annotation/annotation-guidelines.md

# TempBench: Human Annotation Guidelines

**Version 1.1** | **Revised: 10 August 2026** _(v1.0 April 2026 described a
different, much larger round — see §1 for the current scope.)_

> **EN / IT — this packet is bilingual.** An Italian translation is included as
> `annotation-guidelines-IT.md`. Work from whichever you prefer — both raters
> receive both. **In case of any discrepancy, this English version governs**,
> and the label values (`VALID`, `INVALID`, `AMB`, `NO_DATE`, …) stay in English
> in both, because they are typed verbatim into the spreadsheet.
>
> _Questo pacchetto è bilingue: la traduzione italiana è in
> `annotation-guidelines-IT.md`. In caso di discrepanza prevale la versione
> inglese._

---

## 1. Introduction and Task Overview

### What is TempBench?

TempBench is a benchmark dataset for evaluating temporal multi-hop reasoning
over evolving knowledge graphs. The full benchmark holds **8,710 questions**
built from Wikidata (the tkgl-smallpedia subset), stratified by reasoning
complexity — 1-hop (single relation), 2-hop (two-relation chain) and 3+-hop
(three or more) — and split 70/10/20 into train/dev/test.

**You are annotating a 120-question sample of the test split**, not the whole
benchmark. The sample is stratified across complexity levels and the four
temporal-operator types, and it deliberately excludes every question used in an
earlier validation round.

Each question is grounded in a specific temporal query point (t_query), requiring systems to retrieve the correct answer as it existed at that precise moment in time. This differs from static QA benchmarks by demanding both multi-hop reasoning capability and temporal validity awareness.

### Your Annotation Task

You are being asked to **quality-check the benchmark questions**, not to create them. Your role is to verify that each question is well-formed, answerable, and correctly labeled. Specifically, you will make two independent judgments for each question:

1. **Question Validity** — Is this a clear, unambiguous question with a verifiable correct answer?
2. **Temporal Validity** — Is the temporal query point (t_query) correct and consistent with the question?

This quality-checking step is critical. Even small errors in the benchmark (ambiguous questions, incorrect timestamps, or mislabeled hop counts) can introduce noise that degrades both baseline performance and downstream analysis. Your 120 judgments, compared against the other rater's, are what let the paper report an honest reliability estimate for the benchmark's labels.

### Time Estimates and Workload

> **Scope of *this* round: 120 questions.** _(An earlier version of this
> document described a 1,500-question / ~125-hour programme. That was the
> original plan and it is **not** what you are being asked to do.)_

Your file contains **120 questions**: 50 one-hop, 50 two-hop, 20 three-or-more-hop.

Annotation time varies by complexity:

- **1-hop questions:** ~2–3 minutes each → 50 items ≈ 2 hours
- **2-hop questions:** ~4–5 minutes each → 50 items ≈ 4 hours
- **3+-hop questions:** ~7–10 minutes each → 20 items ≈ 3 hours

**Total: roughly 9 hours of annotation, plus about an hour to read this
document — call it 10 hours.** It splits comfortably into two or three
sittings; there is no requirement to do it in one pass, and rushing it defeats
the point.

**Payment:** this is **unpaid**. There is no per-question rate and no
agreement-linked bonus — deliberately, since paying for agreement would bias
exactly the number we are trying to measure.

**What you receive instead:** a named acknowledgement in the paper (CIKM 2026,
Rome). To be direct about why it is an acknowledgement rather than authorship —
the conference has frozen the author list at the accepted version and no
additions are permitted at camera-ready, so authorship is not something we are
able to offer, whatever the merits. If you would rather not be named, say so
and we will thank you anonymously instead.

**How your work is used:** the two raters' labels are compared to produce an
inter-annotator agreement statistic (Cohen's κ). The paper will state plainly
that both raters are non-authors, that the passes were blind and independent,
and that the work was uncompensated.

---

## 2. Annotation Interface

**There is no web portal — you work in a spreadsheet.** You have been sent one
workbook, `rater_A_validation_120.xlsx` or `rater_B_validation_120.xlsx`
(120 rows; the two files contain the **same items in the same order**).

