TL;DR
Every AI citation, Knowledge Panel and “who is X” answer is built on top of an entity record — a structured profile the machines hold about you. You can read that record programmatically, and you can influence it. This guide shows both, with working, clearly-labelled illustrative code.
The honest core of it: the Google Knowledge Graph Search API is read-only. You audit with it; you can’t write to it. The levers you actually control are Wikidata, your entity home and the corroborating web. So the method here is a loop — Locate → Read → Reconcile → Influence → Re-audit — with the Reconcile step optionally LLM-assisted, costed honestly at current Claude rates. Built for the post-May-2026 search environment, where a clean, corroborated entity is exactly the kind of trust signal core updates reward.
1. The entity record is the layer beneath every AI citation
When an AI system answers a question about your brand — names you in a recommendation, summarises what you do, decides whether to hedge or state a fact — it is not reasoning from your website in real time. It is leaning on a structured representation it already holds: an entity record. That record lives in Google’s Knowledge Graph, in Wikidata, in the training corpus, and in the retrieved web. Most teams have never read their own, which is a strange gap, because it is the single object that the whole entity-authority discipline is ultimately trying to improve.
This article is the technical end of that work. Where a brand-SERP read tells you what Google believes by eye, the Knowledge Graph and Wikidata APIs let you pull the record directly, diff it against the truth, and act on the parts you can change. It is, deliberately, an engineer’s view of entity SEO: less narrative, more JSON.
One thing to be clear about up front, because it shapes everything that follows. There are really two kinds of entity data source: the ones you can only read, and the ones you can also write. Google’s Knowledge Graph is read-only to you — there is an API to query it, but no API to edit it. Wikidata is openly writable. Your own site is fully yours. The craft of influencing your Google entity record is therefore indirect: you change the writable, corroborating sources and the schema on your own pages, then watch the read-only record update in response. Anyone selling you a button that “edits your Knowledge Graph” is selling you the indirect work with a direct-sounding label.
It helps to be concrete about the mechanism, because it explains why a messy record costs you real visibility. When a generative system handles “who is X” or “best X for Y,” it tends to resolve the named entity first, pull the structured facts it holds about that entity, and only then compose language around them. If the resolution step is shaky — two candidate entities share your name, or your record is thin — the system either picks the wrong one or hedges. If the facts it pulls are stale, the answer is confidently wrong. The entity record is upstream of the words, so errors there propagate into every answer downstream, invisibly, until someone audits the record itself. That is the failure mode this whole loop is designed to catch.
Why this matters more in the current search climate
A timely note, because the ground is moving as this is written. Google’s May 2026 broad core update completed on 2 June 2026, the second confirmed core update of the year, on a noticeably faster cadence than the historical three-to-four-month spacing. Google issued no update-specific guidance — the standing advice remains people-first, genuinely helpful, well-sourced content — and across every 2026 core update the consistent through-line has been that ranking is comparative and E-E-A-T-driven, judging each page against the rest of the candidate pool rather than to a fixed bar.
A clean entity record is not a separate game from that. It is one of the clearest ways a machine confirms experience, expertise, authoritativeness and trust: a well-formed, corroborated entity lets Google attribute authorship and provenance confidently, which is precisely what the trust half of E-E-A-T rewards. Put bluntly — if the systems are unsure who you are, they cannot be sure you are an authority, and uncertainty is what gets penalised in a comparative model. Auditing and tightening your entity record is, in that sense, core-update hygiene as much as it is AI-citation work. It is also the substrate that AI Overviews draw on when they decide which sources to cite.
2. The deliverable: the Entity-Record Audit Loop
Auditing an entity record is not a one-off lookup; it is a loop you run on a cadence, because the record drifts and your influence takes time to propagate. Five steps:
- Locate — find your entity’s identifier (the Knowledge Graph MID, or kgmid) so you are auditing the right node, not a namesake.
- Read — pull what each source holds: Google’s KG record (types, description, identifiers) and your Wikidata item (statements, sameAs, external IDs).
- Reconcile — diff the machine’s record against your canonical truth and produce a prioritised list of errors, gaps and contradictions.
- Influence — fix the writable sources: edit Wikidata ethically, tighten your entity home’s schema and sameAs, and earn the corroboration that confirms the change.
