Triple

T4425296
Position Surface form Disambiguated ID Type / Status
Subject Arcade Learning Environment E95193 entity
Predicate shortName P43 FINISHED
Object ALE
ALE is a widely used research platform that provides a common interface to hundreds of Atari 2600 games for developing and evaluating artificial intelligence and reinforcement learning algorithms.
 E438355 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: ALE | Statement: [Arcade Learning Environment, shortName, ALE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ALE
Context triple: [Arcade Learning Environment, shortName, ALE]
  • A. AL
    AL is the common abbreviation for the American League, one of the two major professional baseball leagues that make up Major League Baseball in the United States and Canada.
  • B. AL
    AL is the official postal abbreviation for the Brazilian state of Alagoas, located in the country's Northeast region.
  • C. AL
    AL is the two-letter ISO 3166 country code representing the Republic of Albania.
  • D. Ale
    Ale is a common short form of the Italian given name Alessandro, often used as a casual or affectionate nickname.
  • E. ARE
    ARE is a professional licensure examination for architects in the United States that assesses candidates’ knowledge and skills required for independent practice.
  • 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: ALE
Triple: [Arcade Learning Environment, shortName, ALE]
Generated description
ALE is a widely used research platform that provides a common interface to hundreds of Atari 2600 games for developing and evaluating artificial intelligence and reinforcement learning algorithms.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ALE
Target entity description: ALE is a widely used research platform that provides a common interface to hundreds of Atari 2600 games for developing and evaluating artificial intelligence and reinforcement learning algorithms.
  • A. AL
    AL is the common abbreviation for the American League, one of the two major professional baseball leagues that make up Major League Baseball in the United States and Canada.
  • B. AL
    AL is the official postal abbreviation for the Brazilian state of Alagoas, located in the country's Northeast region.
  • C. AL
    AL is the two-letter ISO 3166 country code representing the Republic of Albania.
  • D. Ale
    Ale is a common short form of the Italian given name Alessandro, often used as a casual or affectionate nickname.
  • E. ARE
    ARE is a professional licensure examination for architects in the United States that assesses candidates’ knowledge and skills required for independent practice.
  • 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_69b3453c2a0c8190926b574c90766db9 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3554e40ec8190982acc0948da2f42 completed March 13, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69b5f633a69c8190b062c2a78b0f8319 completed March 14, 2026, 11:58 p.m.
NEDg Description generation batch_69b5f6bcfa0481909d07ffb2a975a350 completed March 15, 2026, 12:01 a.m.
NED2 Entity disambiguation (via description) batch_69b5f733c660819081c68dc3ec342e12 completed March 15, 2026, 12:02 a.m.
Created at: March 12, 2026, 11:30 p.m.