Open it in Excel, LibreOffice or Google Sheets, fill one row per question, and
**return it as `.xlsx`** — please keep the workbook format rather than
exporting to CSV or pasting into another tool. The judgment columns are
dropdowns, and saving to CSV throws them away; the labels then come back
free-typed and have to be normalised by hand before they can be compared.

> **⚠️ Fill in the `Annotation` sheet, not the `Instructions` sheet.** The
> workbook has two tabs; the second one is the data. Grey columns are given —
> please leave them as they are. Yellow columns are yours. The judgment columns
> are dropdowns, so pick from the list rather than typing, which keeps the
> labels machine-readable.

> **Work independently.** The two raters have identical files on purpose: the
> whole point of this round is to measure how far two people agree *without*
> conferring. Please do not discuss items with the other rater, and do not
> compare files, until both are returned.

Each row arrives with these fields pre-populated:

| Column | Content |
|---|---|
| `row` | 1–120, your working order |
| `question_id` | Unique identifier, e.g. `q_004664` |
| `question` | The natural-language question — this is what you evaluate |
| `t_query` | The temporal reference point the question is asked "as of" |
| `stated_answer` | The answer the benchmark records — you judge whether it is right |
| `stated_hops` | The hop count the benchmark claims: `1`, `2` or `3+`. **This is what you check for `WRONG_HOP`** — count the relations the question actually requires and compare |
| `supporting_chain` | The gold evidence chain, one line per hop, as `subject -[relation]-> object (years)`. This is what the answer is supposed to follow from — use it to verify `stated_answer` and to count hops |

> **Two notes on reading the worked examples in §4.**
> 1. They print the chain in a **compact one-line notation**
>    (`a:REL→b:REL→c`) because they were written before the spreadsheet
>    existed. Your file shows the *same information* one hop per line, with
>    validity years. A 1-hop cell looks like this:
>
>    ```
>    Daniel Kahneman -[employer]-> University of British Columbia (1983)
>    ```
>
>    and a 2-hop cell like this — **two lines in the one cell**, the object of
>    the first hop becoming the subject of the second:
>
>    ```
>    Leo Königsberger -[doctoral student]-> Karl Bopp (1902)
>    Karl Bopp -[doctoral advisor]-> Moritz Cantor (1902)
>    ```
>
>    Count the lines to check `stated_hops`. If a cell looks like one line when
>    you expect more, widen the row — Excel hides the rest.
> 2. The examples also show a **"Supporting Facts"** row of explanatory prose.
>    **Your file does not have one** — the chain is your evidence. Wherever
>    these guidelines say to check something "against the supporting facts",
>    read it as **against `supporting_chain`**.

**Columns you fill in:**

1. `judgment_A_validity` — `VALID` or `INVALID`
2. `error_code_if_invalid` — if INVALID: `AMB`, `UNANS`, `WRONG_HOP`, `UNNATURAL`, or `WRONG_ANS`
3. `judgment_B_temporal` — `CORRECT`, `WRONG_DATE`, or `NO_DATE`
4. `naturalness_1_to_5` — how much the question reads like something a person
   would actually write. Judge the **wording only**, never whether the answer is
   right. All five points are anchored:

   | | |
   |---|---|
   | **1** | Barely parseable; you had to re-read it to work out what is being asked |
   | **2** | Understandable but clearly machine-made — stacked possessives, robotic template phrasing |
   | **3** | Awkward but usable; a person might write this in a hurry |
   | **4** | Reads naturally, with a small stiffness or oddity |
   | **5** | Indistinguishable from a question a person would ask |

   Long is not the same as unnatural: a genuinely complex 3-hop question can be
   a 4 or 5 if the phrasing is clean.
5. `low_confidence` — put `y` if unsure, leave blank otherwise
6. `notes` — optional free text for edge cases

Unsure about an item? Mark `low_confidence = y`, add a note, and move on. **Do
not agonise** — flagged items are reviewed separately, and a fast honest
"unsure" is more useful than a slow guess.

---

## 2a. No AI assistance — please read, this one matters

**Do not use ChatGPT, Claude, Copilot, Gemini, or any other AI assistant to
produce or check these labels.** Not for the judgments, not for looking up
whether a fact is true, not for drafting notes.