- Re-audit — re-run Locate and Read on a schedule to confirm the record actually moved, and to catch drift.
The reason the loop is worth formalising is the read/write split. Three of the five steps touch sources you can only read; the influence step is the only one that writes, and it writes to different places than it reads from. Here is the source map that makes that concrete — keep it next to you for the rest of the article.
| Source | Access | What it tells you / does | API surface |
| Google Knowledge Graph | Read only | Whether Google holds a distinct entity for you; its types, description and kgmid | KG Search API (legacy / Enterprise KG) |
| Wikidata | Read + write | Your structured statements, external IDs, sameAs — a direct feed into the graph | wbgetentities API + SPARQL |
| Your entity home | Read + write | The facts you assert: Organization schema, sameAs array, canonical NAP | Your own CMS / structured data |
| The corroborating web | Influence only | Independent confirmation that makes the machine trust the above | Earned links, mentions, citations |
How to read the map. You audit downward (read Google and Wikidata) and you influence upward (write Wikidata and your site, earn corroboration). The gap between what the read-only record says and what your canonical sources say is your work list. Everything below is how to run each step in code, what it costs, and where it breaks.
3. Steps 1–2: Locate and Read
Finding your kgmid with the Knowledge Graph Search API
Start by locating your entity. The Google Knowledge Graph Search API lets you query the graph by name and returns matching entities with their identifiers. A note on status, because it matters for anything you build on it: the original endpoint still works for backward compatibility, but Google is migrating this to Cloud Enterprise Knowledge Graph, and its own documentation warns the legacy API is read-only and “not suitable for use as a production-critical service.” Treat it as an audit tool, not infrastructure.
Illustrative — not production code. Locate your entity and read its kgmid + types.
import requests
API_KEY = “<your-kgsearch-api-key>”
ENDPOINT = “https://kgsearch.googleapis.com/v1/entities:search”
def locate(query, limit=5):
params = {“query”: query, “limit”: limit,
“indent”: True, “key”: API_KEY}
r = requests.get(ENDPOINT, params=params, timeout=10)
r.raise_for_status()
for el in r.json().get(“itemListElement”, []):
e = el[“result”]
print(e.get(“@id”), # kg:/g/… or /m/… (the kgmid)
e.get(“name”),
e.get(“@type”), # entity types Google assigns
e.get(“description”)) # the short descriptor it holds
locate(“Your Brand Name Ltd”)
Two things to inspect in the output. The @id is your entity’s identifier — confirm it resolves to you and not a same-name entity, because a namesake collision here means every downstream audit is reading the wrong node. The @type and description are Google’s current understanding of what you are; if the description is stale or the types are wrong, you have found your first work item. If the call returns nothing, that is itself a finding: Google may not hold a distinct entity for you yet, which makes the Influence step in section 5 your starting point rather than a refinement.
Spend real care on the confirmation step, because everything after it inherits the choice. If several candidates come back, use the @type and description to pick the one that is unambiguously your organisation, and sanity-check it by opening the entity in Google directly (the kgmid maps to a specific Google result). For brands with a common name or a more famous namesake — a consultancy sharing a founder’s name with an author, a UK firm sharing a name with a US product — you may find Google holds no clean entity for you at all, or holds one that is half-merged with the namesake. Both outcomes are diagnoses, not dead ends: the first sends you to entity creation, the second to disambiguation, and you now know which before spending a penny on either.
Reproducibility metadata
KG Search API: kgsearch.googleapis.com/v1 (legacy, read-only)
Migration target: Cloud Enterprise Knowledge Graph (Basic/Advanced)
Claude model (section 4): claude-haiku-4-5-20251001
anthropic-version: 2023-06-01 | SDK: anthropic Python (current 0.x)
Date tested: June 2026
Reading your Wikidata item
Google’s API tells you what Google concluded. Wikidata tells you what one of its most important inputs actually says — and unlike Google’s graph, you can edit it. Pull your item’s statements, external identifiers and sameAs links to see the structured facts feeding the wider graph. You can hit the wbgetentities action or, for anything relational, the SPARQL endpoint.
