenPhotosPerson`	photos
85	`photosPersonPosteriorProbabilityGivenMorning`	photos
86	`photosPersonPopularityGivenAfternoon`	photos
87	`afternoonPopularityGivenPhotosPerson`	photos
88	`photosPersonPosteriorProbabilityGivenAfternoon`	photos
89	`photosPersonPopularityGivenEvening`	photos
90	`eveningPopularityGivenPhotosPerson`	photos
91	`photosPersonPosteriorProbabilityGivenEvening`	photos
92	`photosPersonPopularityGivenNight`	photos
93	`nightPopularityGivenPhotosPerson`	photos
94	`photosPersonPosteriorProbabilityGivenNight`	photos
95	`photosPersonPosteriorProbabilityGivenMondayMorning`	photos
96	`photosPersonPosteriorProbabilityGivenMondayAfternoon`	photos
97	`photosPersonPosteriorProbabilityGivenMondayEvening`	photos
98	`photosPersonPosteriorProbabilityGivenMondayNight`	photos
99	`photosPersonPosteriorProbabilityGivenTuesdayMorning`	photos
100	`photosPersonPosteriorProbabilityGivenTuesdayAfternoon`	photos
101	`photosPersonPosteriorProbabilityGivenTuesdayEvening`	photos
102	`photosPersonPosteriorProbabilityGivenTuesdayNight`	photos
103	`photosPersonPosteriorProbabilityGivenWednesdayMorning`	photos
104	`photosPersonPosteriorProbabilityGivenWednesdayAfternoon`	photos
105	`photosPersonPosteriorProbabilityGivenWednesdayEvening`	photos
106	`photosPersonPosteriorProbabilityGivenWednesdayNight`	photos
107	`photosPersonPosteriorProbabilityGivenThursdayMorning`	photos
108	`photosPersonPosteriorProbabilityGivenThursdayAfternoon`	photos
109	`photosPersonPosteriorProbabilityGivenThursdayEvening`	photos
110	`photosPersonPosteriorProbabilityGivenThursdayNight`	photos
111	`photosPersonPosteriorProbabilityGivenFridayMorning`	photos
112	`photosPersonPosteriorProbabilityGivenFridayAfternoon`	photos
113	`photosPersonPosteriorProbabilityGivenFridayEvening`	photos
114	`photosPersonPosteriorProbabilityGivenFridayNight`	photos
115	`photosPersonPosteriorProbabilityGivenSaturdayMorning`	photos
116	`photosPersonPosteriorProbabilityGivenSaturdayAfternoon`	photos
117	`photosPersonPosteriorProbabilityGivenSaturdayEvening`	photos
118	`photosPersonPosteriorProbabilityGivenSaturdayNight`	photos
119	`photosPersonPosteriorProbabilityGivenSundayMorning`	photos
120	`photosPersonPosteriorProbabilityGivenSundayAfternoon`	photos
121	`photosPersonPosteriorProbabilityGivenSundayEvening`	photos
122	`photosPersonPosteriorProbabilityGivenSundayNight`	photos
123	`photosPersonPopularityGivenHome`	photos
124	`homePopularityGivenPhotosPerson`	photos
125	`photosPersonPosteriorProbabilityGivenHome`	photos
126	`photosPersonPopularityGivenWork`	photos
127	`workPopularityGivenPhotosPerson`	photos
128	`photosPersonPosteriorProbabilityGivenWork`	photos
129	`photosPersonPopularityGivenWeekday`	photos
130	`weekdayPopularityGivenPhotosPerson`	photos
131	`photosPersonPosteriorProbabilityGivenWeekday`	photos
132	`photosPersonPopularityGivenWeekend`	photos
133	`weekendPopularityGivenPhotosPerson`	photos
134	`photosPersonPosteriorProbabilityGivenWeekend`	photos
135	`photosPersonPosteriorProbabilityGivenWeekdayMorning`	photos
136	`photosPersonPosteriorProbabilityGivenWeekdayAfternoon`	photos
137	`photosPersonPosteriorProbabilityGivenWeekdayEvening`	photos
138	`photosPersonPosteriorProbabilityGivenWeekdayNight`	photos
139	`photosPersonPosteriorProbabilityGivenWeekendMorning`	photos
140	`photosPersonPosteriorProbabilityGivenWeekendAfternoon`	photos
141	`photosPersonPosteriorProbabilityGivenWeekendEvening`	photos
142	`photosPersonPosteriorProbabilityGivenWeekendNight`	photos
143	`hasWeekendWeekdayPhotosFeatures`	photos

Every input EntityTagging_Family.mlmodelc reads, in the order its metadata.json declares them. EntityTagging_FamilyAndFriends reads the same 144 in the same order, and every one is a number. The groups are the schema's own three blocks: twenty on who the person is, forty on when and how you communicate with them, eighty-four on the photographs you both appear in. Two of the twenty, photosPersonAge and photosAgeDifferenceCategory, are read from Photos too. The names are Apple's; the group labels are ours.