This is not a general policy preference. This benchmark exists to measure
whether AI systems retrieve time-valid evidence correctly. If AI output is used
to validate it, the validation measures the same system it is meant to check,
and the result is worthless — the paper would be reporting a machine agreeing
with itself. A human judgment that is uncertain is *far* more valuable here than
a confident machine one.

Searching Wikipedia or Wikidata directly is fine and encouraged. Using a
**search engine** is fine. Asking a **chatbot** is not.

**Please confirm in your reply when you return the file:**

> "I completed this annotation without using AI assistants, and the judgments
> are my own."

Two practical notes so this is not just an honour system:
- A small number of items in your file are **known-bad questions included
  deliberately** as attention checks. They are there to confirm the task was
  engaged with, not to catch you out.
- Please **note roughly when you start and finish** each working session (a
  line in your reply is enough — "Tue evening, ~1h, rows 1–40"). Per-item timing
  helps distinguish careful work from fast work, and protects you as much as it
  protects the data.

---

## 3. Core Annotation Task

Your annotation task involves two independent but complementary judgments. Complete them in this order for each question.

### Judgment A: Question Validity

Ask yourself: **Is this a well-formed, answerable question that requires the stated number of hops and has a unique, verifiable correct answer at t_query?**

**VALID** — Select this if:

- The question is grammatically clear and unambiguous
- The question asks for a single, well-defined answer (not multiple possible answers)
- The stated hop count matches the complexity of the reasoning required
- The gold answer is correct given the supporting KG chain at the stated t_query
- The question does not rely on present-day information that was not true at t_query

**INVALID** — Select one of these error codes if the question fails any of the above criteria:

| Error Code | Definition | Example |
|------------|-----------|---------|
| **AMB** | **Ambiguous** — Multiple valid answers exist at t_query | "Who was the leader of Germany in 2010?" (could be Merkel but her title varied; too vague) |
| **UNANS** | **Unanswerable** — No answer exists in Wikidata at t_query | "What was the stock price of Company X on 1980-01-01?" (historical price data not reliably in Wikidata) |
| **WRONG_HOP** | **Incorrect hop count label** — The actual reasoning required differs from the stated label | Question labeled "2-hop" but only requires 1 hop to answer |
| **UNNATURAL** | **Grammatically unnatural or incomprehensible** — The question is difficult to parse or uses awkward phrasing | "What did person X have entity Y relation-wise in 2005?" (unclear, ungrammatical) |
| **WRONG_ANS** | **Gold answer is incorrect** — The provided answer does not match the KG chain or does not satisfy the question | Question asks "Who won?" but gold answer lists the runner-up |

For each question, select the **primary error code** — the most significant issue. (A question may have multiple problems; flag the most critical one.)

### Judgment B: Temporal Validity

Ask yourself: **Is the stated t_query correct and consistent with what the question is asking?**

**CORRECT** — Select this if:

- The t_query timestamp matches the temporal anchor in the question (e.g., "in 2008" → t_query = 2008-XX-XX is reasonable)
- For questions without an explicit year but asking about a state or role (e.g., "Who was CEO in the year X?"), the t_query is set to a valid date in that year
- For "stale facts" (facts that remain valid at present, like "was born in"), the t_query is set appropriately and the answer is correctly retrieved at that point

**WRONG_DATE** — Select this if:

- The t_query does not align with the question's temporal anchor (e.g., question says "in 2015" but t_query = 2010-01-01)
- The timestamp is off by a significant amount (days matter for time-sensitive events, but one month off in historical queries is typically acceptable; use judgment)
- The t_query is set after a fact became invalid (e.g., asking about someone's role as CEO at a date after they left office)

**NO_DATE** — Select this if:

- The question has no temporal anchor at all and should not be in a temporal benchmark (e.g., "Who founded Company X?" with no time reference)
- The t_query is missing or unpopulated (data entry error)

---

## 4. Worked Examples

The following six detailed examples (two per complexity level) illustrate correct and incorrect annotations.

