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.