Illustrative — not production code. Read a Wikidata item’s labels, claims and identifiers.
import requests
WD = “https://www.wikidata.org/w/api.php”
def read_item(qid):
params = {“action”: “wbgetentities”, “ids”: qid,
“format”: “json”, “props”: “labels|descriptions|claims”}
r = requests.get(WD, params=params, timeout=10)
r.raise_for_status()
ent = r.json()[“entities”][qid]
label = ent[“labels”].get(“en”, {}).get(“value”)
desc = ent[“descriptions”].get(“en”, {}).get(“value”)
props = sorted(ent[“claims”].keys()) # e.g. P856 (website), P1448 (name)
return {“label”: label, “description”: desc, “properties”: props}
print(read_item(“Q12345”))
What you are checking: does the label and description match your real name and positioning; are the key properties present (official website P856, official name, industry, founders, headquarters); and crucially, is your sameAs web of external IDs complete and correct? The sameAs links — to your site, your LinkedIn, your Companies House record — are how the graph reconciles all your scattered mentions back to one entity, so a missing or wrong one is a high-priority fix. If you have no Wikidata item at all, that is the single biggest lever you are leaving unpulled, and creating one (notability permitting) belongs at the top of your Influence list.
Failure threshold and fallback. The KG Search API is rate-limited and explicitly not for production-critical use; if you are auditing more than a handful of entities or need reliability, the threshold to move is roughly “anything you’d schedule daily.” At that point the cheaper, more robust fallback is to cache results and lean on Wikidata’s own data dumps for relational data — which is exactly what Google’s documentation itself recommends when you need graphs of connected entities rather than single look-ups.
4. Step 3: Reconcile — diffing the record against the truth
Reconciliation needs a reference, and the reference is your canonical fact sheet: the small, authoritative set of facts about your entity that you treat as ground truth. Before you diff anything, write it down — exact legal and trading name, founding year, headquarters, primary industry, official website, key people, and the canonical URLs for every owned profile. This is dull and it is the most important five minutes in the process, because every downstream comparison is only as good as the truth you measure against. Teams that skip it end up “reconciling” two machine records against each other, which tells you where Google and Wikidata disagree but not which one is right.
With the fact sheet in hand, reconciliation is producing the diff: every place the machines are wrong, stale, incomplete or contradictory. For the hard, exact-match parts — does the phone number on Wikidata equal your canonical number, is the website P856 your live domain — do not reach for an LLM. A few lines of deterministic string comparison are faster, free and perfectly reliable. Reserve the model for the fuzzy parts: is this description semantically out of date, is this “industry” statement subtly wrong, does this related-entity set imply a confusion a string diff would miss.
Here is a pragmatic split: deterministic checks first, then an optional LLM pass over only the ambiguous fields. Using Claude Haiku for the fuzzy pass keeps it cheap, because this is exactly the high-volume extraction-and-classification work Haiku is built for.
Illustrative — not production code. Deterministic diff first, LLM only for ambiguous fields.
import anthropic
client = anthropic.Anthropic() # reads ANTHROPIC_API_KEY
def deterministic_diff(canonical, record):
# free, exact-match: NAP, website, identifiers
return {k: (canonical[k], record.get(k))
for k in canonical if canonical[k] != record.get(k)}
def semantic_review(canonical_desc, machine_desc):
msg = client.messages.create(
model=”claude-haiku-4-5-20251001″,
max_tokens=500,
messages=[{“role”: “user”, “content”:
f”Truth: {canonical_desc}\nMachine: {machine_desc}\n”
“List only factual discrepancies. Quote the exact wording “
“from ‘Machine’ for each. If none, reply ‘NONE’.”}],
)
return msg.content[0].text
Notice the prompt forces the model to quote the exact machine wording for every discrepancy it claims. That is a deliberate hallucination guardrail: a model asked to “find problems” will invent them to be helpful, so requiring a verbatim quote from the source makes fabricated contradictions self-evidently fail. Anything the model can’t quote, it can’t claim.