### Example 1a: 1-Hop VALID

| Field | Value |
|-------|-------|
| **Question ID** | tgb-001-1hop-001 |
| **Question** | Who was the mayor of Paris in 2010? |
| **t_query** | 2010-06-15 |
| **Stated Hop Count** | 1 |
| **Gold Answer** | Bertrand Delanoë |
| **Supporting KG Chain** | paris:MAYOR→bertrand_delanoë (2001-2014) |
| **Supporting Facts** | Bertrand Delanoë served as Mayor of Paris from 2001 to 2014. |

**Annotation Decision:**

- **Judgment A (Validity):** `VALID`
  - The question is clear and unambiguous.
  - It asks for a single entity (the mayor) at a specific time.
  - The hop count is correct: 1 hop (paris → mayor → person).
  - The gold answer (Bertrand Delanoë) is correct at t_query.
  - The question is natural and answerable.

- **Judgment B (Temporal Validity):** `CORRECT`
  - The question specifies 2010; t_query is set to June 2010.
  - Bertrand Delanoë was mayor throughout 2010 (served 2001–2014).
  - The timestamp is within the valid range.

---

### Example 1b: 1-Hop INVALID

| Field | Value |
|-------|-------|
| **Question ID** | tgb-001-1hop-002 |
| **Question** | Who is the current mayor of Paris? |
| **t_query** | 2020-01-15 |
| **Stated Hop Count** | 1 |
| **Gold Answer** | Anne Hidalgo |
| **Supporting KG Chain** | paris:MAYOR→anne_hidalgo (2014-present) |
| **Supporting Facts** | Anne Hidalgo became Mayor of Paris in 2014 and is the current mayor. |

**Annotation Decision:**

- **Judgment A (Validity):** `INVALID` — Error Code: `AMB`
  - The question uses "is the current mayor," a *relative* temporal anchor — it points at "now", not at a fixed historical point.
  - Which "now"? Read against `t_query` (2020) the answer is Anne Hidalgo; read as "today" it is whoever holds the office when the question is asked. **The question admits more than one defensible answer**, which is exactly the `AMB` criterion.
  - **Note the code carefully.** This is *not* `UNNATURAL`: the sentence is perfectly grammatical and reads like something a person would say. `UNNATURAL` is for wording a fluent speaker would not produce. Judge the two separately — a question can be fluent and ambiguous at once, and this one is.
  - **Fix:** Reword as "Who was the mayor of Paris in 2020?" to pin the temporal reference.

- **Judgment B (Temporal Validity):** `CORRECT`
  - `t_query` = 2020 is a valid date and Anne Hidalgo was indeed mayor then, so the *timestamp* is right.
  - **The two judgments are independent.** A question can fail Judgment A and still pass Judgment B — record what you actually see in each, and do not let one verdict drag the other along.

---

### Example 2a: 2-Hop VALID

| Field | Value |
|-------|-------|
| **Question ID** | tgb-002-2hop-001 |
| **Question** | Who was the spouse of the CEO of Microsoft in 1998? |
| **t_query** | 1998-06-15 |
| **Stated Hop Count** | 2 |
| **Gold Answer** | Melinda Gates |
| **Supporting KG Chain** | microsoft:HAS_CEO→bill_gates:SPOUSE→melinda_gates |
| **Supporting Facts** | Bill Gates was CEO of Microsoft from 1975 to 2000. Bill Gates married Melinda French in 1994 and remained married through 1998. |

**Annotation Decision:**

- **Judgment A (Validity):** `VALID`
  - The question is clear and asks for a single, well-defined answer.
  - Hop count is correct: 2 hops.
  - Gold answer is correct: Melinda Gates was married to Bill Gates, who was CEO in 1998.

- **Judgment B (Temporal Validity):** `CORRECT`
  - t_query=1998-06-15 aligns with the question's "in 1998."
  - Bill Gates was CEO at this time, and Melinda was his spouse.

---

### Example 2b: 2-Hop INVALID

| Field | Value |
|-------|-------|
| **Question ID** | tgb-002-2hop-002 |
| **Question** | What country was the birthplace of the spouse of the CEO of Apple in 2005? |
| **t_query** | 2005-06-15 |
| **Stated Hop Count** | 2 |
| **Gold Answer** | United States |
| **Supporting KG Chain** | apple:HAS_CEO→steve_jobs:SPOUSE→laurene_powell:BIRTHPLACE→united_states |
| **Supporting Facts** | Steve Jobs was CEO of Apple in 2005. Steve Jobs married Laurene Powell in 1991. Laurene Powell was born in the United States. |

**Annotation Decision:**

- **Judgment A (Validity):** `INVALID` — Error Code: `WRONG_HOP`
  - The question asks for the birthplace of Steve Jobs' spouse.
  - The chain is: Apple → CEO (Steve Jobs) → Spouse (Laurene Powell) → Birthplace (US).
  - This is actually **3 hops**, not 2 hops as labeled.
  - The stated hop count is incorrect.