The identity group is the tidy story: same surname, same home address, an iCloud Family flag — and it is the small half. The bulk of the model is the other two: who you message and who appears in your photographs, conditioned on the day of the week and the time of day, and then on both at once. photosPersonPosteriorProbabilityGivenSaturdayNight is a shipped feature name in a shipped classifier whose job is deciding whether someone is family.

Deleting something does not delete it everywhere.

The live graph forgets. Delete a contact and nothing re-derives them; the next run replaces the table wholesale.

But the store keeps a second table, expired_stable_graph, beside the live one. The retracted row moves there and keeps its confidence and its sources. How long it stays there is not clear from what we could see. It seems to go after about 48 hours (the store keeps a prune watermark, and a recent snapshot of the table reached back no further than that); but on my own Mac, I could still read very old records in it.

And a conclusion can outlive the evidence for it. On my Mac, the scorecard still scored two people the graph had dropped the day before, both of them known to it only from a calendar invitation. Well, if you want your partner not to discover some contacts you might have deleted and their "closeness" to you, better to drop some rows from expired_stable_graph too (unfortunately, you have to do it manually). But I am sure you have nothing to hide (just joking).

Look into your own data

Is any of this sensitive? We'll leave that to you, since "sensitive" means different things to different people (though I think you have plenty to choose from here). However, the thing is, none of it lives in isolation. It is built to be joined and correlated; the nodes are there to be linked:

a personal knowledge graph (synthetic data)

me · the owner of this Mac · 1a group the store holds as an entity · 1family, as scored · 7scored close to me · 9employer · 4group chat · 6known, not scored close · 61membership of a group chata calendar event both attendedan employment statementnamed by the stored groupa closeness score above the cut · nobody typed it

How to read this

Every node is a row in the store, and the record under it is what the store holds. Size is how many rows name it; shape is what kind of thing it is; the one node in accent is you, and a ring marks the four the store scores as family. A dashed line is a conclusion nobody typed.

Click a node — or reach the graph with Tab, walk it with the arrow keys and press Enter — and its record replaces these notes.

Click a node and the record under the graph is what the store holds on it (the rows, not a summary of them). The sixty-one faint ones are the part nobody expects: people the store knows and has not placed, in it because they wrote to you once, or because they were on somebody else's invitation.

The most important point, though, is that this is not only about you (the person using the Mac). The people around you are in it too: your family, your friends, "anyone" you have talked to through your device. None of them was asked, and the store links them to you and, through the events and groups they share, to each other. A relationship is intelligence one level above the rows it was inferred from. For instance, by correlating across my own store, I (well, an LLM working for me) could easily work out the following:

That there is a child in the household, and roughly how old. The family-member types and a family circle give the household. The scorecard's child and son/daughter columns give the child's place in it. Beside it sits an age, copied from Family Sharing; and dates of birth in the store can carry the system's own word imputed, though what the system means by it is not clear to us.
Which daycare the child goes to, and both of the numbers that reach it. Copied off contact cards. Note what is not there: the calendar has no school-run event, so the store holds the institution and its lines but not the hour the child is dropped off. That is still enough to call a parent from a number their phone already trusts.
Where the family will be, on which days, between which hours. One calendar event, a place, two stored time slots and a title that names the family (so the guest list is in the string even where the attendee rows are empty).
Which of the family will be there. The scorecard names the ones scored as family, a household entity holds them together, and a Family Sharing circle says which of them are in the same family group.
And, in the same file, what is installed on the machine, down to the password manager.

It is easy to see how this could be weaponized if this single point of failure for your privacy ends up in the wrong hands. We play that (hypothetical) scenario out in a second write-up. The good thing is that this knowledge is meant to stay local on your machine, protected by the Full Disk Access permission; although it can be consumed by other services (but it is not entirely clear to us how and what can be accessed).

The reality is that the amount of information in this store is really something else: every time we dig into what the daemons infer, and how, a few new lines of investigation open up. Whatever you would rather no one knew about you or your family may well be in there somewhere, and we think you have the right to know whether it is. What you get instead: no documentation, no way to inspect the store and no general way to correct an inference that is wrong.