Cost at volume (current Claude pricing)
For a single brand auditing itself, the LLM pass is a rounding error — a handful of calls, a fraction of a penny. The cost question only appears at agency scale, where you might reconcile hundreds or thousands of entities (a client portfolio, or a competitor set). Per the official Claude pricing, Haiku 4.5 is $1 per million input tokens and $5 per million output tokens. A realistic reconciliation call — feeding the KG and Wikidata fields plus your canonical facts — runs roughly 2,500 input and 500 output tokens.
| Workload | Tokens | Standard | Batch (−50%) |
| 1 brand self-audit (~10 calls) | 25k in / 5k out | ~$0.05 | ~$0.03 |
| 1,000 entities | 2.5M in / 0.5M out | ~$5.00 | ~$2.50 |
| 10,000 entities | 25M in / 5M out | ~$50 | ~$25 |
Two levers cut even that. The Batch API halves it for anything you don’t need in real time — and entity audits are almost never time-sensitive, so batch should be your default here. Prompt caching cuts it further if you reuse a fixed instruction-and-schema prefix across every call, since cached input bills at roughly a tenth of the standard rate. The figures above are illustrative arithmetic from the published rates, not a quote, but the shape is the point: auditing entities with an LLM is cheap enough that cost is never the reason to skip it.
Failure threshold and cheaper fallback. Watch precision: if the semantic pass starts flagging “discrepancies” you can’t verify against the quoted source, or its false-positive rate climbs above what a human can triage comfortably, that’s the threshold to pull it back. The fallback is free and boring — drop to deterministic field matching plus a human eye on the handful of genuinely fuzzy descriptions. The LLM is an accelerant for scale, not a dependency; the audit works without it, just slower.
5. Step 4: Influence — changing what you can actually change
Here is where most “Knowledge Graph” pitches quietly mislead. You cannot write to Google’s Knowledge Graph. There is no edit endpoint, no submission form that injects a fact. What you can do is change every input Google reads, then wait for the read-only record to catch up. Three levers, in priority order.
Lever 1 — Edit Wikidata, ethically
Wikidata is the most direct writable feed into the graph, and editing it is legitimate — provided you do it as a genuine contributor, not a manipulator. Add missing statements (official website, founding date, headquarters), fix wrong ones, and complete your sameAs external IDs, each backed by a reliable published reference. The community will revert unsourced, promotional or self-serving edits, so the discipline is: state facts, cite sources, declare your connection, and never editorialise. This is the same line that separates legitimate from manipulative everywhere in this field — the intent test that underlies Google’s own stance on link schemes and manipulation applies just as cleanly to entity edits. Corroborated facts stick; assertions get reverted.
Failure threshold and fallback. If your Wikidata edits keep getting reverted, the threshold has been crossed: the problem isn’t the edit, it’s that the open web doesn’t yet corroborate the fact. The fallback is to go earn the corroboration first (lever 3), then re-state the fact. You can’t push a claim the rest of the record contradicts.
Lever 2 — Tighten your entity home and its schema
Your entity home — the canonical page that defines your organisation — is the input you control most completely. Mark it up with Organization schema whose name, URL, logo, founding date and contact facts are exactly right, and carry a complete sameAs array linking every official profile you own. That array is doing the same reconciliation job for Google that it does on Wikidata: telling the machine that all these scattered presences are one entity. The structured-data craft here overlaps directly with the technical and on-page foundations that also make pages eligible for AI citation — the same markup pays off twice.
Failure threshold and fallback. If schema is valid and consistent but the record still won’t form or correct, schema is no longer the constraint — you’ve hit the threshold where assertion isn’t enough and corroboration is the missing ingredient. The fallback is, again, lever 3.
Lever 3 — Earn the corroboration that makes facts stick
This is the one a link-building publication exists to talk about, and it is the lever that ultimately decides whether the other two hold. Google does not believe a fact because you asserted it on your site or added it to Wikidata; it believes it when independent, trusted sources repeat it. That independent repetition is exactly the output of editorial links and digital PR — and if the link layer itself is new to you, our primer on what backlinks are and why they carry authority sets the foundation this assumes. Every authoritative mention that names you and states your facts correctly is a corroboration event, which is why the activities that build links and the activities that build entity recognition are the same activities. Treat each earned placement as doing double duty: a link for ranking, and a fact for the graph.