- **Judgment B (Temporal Validity):** `CORRECT`
  - t_query=2005-06-15 aligns with the question's temporal anchor (2005).
  - Steve Jobs was CEO at this time.

---

### Example 3a: 3+-Hop VALID

| Field | Value |
|-------|-------|
| **Question ID** | tgb-003-3hop-001 |
| **Question** | In what year was the employer of the winner of the 1992 Nobel Prize in Physics founded? |
| **t_query** | 1992-12-10 |
| **Stated Hop Count** | 3+ |
| **Gold Answer** | 1954 |
| **Supporting KG Chain** | nobel_physics_1992:AWARDED_TO→georges_charpak:EMPLOYER→cern:INCEPTION→1954 |
| **Supporting Facts** | Georges Charpak was the sole recipient of the 1992 Nobel Prize in Physics. He worked at CERN. CERN was founded in 1954. |

**Annotation Decision:**

- **Judgment A (Validity):** `VALID`
  - The question is long but grammatically sensible and asks for one well-defined answer (a year).
  - **The chain answers the question that was actually asked.** Trace it: prize → laureate → employer → founding year. Each hop is one the question requires, and the endpoint (1954) is the gold answer.
  - `stated_answer` (1954) is exactly where the chain ends. **Check this every time** — if the stated answer and `supporting_chain` disagree, the code is `WRONG_ANS`, however plausible the answer looks on its own.
  - The laureate is unambiguous: 1992 Physics was a sole award. **Had the prize been shared**, "the winner" would admit more than one referent and the question would be `AMB`.
  - Hop count 3+ is right: three relations are traversed.

- **Judgment B (Temporal Validity):** `CORRECT`
  - `t_query` = 1992-12-10 matches the question's anchor (1992).
  - Note the anchor fixes *which laureate* is being asked about, not the founding year — CERN's founding date does not change with `t_query`.

---

### Example 3b: 3+-Hop INVALID

| Field | Value |
|-------|-------|
| **Question ID** | tgb-003-3hop-002 |
| **Question** | What is the nationality of the child of the colleague of the mentor of the researcher who published a paper on topic X in 2015? |
| **t_query** | 2015-06-15 |
| **Stated Hop Count** | 3+ |
| **Gold Answer** | [Not clearly specified] |
| **Supporting KG Chain** | [Complex, unclear chain with multiple undefined relations] |
| **Supporting Facts** | A researcher published on topic X in 2015... [details vague] |

**Annotation Decision:**

- **Judgment A (Validity):** `INVALID` — Error Code: `UNNATURAL`
  - The question is extremely convoluted and difficult to parse.
  - The relations are not well-defined (what counts as a "colleague"? how many levels of mentorship?).
  - The gold answer is vague or unclear.
  - While technically there might be a chain of reasoning, the question is not naturally answerable without significant clarification.

- **Judgment B (Temporal Validity):** `CORRECT` (but moot given the INVALID judgment)
  - t_query aligns with the question's year (2015), but this is a secondary concern.

---

## 5. Edge Cases and Decision Rules

This section codifies rules for specific edge cases you are likely to encounter.

### Entity Aliases

**Situation:** A question refers to an entity by one name (e.g., "Deutsche Bank") but the KG uses a different alias (e.g., "Deutsche Bank AG").

**Rule:** Treat these as the **same entity**. Do not mark as INVALID for this reason. Aliases are normal in Wikidata and knowledge graphs. If the supporting chain clearly refers to the same entity, the question is valid.

**Example:**
- Question: "Who was the CEO of Deutsche Bank in 2010?"
- KG Chain uses: "Deutsche Bank AG"
- **Judgment:** VALID — the names refer to the same organization.

---

### Implicit Timestamps

**Situation:** A question includes a temporal reference that is not a specific date (e.g., "during the financial crisis," "after the Cold War," "in the 1990s").