So we give you two tools. The first is iKnow, a local dashboard for browsing your own graph without anything leaving your Mac (the next section walks through it). The second is for going deeper. The iKnow repository ships a manual for your own agent that tells it where the databases are and how to read them, so it can go looking for whatever you are after. (Please point a self-hosted model at it, not a cloud one.)

iKnow

We designed iKnow as a web dashboard implemented in the Python standard library; no npm and no CDN.

It is meant to be run directly on your Mac, where the databases are stored. When executed, it binds 127.0.0.1, locates the databases in your file system (granted the necessary permissions), opens them read-only, collects the info on the fly and represents it on a dashboard. Nothing is written and nothing is logged; values go from the stores, over loopback, into a browser tab; it can be run air-gapped if you want.

We have tested it on macOS 26.6.2 and 26.7.1, and, only partially, on 27.0.1.

iKnow has a dozen pages.

Start here

What your Mac knows. The landing page: the graph of people around you. Below it come the faces in your photos, your family, your handles, your correspondents and where you will be next.

Its graph is the same household as the one at the top of this post, this time drawn by the tool's own renderer:

A dark dashboard showing a force-directed graph of about seventy-eight labeled nodes around one white node marked "(Me)", with a color key reading "you", "a group the store holds as an entity", "family, as scored", "scored close to you", "employer", "group chat" and "known, not scored close". — The first screen. The white node marked (Me) is the owner of the Mac; the rest are the people, employers and group chats the store links to it, colored by *what it says each one is*. Orange is family, as scored. Blue is a closeness score above the cut. The faded names around the edges are people the store knows and has not placed.
Nothing here is summarized that is not in the record: every node is a row, and clicking one shows you the row (which is what the graph at the top of this post does too).

The graph is the overview, and an overview is easy to shrug at. The pages behind it are not.

Social graph. Who the store links to whom through shared meetings, group chats and a closeness score. Each circle opens to its members and to the evidence behind it.

What it concluded

Kinship scorecard. The family bindings inferred by the daemon.

Its card on the landing page is cut down to the people it names:

A dashboard card headed "Family, as the scorecard has it", subtitled "61 people scored on 17 relationship columns. Nobody typed any of it", listing six people with one label and one score each: Mara Quill, mother, 0.960; Tomas Quill, father, 0.960; Maya Smith, daughter, 0.960; Nils Quill, brother, 0.500; Leo Smith, son, 0.500; Ines Quill, sister, 0.500. A section headed "The household, by how many signals agree" scores Mara Quill 8 of 9, Tomas Quill and Priya Sandoval 7 of 9 and Maya Smith 6 of 9, and a last one names the Quill household, 4 members, at confidence 0.880. — Six people, six relationships, and a subtitle saying nobody typed any of it. That holds for the scores; some of what they are computed from, you did type (a relation on a contact card, an emergency contact, Family Sharing). A mother, a father and a daughter at 0.960, which is as high as these columns go; a brother, a son and a sister at 0.500.
Below that is the part I find hardest to argue with. Nine separate checks (the partner column, the composite score, the household group entity, the Family Sharing token, the iCloud Family flag, top twelve-month contact, a Photos face cluster, a Contacts record, a reachable handle), and the card counts how many of them land on the same person. Eight of nine on the mother; seven on the father, and seven on Priya Sandoval, the partner, who is not in the list above: iKnow lists the relationships that roll up into another column (*mother* and *father* into *parent*), and *partner* rolls up into nothing. The join found the partner anyway.

The card is a summary of one table, and iKnow draws the whole table too:

A heatmap headed "Every person, every column": thirty people against seventeen relationship columns with abbreviated headers. The top seven rows are the family, each lit on its own relationship (mother, father, daughter, brother, son, sister, partner), on the column that relationship rolls up into, and on family and familyAndFriends; the partner's row is also lit on coworker. Below them a few rows are lit on friend or coworker, every row is lit on family and familyAndFriends, most of them at the same two shades, and the alumni and unknown columns are empty from top to bottom. — Sorted by *familyAndFriends*, the broadest of the scores. The seven at the top each light up their own relationship and the column it rolls up into (*mother* and *father* into *parent*, *sister* and *brother* into *sibling*, *son* and *daughter* into *child*), and the partner is also a colleague. Further down are the friends and the rest of the office. Under them, almost everyone else is the same two cells: one repeated value on *family* and another on *familyAndFriends*, which is what the classifier writes when it has nothing to say. Two columns (*alumni* and *unknown*) are never used at all.

The scores in these two shots are invented, and more generous than the real thing. On the Mac we took apart, only mother reached the top of the ladder; the few others it scored at all got one small vote on every family label at once, father and brother alike. What the fixture keeps is the table's grain: it only ever writes a handful of values (0.125, 0.25, 0.5 and 0.96), and most people sit on one default.

