Triple
T18787685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MIT course 6.034 Artificial Intelligence |
E459419
|
entity |
| Predicate | coversTopic |
P380
|
FINISHED |
| Object | A* search |
—
|
NE NERFINISHED |
How this triple was built (3 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: A* search | Statement: [MIT course 6.034 Artificial Intelligence, coversTopic, A* search]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: A* search Context triple: [MIT course 6.034 Artificial Intelligence, coversTopic, A* search]
-
A.
Dijkstra
Dijkstra is a renowned Dutch computer scientist best known for his pioneering work in algorithms, including Dijkstra's shortest path algorithm, and for his influential contributions to programming methodology and software engineering.
-
B.
Generalized Search Tree
Generalized Search Tree is a flexible, balanced tree data structure framework that supports building custom index types for complex data and queries, often used in database systems.
-
C.
Dijkstra's shortest path algorithm
Dijkstra's shortest path algorithm is a classic graph algorithm that efficiently computes the minimum-cost paths from a single source vertex to all other vertices in a weighted graph with non-negative edge weights.
-
D.
Monte Carlo tree search
Monte Carlo tree search is a heuristic search algorithm that uses random sampling of game states to build and explore a search tree, enabling strong decision-making in complex domains like Go and other board games.
-
E.
SeARCH
SeARCH is a Dutch architecture and urban design firm known for its innovative, context-sensitive projects that often integrate buildings seamlessly into their natural surroundings.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: A* search Target entity description: A* search is a widely used informed search algorithm in artificial intelligence that finds optimal paths by combining actual path cost with a heuristic estimate of the remaining cost.
-
A.
Dijkstra
Dijkstra is a renowned Dutch computer scientist best known for his pioneering work in algorithms, including Dijkstra's shortest path algorithm, and for his influential contributions to programming methodology and software engineering.
-
B.
Generalized Search Tree
Generalized Search Tree is a flexible, balanced tree data structure framework that supports building custom index types for complex data and queries, often used in database systems.
-
C.
Dijkstra's shortest path algorithm
Dijkstra's shortest path algorithm is a classic graph algorithm that efficiently computes the minimum-cost paths from a single source vertex to all other vertices in a weighted graph with non-negative edge weights.
-
D.
Monte Carlo tree search
Monte Carlo tree search is a heuristic search algorithm that uses random sampling of game states to build and explore a search tree, enabling strong decision-making in complex domains like Go and other board games.
-
E.
SeARCH
SeARCH is a Dutch architecture and urban design firm known for its innovative, context-sensitive projects that often integrate buildings seamlessly into their natural surroundings.
- F. None of above. chosen
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e59783ea4c8190b1b04d08f65b7d19 |
completed | April 20, 2026, 3:03 a.m. |
Created at: April 10, 2026, 11:53 a.m.