**Rule:** Check whether the t_query is **reasonable and consistent** with the implicit temporal anchor.

- If t_query falls clearly within the historical period referenced, mark as `CORRECT`.
- If t_query is before or well after the period, mark as `WRONG_DATE`.
- If the implicit anchor is too vague to pin to a specific date (e.g., "in recent times"), mark as `NO_DATE`.

**Example:**
- Question: "Who was the CEO of Lehman Brothers at the onset of the financial crisis?"
- t_query: 2008-08-15
- The financial crisis began in earnest in September 2008, so August 15 is slightly early but reasonable (the crisis had been building). Mark as `CORRECT`.

---

### Currently-Valid Facts (Stale Facts)

**Situation:** A fact that remains valid "up to the present" (e.g., "Barack Obama was born in 1961"). The KG marks this with `t_end = present` or `t_end = null`.

**Rule:** Such facts are valid for **any t_query up to the knowledge cutoff of
the source graph, which is 2024** (the benchmark is built from a Wikidata
snapshot covering 1900–2024). These are "stale facts" — once true, always true
(e.g. birth dates, historical events).

- If t_query is at or before 2024 and the fact is recorded as ongoing, mark `CORRECT`.
- If t_query is after 2024, the graph cannot vouch for the fact — mark `WRONG_DATE`.

> **A note on the term.** "Stale fact" here means *permanently valid* — an
> unfortunate name, and elsewhere in this project the same phrase means the
> opposite (a fact that *was* true but is out of date at the query time). In
> this document it always means the permanent kind.

**Example:**
- Question: "In what year was Malala Yousafzai born?"
- t_query: 2015-01-01 (or any past date)
- Gold answer: 1997
- KG fact: born 1997-07-12 (marked as permanent/unchanging)
- **Judgment:** VALID and `CORRECT` — birth is a stale fact, valid at any t_query.

---

### Multiple Roles Simultaneously

**Situation:** A person held multiple roles at the same time (e.g., "person X was both CEO and Chairman in 2008"). The question asks about one role, and the answer satisfies it.

**Rule:** Mark as **VALID** if the answer correctly satisfies the question, **even if other roles also applied**.

**Example:**
- Question: "Who was the CEO of Company X in 2008?"
- KG shows: person Y was CEO *and* Chairman in 2008.
- Gold answer: person Y
- **Judgment:** VALID — person Y was indeed CEO in 2008, even though they also held another title.

---

### Successor Relationships and Constraints

**Situation:** A question includes a constraint like "before X became Y" or "after Z left position W". The t_query and answer must respect this temporal ordering.

**Rule:** Verify that the t_query **does not violate** the temporal constraint. If it does, mark as `WRONG_DATE`.

**Example:**
- Question: "Who was the CEO of Microsoft before Steve Ballmer became CEO?"
- Constraint: t_query must be before Ballmer's start date (which was January 2000).
- If t_query = 1999-06-15 and gold answer = Bill Gates, this is `CORRECT`.
- If t_query = 2005-06-15 (after Ballmer took over), this is `WRONG_DATE`.

---

### Event-Based vs. State-Based Questions

**Situation:** Some questions ask about events ("When did X win the election?") while others ask about states ("Who was in office in year Y?"). These require different answer types.

**Rule:** Both types are **valid**. Distinguish between them, but do not penalize a state-based question for being state-based, or vice versa.

**Example:**
- Question A: "When did John Smith become mayor of Boston?" (event)
  - Answer type: date
  - This is asking for a specific transition point.

- Question B: "Who was the mayor of Boston in 1990?" (state)
  - Answer type: person name
  - This is asking for a snapshot at a point in time.

Both are valid for a temporal QA benchmark.

---

### Retroactive Facts and Posthumous Awards

**Situation:** A fact is assigned retroactively (e.g., an award given posthumously, a historical reassignment of a role).

**Rule:** These are **edge cases**. Mark the question as `VALID` but **use the free-text notes** to flag the retroactive nature. This helps downstream analysis understand why the chain might appear unusual.

**Example:**
- Question: "Who won the 2005 Fields Medal?" (not a real Fields Medal year, but for illustration)
- Gold answer: mathematician Z (who passed away in 2010)
- This is valid because the answer was determinable at t_query=2005, even if a later commemoration or official recognition occurred posthumously.
- **Judgment:** VALID, and add note: "Retroactive award (if applicable)."

