AI-BRIDGES/wikidata-search-traces

Wikidata-Search-Traces is an open corpus of 10,235 reasoning trajectories over Wikidata. In each one, an agent answers a question by writing Python in a REPL: it searches entities, follows relations, reads qualifiers and references, keeps intermediate results in variables, and returns an answer checked exactly against the graph.

Wikidata describes more than 120 million entities in over 300 languages, and AI systems have no good way to explore it. SPARQL takes expertise and pasting graph data into a context window degrades as the question grows. Recursive Language Models (RLMs; Zhang, Kraska & Khattab, 2026) fit this task better: the model explores the graph in code and calls itself on the parts that matter. Training on trajectories from a larger model turns a small model into an effective RLM, but no such trajectories existed for a knowledge graph. This corpus provides them.

Composition

Category	In short	Question types	Example	Traces
Single-entity	Simple lookup around one node	A property's value (entity, year or quantity), a qualifier, a reference, the preferred value, the label in French, German, Chinese, Arabic or Russian, every value of a property, how many entities point at it	The Japanese submarine I-53 was launched during World War II, but do you know exactly when that happened? → 1942	9,563
Multi-hop	Long-horizon search through the graph	The target never appears by name; a chain of facts about unnamed entities identifies it. The question asks for the target, a year, a quantity, how many entities point at it, the list of those entities, or the one with the highest or lowest value	A cave in Lebanon is located within a district belonging to a specific governorate that shares a border with the South Governorate. Which cave is this? → Afqa Grotto	672

A single-entity trace takes 1 turn at the median; a multi-hop trace takes 10, up to 27. Every answer is an entity (QID), a year, a quantity, a number, a string or a list of entities, so you can score a model exactly, with no judge. The graph is a frozen Wikidata dump of February 2026 (48 M entities, 262 M edges).

Each row holds the question, the answer format, the expected answer, the model and the conversation in chat format.

Processing

1. Seeds. We sample seed entities evenly across popular, moderate and rare parts of the graph.
2. Building complex questions. Starting from the seed, a model expands through its neighbouring nodes and grows a tree of facts that leads back to the seed without naming it. That tree becomes the question.
3. Natural question. A model converts the tree into a natural question using a persona from Nemotron-Personas-USA, so the questions vary in tone and style.
4. Traces. A model answers each question inside our RLM harness. We keep only the traces that reach the expected answer.

Evaluation

We evaluated five systems on 100 questions built with the pipeline: 50 single-entity and 50 multi-hop.

• Single-entity types: language 6, neighbour 5, profile 5, qualifier 6, rank 5, reference 6, value_date 5, value_item 6, value_quantity 6.
• Multi-hop types: identity 24, year 12, quantity 7, count 4, group 3.
• Scoring: exact match after canonicalisation, with no judge. Entities must match as QIDs. Quantities must match both the amount and the unit label, with null when the value has no unit.

Systems

System	Model	Setup
Qwen3.8-27B + RLM	Qwen3.8-27B (FP8, served with vLLM on one H100)	RLM harness
gpt-6-luna + RLM	gpt-6-luna	RLM harness
glm-5.3-flash agent	glm-5.3-flash	tool-calling agent
gpt-6-luna agent	gpt-6-luna	tool-calling agent
gemini-3.1-flash-lite agent	gemini-3.1-flash-lite	tool-calling agent

• Shared: every system uses the same 13 graph functions over the same frozen graph, with low reasoning effort. Every system gets the same instruction: "The question has an answer. Do not stop until you find one."
• RLM harness: the model writes Python in a REPL and keeps intermediate results in variables. It can call itself on sub-problems. Temperature 1, up to 100 turns and 80 sub-calls maximum.
• Tool-calling agents: openai-agents with LiteLLM. They call the graph functions directly, with tool output truncated at 10,000 characters, up to 100 calls and $0.50 per question maximum.

Results

System	Single-entity	Multi-hop	Overall	Cost / question	Tokens / question (median)	Seconds / question (median)
Qwen3.8-27B + RLM	46/50	28/50	74/100	$0.04 ¹	35,754	25 ²
gpt-6-luna + RLM	42/50	19/50	61/100	$0.013	24,942	18
glm-5.3-flash agent	37/50	13/50	50/100	$0.067	30,478	33
gpt-6-luna agent	40/50	9/50	49/100	$0.005	24,924	16
gemini-3.1-flash-lite agent	31/50	10/50	41/100	$0.19	460,426	104

For the API systems, cost is input tokens × input price + output tokens × output price, with prices from LiteLLM's model price table. For every model, reasoning tokens are counted as output tokens, in both the token counts and the costs.

¹ Estimated cost of batched serving on an H100 in Google Colab, about 40 compute units in total. ² Processing one question at a time.

License

Facts from Wikidata (CC0 1.0). Question style from NVIDIA Nemotron-Personas-USA (CC BY 4.0); no persona text appears in a question.

The traces were generated with gemini-3.1-flash-lite: we provide them for demonstration purposes of our harness design.