The face index. A face cluster for everyone in your photos.

Its co-occurrence graph does not use a single contact, message or calendar entry. It is built from your photographs alone:

A dashboard card headed "The face co-occurrence graph": forty-seven circles, each filled with a blurred face, linked when two of them appear in the same photograph and labeled with six-character fragments of cluster identifiers instead of names. One cluster sits at the center, linked to nearly everything; a tight knot sits to its left, a looser group above it, and a third group at the bottom right shares no link with the knot. A control above the graph reads "photographs on the nodes — 47 of 47 drawn here have one". — Two faces are linked when they appear in the same photograph, and that is the only input: 8,904 clustered faces over the 1,643 photographs that hold more than one. The labels are fragments of each cluster's local identifier, because there are no names to show. The owner is the hub. The knot on the left is the family, the group at the bottom right is work, and the two never share a frame.
Each node wears its cluster's key face, read out of the photo library and blurred by iKnow's own switch for sharing the screen. Under the blur there is nobody: the fixture has no photo library, so every face is a synthetic portrait painted for it.

This is the page's own honest point: the graph points at the face clusters and can name almost none of them (three of the twenty-nine, in the fixture). It does not need to. Who stands next to whom, how often, and which groups never meet is all there without a single name, and whoever knows the household can put the names back on in a second.

Guessed or given. What you typed in and what the Mac worked out for itself live in the same table. A confidence score is the only thing separating them.

What it recorded

A year of contact. Who you talked to, when, and not a word of message text (and it does not need any to tell when you are asleep).

Its card on the landing page is the one that gets the reaction, and it contains no inference at all.

A dashboard card headed "A year of who you talked to", subtitled "18,432 interactions over 365 days, 146 correspondents (97 named), 5 apps — and not one word of text", listing eight people with interaction counts, in and out splits, call counts and last-contact dates, and a final section headed "The hours it can read as sleep". — A year of contact, per person, split incoming and outgoing, with call counts and a last-seen date, across five messaging apps — and, exactly as the subtitle says, not one word of message text. None of this is content. It is all metadata, and it is enough.
The last line is the one to read twice: *the hours it can read as sleep.*

iKnow draws that line too, from the same log folded onto a single week:

Two dashboard cards. The first, headed "Weekday against hour of day — when this person is reachable, and when they are asleep", is a heatmap of seven weekday rows against twenty-four hour columns: a pale band from 01 to 07 on every day, running later into Saturday and Sunday mornings, a dark plateau through the working day on weekdays, and the darkest cells between 20 and 22. The second, headed "The hours the log goes quiet — a sleep schedule nobody wrote down", reads: "Hours carrying less than 25% of an even spread form one unbroken run of 6 hours, 01:00 to 07:00 Europe/London. 402 of 18.4K events fall inside it (2%)." — Every cell counts the messages and calls that fell in one hour of one weekday, over the year. The working week is in there, and so are the evenings and the late weekend mornings; and so is the pale band from one to seven, every night. Nobody stored a sleep schedule. The quietest six-hour stretch falls out of the timestamps for free.

That is the general shape of this store: almost nothing in it had to be collected, because almost everything in it is a consequence of something that was.

The calendar. The one layer that knows where you will be: the hour, the room, the attendees and the dial-in.

What you withdrew. Withdrawing a fact does not remove it. The row moves to an expired table and keeps its confidence and provenance.

The machinery

The store. The file itself: what it models, and two real rows expanded into everything they touch.

Device fingerprint. Every app installed on the Mac is written down in plain text, next to a token that is an unsalted SHA-256 of that same app identifier, so the page recomputes it in your browser and you can watch it match.

Who else reads it. The graph is the first copy, not the only one. This page maps the views that subscribe to it, the databases they publish into, the tables that repeat the same facts, the streams coming in from hardware paired with the Mac, and the machinery that is set up but has not run yet.

A note on macOS 27. macOS 27 was released on September 14, 2026. After the upgrade to 27.0.1, the graph withdrew most of the rows it had built from Contacts, Calendar, Siri Suggestions, Family Sharing and the app catalog, and rebuilt them over the following day. Some pages of iKnow may not work fully on macOS 27 yet.

But let's go deeper:

How to cite this game

BibTeX · click to select

@misc{8rank:apple-intelligence-personal-knowledge-graph,
  author       = {Dario Pasquini and Micha{\l} Bazyli},
  title        = {{Anatomy of Apple's Personal Knowledge Graph}},
  year         = {2026},
  month        = oct,
  howpublished = {\url{https://8rank.ai/game/apple-intelligence-personal-knowledge-graph}},
}