---

### Questions with No Determinable Answer in Wikidata

**Situation:** The question is well-formed and clear, but Wikidata simply does not contain the information needed to answer it at t_query.

**Rule:** Mark as `INVALID` with error code **UNANS** (Unanswerable).

**Example:**
- Question: "What was the closing stock price of Microsoft on January 1, 1990?"
- Wikidata does not reliably contain historical stock price data.
- **Judgment:** INVALID — UNANS. The question is natural and clear, but the data doesn't exist in the KG.

---

## 6. Quality Standards and Agreement

### What the agreement number is for

Your labels and the other rater's are compared to produce an inter-annotator
agreement statistic, which the paper reports as its evidence that the
benchmark's labels are reliable.

**Deliberately not stated here: the agreement level we hope for.** Knowing the
target would give you a reason, however unconscious, to guess at what the other
rater would say rather than record what you actually think — which is precisely
what the statistic is supposed to measure. A genuine disagreement between two
careful readers is a useful result; an agreement manufactured by both of you
aiming at a number is not.

So: judge each item on its own terms, flag uncertainty honestly, and let the
number be whatever it is.

### Double-Annotation Protocol

**Every question in this round is double-annotated.** Both raters receive the
identical 120 items, and the comparison between the two label sets is the
inter-annotator agreement the paper reports. There is no hidden subset and
nothing is singled out for special scrutiny — the whole file is the measurement.

Work at your normal standard throughout. The one thing that matters is that the
judgments are **yours**: please do not discuss items with the other rater or
compare files until both are returned.

### Handling Uncertainty

If you are unsure about a judgment, **do not guess**. Instead:

1. Put `y` in the `low_confidence` column.
2. Write a brief note in the `notes` column explaining what is unclear (e.g., "The supporting chain seems to have two possible endpoints; unclear which is primary").
3. Proceed to the next question.

Uncertain flags are helpful for identifying questions that may need clarification or rewriting. They are **not** penalized; uncertainty is better than confident errors.

### Escalation and Questions

If you encounter a question that you believe has a fundamental error, or if you need clarification on the guidelines, **do not spend excessive time on it**. Put `y` in `low_confidence`, say what is wrong in `notes`, and move on. Flagged rows are reviewed separately.

**For questions during annotation:** reply to the email that sent you this
document. ⚠️ Please note I am **away 11–17 August with no computer**, so
messages in that window will not get an answer until the 18th. That is exactly
why the rule above matters: if something is unclear, mark it
`low_confidence = y`, add a note, and move on rather than waiting on a reply.
Flagged items are reviewed separately and nothing is lost by flagging.

---

## 7. Annotator Checklist

Use this checklist for **each question** before submitting your annotation.

### Pre-Annotation Checklist

- [ ] I have read the question text carefully.
- [ ] I understand what the question is asking (entity type, relation, temporal anchor).
- [ ] I have read `stated_answer` and `supporting_chain`.
- [ ] I have checked whether `supporting_chain` actually leads to `stated_answer`.

### Judgment A: Validity — Checklist

- [ ] The question is grammatically clear and natural.
- [ ] The question asks for a single, well-defined answer (not multiple possible answers).
- [ ] The stated hop count matches the complexity of the reasoning.
- [ ] `stated_answer` is correct according to `supporting_chain`.
- [ ] The question does not depend on present-day information unknown at t_query.
- [ ] If I'm marking INVALID, I've selected the single most significant error code.

### Judgment B: Temporal Validity — Checklist

- [ ] The t_query is populated (not missing or null).
- [ ] The t_query matches the temporal anchor in the question.
- [ ] The gold answer is true/valid at t_query (not before or after).
- [ ] For implicit temporal references (e.g., "during the crisis"), I've verified t_query is reasonable.
- [ ] I've selected one of: CORRECT, WRONG_DATE, or NO_DATE.

### Confidence and Notes Checklist

- [ ] If I was unsure, I put `y` in `low_confidence` (leave blank when confident — there is no middle setting).
- [ ] If I flagged `low_confidence`, I added a note saying what was unclear.
- [ ] If I hit an edge case (retroactive facts, aliases, multiple roles), I documented it in `notes`.