When a fact is wrong in the record and resists correction, this is often the real fix — not more schema, but more corroboration of the correct fact, the same pattern that underpins recovering lost AI citations. And it is worth saying plainly, especially in the current update climate: there is no shortcut here. Manufactured corroboration — networks of thin sites all repeating the same claim — reads as manipulation to systems that are increasingly good at spotting coordinated patterns, and it can suppress the very entity you are trying to build. Real, independent sources or nothing.
6. Step 5: Re-audit and monitor
Entity influence is slow and non-linear. A Wikidata edit might reflect in the wider graph in days or take months; a corroboration campaign moves the record on its own schedule, not yours. So the final step is to close the loop: schedule the Locate and Read steps to run on a cadence and track whether the record actually moved.
A sensible rhythm is monthly for an active campaign, quarterly for maintenance. Log the kgmid, the Google description, the Wikidata statements and your reconciliation score each cycle, so you can see drift and confirm propagation rather than guessing. The discipline mirrors the way we treat entity authority as a quarterly instrument rather than a one-off number — the value is in the trend line.
Two practical monitoring notes. First, re-run Locate every cycle, not just Read: namesake collisions and merges can change which node is your entity over time, and you want to catch that early. Second, watch propagation asymmetrically — your own changes (schema, Wikidata) show up faster than earned-corroboration effects, so don’t judge a corroboration campaign on a four-week window. If you want to benchmark how a competitor’s entity record is constructed before you start, the same read steps point cleanly at a competitor-analysis workflow; their kgmid, Wikidata item and sameAs web are all public.
The tracker itself can be trivially simple — one spreadsheet row per cycle, with columns for the kgmid, the Google description verbatim, the count of correct Wikidata statements, the count of open discrepancies, and a one-line note on what changed. The value isn’t in the sophistication; it’s in having a dated baseline so that when a description finally updates or a discrepancy finally clears, you can tie it to the specific edit or campaign that moved it. Without the log you’re guessing at cause and effect across timescales long enough that memory fails. With it, you slowly build something most teams never have: an evidence base for what actually shifts an entity record, which compounds in value every cycle you keep it.
If you’d rather not script the read steps yourself, several commercial entity and rank-tracking platforms now expose kgmid lookups, sameAs auditing and cross-engine entity monitoring — our best link building and SEO tools hub tracks which of them are worth the licence. The build-versus-buy line is simple: the raw APIs are free and fine for periodic self-audits, while the paid tools earn their keep when you’re monitoring many entities continuously and want the alerting and history handled for you. Either way the method is identical — the tools just automate the loop, they don’t replace the thinking.
7. What a real audit typically turns up
Run this loop across enough entities and the findings cluster into a handful of recurring shapes. Naming them helps you triage fast. Take an anonymised composite — a UK B2B SaaS firm, established brand, healthy traffic — whose team was baffled that AI answers kept describing them with a competitor’s feature set. The loop found it in twenty minutes.
Finding 1 — the stale description. Google’s KG description still carried positioning the company had moved on from two years earlier. Nothing was “wrong” in a way a backlink tool would flag; the record was simply out of date, and every AI summary inherited the old framing. Fix: update the entity home and Wikidata, then earn a few fresh mentions stating the current positioning.
Finding 2 — the incomplete sameAs. The Wikidata item existed but linked only the website and LinkedIn — no Companies House ID, no Crunchbase, no second-language profile. The graph had fewer threads to reconcile the brand’s scattered mentions, weakening confidence. Fix: complete the sameAs web with sourced external IDs.
Finding 3 — the quiet collision. “People also search for” and the related-entity set included a similarly named US product. The semantic pass caught what an exact-match diff missed: the two entities were being partially merged, which is how a competitor’s feature ended up attributed to them. Fix: hard disambiguation — distinct schema, distinct Wikidata facts, and corroboration that ties the name unambiguously to their sector.
None of those three are exotic, and none would surface in a standard SEO audit. That is the point of reading the record directly: the most consequential entity problems are usually invisible from the dashboards teams already watch, and obvious the moment you pull the JSON. The fixes, notice, all route back through the three influence levers — there is no fourth, magic lever.
8. Where this breaks in production
Everything above works on a good day. Here is what goes wrong on the others, and how to defend against each.
