Triple

T18266795
Position Surface form Disambiguated ID Type / Status
Subject Hector Levesque E437503 entity
Predicate knownFor P22 FINISHED
Object Winograd Schema Challenge
The Winograd Schema Challenge is an AI benchmark test that evaluates a system’s commonsense reasoning by requiring it to resolve pronoun references in carefully constructed, ambiguous sentences that humans find easy but machines find difficult.
E1315983 NE FINISHED

How this triple was built (4 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: Winograd Schema Challenge | Statement: [Hector Levesque, knownFor, Winograd Schema Challenge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Winograd Schema Challenge
Context triple: [Hector Levesque, knownFor, Winograd Schema Challenge]
  • A. SQuAD 2.0
    SQuAD 2.0 is a widely used reading comprehension benchmark dataset that tests machine learning models’ ability to answer questions from passages while also handling unanswerable queries.
  • B. NETL: A System for Representing and Using Real-World Knowledge
    NETL: A System for Representing and Using Real-World Knowledge is an influential early work in artificial intelligence that introduces a network-based framework for encoding and reasoning about commonsense knowledge.
  • C. “Natural Language Input for a Computer Problem-Solving System”
    “Natural Language Input for a Computer Problem-Solving System” is a seminal research paper in artificial intelligence and computational linguistics that explores how computers can understand and process human language to solve problems.
  • D. Turing test
    The Turing test is a benchmark in artificial intelligence that evaluates a machine's ability to exhibit human-like intelligence by determining whether its responses are indistinguishable from those of a human in conversation.
  • E. “A Question-Answering System for High School Algebra Word Problems”
    “A Question-Answering System for High School Algebra Word Problems” is an early AI research project that automatically interprets and solves algebra word problems in natural language, demonstrating machine understanding and reasoning in mathematics.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Winograd Schema Challenge
Triple: [Hector Levesque, knownFor, Winograd Schema Challenge]
Generated description
The Winograd Schema Challenge is an AI benchmark test that evaluates a system’s commonsense reasoning by requiring it to resolve pronoun references in carefully constructed, ambiguous sentences that humans find easy but machines find difficult.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Winograd Schema Challenge
Target entity description: The Winograd Schema Challenge is an AI benchmark test that evaluates a system’s commonsense reasoning by requiring it to resolve pronoun references in carefully constructed, ambiguous sentences that humans find easy but machines find difficult.
  • A. SQuAD 2.0
    SQuAD 2.0 is a widely used reading comprehension benchmark dataset that tests machine learning models’ ability to answer questions from passages while also handling unanswerable queries.
  • B. NETL: A System for Representing and Using Real-World Knowledge
    NETL: A System for Representing and Using Real-World Knowledge is an influential early work in artificial intelligence that introduces a network-based framework for encoding and reasoning about commonsense knowledge.
  • C. “Natural Language Input for a Computer Problem-Solving System”
    “Natural Language Input for a Computer Problem-Solving System” is a seminal research paper in artificial intelligence and computational linguistics that explores how computers can understand and process human language to solve problems.
  • D. Turing test
    The Turing test is a benchmark in artificial intelligence that evaluates a machine's ability to exhibit human-like intelligence by determining whether its responses are indistinguishable from those of a human in conversation.
  • E. “A Question-Answering System for High School Algebra Word Problems”
    “A Question-Answering System for High School Algebra Word Problems” is an early AI research project that automatically interprets and solves algebra word problems in natural language, demonstrating machine understanding and reasoning in mathematics.
  • F. None of above. chosen

Provenance (5 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_69d8b913351c8190932b6a426de04b41 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4ff7af85c81909859e7247738a535 completed April 19, 2026, 4:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a03b3f6c1b081908fdd0ccb6f1bf633 completed May 12, 2026, 11:12 p.m.
NEDg Description generation batch_6a03b4f0cd188190a9577a3999a5b473 completed May 12, 2026, 11:17 p.m.
NED2 Entity disambiguation (via description) batch_6a03b5c09f4881909fb0ead5fc48895c completed May 12, 2026, 11:20 p.m.
Created at: April 10, 2026, 10:34 a.m.