Triple

T14910946
Position Surface form Disambiguated ID Type / Status
Subject Tukey's range test E371257 entity
Predicate implementedIn P2539 FINISHED
Object SAS
SAS is a widely used statistical software suite for advanced analytics, data management, and business intelligence.
 E5277 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: SAS | Statement: [Tukey's range test, implementedIn, SAS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SAS
Context triple: [Tukey's range test, implementedIn, SAS]
  • A. SAS
    SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
  • B. SAS
    SAS is the standard abbreviation used for the NBA team San Antonio Spurs.
  • C. SAS
    SAS is a major Scandinavian airline group that provides passenger and cargo air transport services primarily across Europe and to intercontinental destinations.
  • D. SAS
    SAS is the common abbreviation for the San Antonio Silver Stars, a former Women’s National Basketball Association (WNBA) team based in San Antonio, Texas.
  • E. SAS
    SAS is the standard abbreviation used for the Canadian Football League team Saskatchewan Roughriders.
  • 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: SAS
Triple: [Tukey's range test, implementedIn, SAS]
Generated description
SAS is a widely used statistical software suite for advanced analytics, data management, and business intelligence.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SAS
Target entity description: SAS is a widely used statistical software suite for advanced analytics, data management, and business intelligence.
  • A. SAS chosen
    SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
  • B. SAS
    SAS is a high-speed, point-to-point serial interface standard commonly used to connect enterprise storage devices like hard drives and solid-state drives to servers.
  • C. SAS
    SAS is a major Scandinavian airline group that provides passenger and cargo air transport services primarily across Europe and to intercontinental destinations.
  • D. SAS
    SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
  • E. SAS
    SAS is the common abbreviation for the San Antonio Silver Stars, a former Women’s National Basketball Association (WNBA) team based in San Antonio, Texas.
  • F. None of above.

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_69d85cc7ea3481908228b5acb7d06f12 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded61c6b9c8190a92934d49b98fe46 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe72bb366481909706d511f5ae1290 completed May 8, 2026, 11:33 p.m.
NEDg Description generation batch_69fe733a580c8190bc2f053188bb7145 completed May 8, 2026, 11:35 p.m.
NED2 Entity disambiguation (via description) batch_69fe75ecce8c8190a879d8f908d9fb28 completed May 8, 2026, 11:46 p.m.
Created at: April 10, 2026, 2:26 a.m.