### Per-Question Checklist

- [ ] `judgment_A_validity` filled, plus `error_code_if_invalid` if the verdict was INVALID.
- [ ] `judgment_B_temporal` filled.
- [ ] `naturalness_1_to_5` filled.

### Before You Return the File

- [ ] All 120 rows have Judgment A, Judgment B and a naturalness score.
- [ ] I worked on the **Annotation** sheet, not the Instructions sheet.
- [ ] I did not confer with the other rater.
- [ ] My reply includes the no-AI confirmation sentence (§2a) and a rough note of when I worked.

---

## 8. Glossary

This section defines key terms used throughout the guidelines.

**Anchor Entity**
The initial entity in a multi-hop question from which relations are traversed. In "Who was the spouse of the CEO of Microsoft in 1998?", the anchor entity is Microsoft.

**Composability**
The property that each hop in a multi-hop chain builds on the previous result. A 3-hop question is composable if each intermediate result (1-hop, 2-hop) is a valid and retrievable entity.

**Gold Answer** — in your spreadsheet this is the column `stated_answer`. The
two terms mean the same thing; "gold answer" is the phrase these guidelines use
in prose, `stated_answer` is the column header. Note the column name says
*stated*, not *correct*: judging whether it **is** correct is your task.

The correct, ground-truth answer to a question, as verified by the KG chain. For example, if the question is "Who was the CEO of Apple in 2010?", the gold answer is "Steve Jobs."

**Hop Count**
The number of relations traversed in the KG to answer a question. A 1-hop question requires traversing one edge. A 2-hop question requires two edges. Multi-hop reasoning chains are more complex and require composing multiple steps.

**Inter-Annotator Agreement (IAA)**
A statistical measure of how often two independent annotators make the same judgment. Higher agreement indicates the benchmark's labels are clear and unambiguous.

**Multi-Hop Chain**
A sequence of relations in the knowledge graph that together answer a question. For example: Apple → (HAS_CEO) → Steve Jobs → (HAS_SPOUSE) → Laurene Powell is a 2-hop chain ending in the spouse's name.

**Stale Fact**
A fact that, once established, does not change over time — birth dates,
founding dates, historical events. Valid for any t_query after the fact
occurred **and at or before the graph's 2024 cutoff** (see §5, which also
explains why the name is misleading: here it means *permanently valid*, the
opposite of what "stale" means elsewhere in this project).

**Supporting Chain**
The path through the knowledge graph (Wikidata) that justifies the gold answer. This is shown to you for verification and should match the question and t_query.

**Supporting Facts**
Explanatory prose that appears in the §4 worked examples only. **It is not a
column in your file** — your evidence is `supporting_chain`. Where these
guidelines say "supporting facts", read `supporting_chain`.

**t_query**
The temporal query point — a specific timestamp (e.g., 2008-06-15) at which the question asks for the answer. This is the "slice in time" being queried.

**Temporal Knowledge Graph**
A knowledge graph where facts are associated with temporal validity windows (t_start, t_end). Unlike static KGs, temporal KGs explicitly track when facts are true, enabling time-aware reasoning.

**Temporal Validity Window**
The time range during which a fact is true. For example, "Steve Jobs was CEO of Apple" has a temporal window from 1997 to 2011 (approximately). Answers must be checked against this window.

**Unambiguous Answer**
A single, well-defined answer to a question that does not have competing alternatives at t_query. For example, "Who was the only mayor of Boston in 1995?" should have a unique answer (or clearly none, if there was an interregnum).

**Validity**
The property that a question is well-formed, clear, answerable, and grounded in the benchmark KG. Valid questions have unambiguous answers and correct hop counts.

**Wikidata**
A large, multilingual, community-edited knowledge base containing structured facts about entities (people, organizations, events, etc.) and their relationships. TempBench is constructed from a subset called tkgl-smallpedia.

---

## Conclusion

Thank you for carefully annotating these questions. Your work is essential to creating a high-quality benchmark that will be used to evaluate temporal reasoning systems for years to come. Please work systematically, flag uncertainties, and do not hesitate to reach out if you have questions.

**Good luck, and thank you for your diligence!**

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**Document Version:** 1.1 | **Last Updated:** 10 August 2026