Rate limits and quotas. The KG Search API has request quotas and is explicitly not built for production-critical traffic. Auditing one brand is fine; looping over thousands will hit limits. Defence: cache aggressively, batch your runs, and move relational needs to Wikidata dumps rather than hammering the live endpoint.
Schema drift. The APIs change under you. The legacy KG Search API’s resultScore field, for instance, was removed in the Enterprise migration, and field shapes differ between the Basic and Advanced editions. Defence: never assume a field exists — use .get() with defaults (as the samples above do), validate response shape, and pin which edition you’re calling in your reproducibility notes.
Empty retrievals. A query can legitimately return nothing — no entity, no kgmid — and naive code that indexes the first result will crash. An empty return is data, not an error: it usually means Google holds no distinct entity for you yet. Defence: handle the empty case explicitly and route it to the Influence step.
Hallucinated discrepancies. If you use the LLM pass, an over-eager model will invent contradictions to seem useful. Defence: the verbatim-quote guardrail shown above — every claimed discrepancy must quote the exact source wording — plus a deterministic pre-filter so the model only ever sees genuinely ambiguous fields.
PII and person-entities. Auditing an organisation is low-risk — the data is public and corporate. Auditing people (founders, executives, authors) means handling personal data, which under UK GDPR carries real obligations around purpose and proportionality. Defence: keep person-entity audits scoped to public professional facts, store the minimum, and never use this tooling to compile dossiers on private individuals. The line is public entity hygiene, not surveillance.
Treating the read-only record as writable. The most expensive mistake of all, and the one this whole article is built to prevent: assuming you can edit Google directly. You can’t. Every fix routes through Wikidata, your site or corroboration, then waits. Build that latency into your expectations and your reporting.
9. Frequently asked questions
Can I edit my Google Knowledge Panel through the API?
No. The Knowledge Graph Search API is read-only; there is no write endpoint. (Claiming a panel through the official verification flow lets you suggest edits to a panel you’ve claimed, but that’s a separate, manual channel — not API access to the graph.) Influence runs through Wikidata, your entity home and corroboration.
Do I need a Wikidata item to have a Google entity?
Not strictly, but it’s the highest-leverage writable input you have, because it feeds the graph in clean, machine-readable form. If you can meet Wikidata’s notability bar with reliable sources, creating and maintaining a well-sourced item is usually the single most effective influence move available.
Is the Knowledge Graph Search API being shut down?
Not shut down, but migrated. The legacy endpoint still works for backward compatibility while Google steers new projects to Cloud Enterprise Knowledge Graph. For audit work the legacy API is fine; for anything you’d run at scale or in production, build on the Enterprise product.
How does any of this connect to ordinary link building?
Directly. The corroboration that makes entity facts stick is earned mentions and links — so the influence layer of entity SEO and the output of a good link-building programme are the same thing. The APIs just let you measure whether that work is landing where the machines can see it. For the wider 2026 data on how these signals interact, our link building statistics roundup keeps the benchmarks in one place.
How long before edits show up in the Google record?
There’s no fixed timeline, and it varies by lever. Your own schema changes can be recrawled within days; Wikidata edits may reflect in the wider graph in days or weeks; earned-corroboration effects are the slowest, often a quarter or more, because Google waits for a pattern of independent agreement before it moves a fact. Plan and report on the slowest lever, not the fastest, or you’ll declare failure before the corroboration has had time to land.
Should I use the legacy API or the Enterprise Knowledge Graph?
For a one-off or periodic audit, the legacy KG Search API is the quickest way in and entirely adequate. The moment you want reliability, higher request volumes, type-restricted queries or anything resembling production use, build on Cloud Enterprise Knowledge Graph instead — it’s where Google is steering new development and where the newer features live. Pin whichever you use in your reproducibility notes, because response shapes differ between them.
Run the loop once and the abstract becomes concrete fast: you’ll have your kgmid, a diff of everything the machines currently get wrong about you, and a short, ordered list of writable fixes. That’s the whole value of treating your entity record as something you read and engineer, rather than something that merely happens to you — and in a search environment that increasingly rewards entities it can trust, it’s some of the highest-leverage technical SEO work available.
This article describes auditing public entity records and editing open data sources within their terms; it is not a route to manipulating search results, and the ethical lines drawn throughout are load-bearing, not decorative.
