Triple

T9549108
Position Surface form Disambiguated ID Type / Status
Subject McDonnell Douglas DC-9 E230372 entity
Predicate primaryUser P2090 FINISHED
Object SAS
SAS (Scandinavian Airlines System) is the flag carrier of Denmark, Norway, and Sweden, operating as a major airline in Northern Europe.
 E245449 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: [McDonnell Douglas DC-9, primaryUser, SAS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SAS
Context triple: [McDonnell Douglas DC-9, primaryUser, SAS]
  • A. SAS
    SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
  • B. SAS
    SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
  • C. 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.
  • D. SAS
    SAS is the station code for San Antonio railway station.
  • E. SAS
    SAS is the common abbreviation for the San Antonio Scorpions, a former professional soccer team based in San Antonio, Texas.
  • 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: [McDonnell Douglas DC-9, primaryUser, SAS]
Generated description
SAS (Scandinavian Airlines System) is the flag carrier of Denmark, Norway, and Sweden, operating as a major airline in Northern Europe.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SAS
Target entity description: SAS (Scandinavian Airlines System) is the flag carrier of Denmark, Norway, and Sweden, operating as a major airline in Northern Europe.
  • A. SAS chosen
    SAS is a major Scandinavian airline group that provides passenger and cargo air transport services primarily across Europe and to intercontinental destinations.
  • B. SAS
    SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
  • C. SAS
    SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
  • 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 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.
  • 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_69ca847c70b8819088a0a0bad64a50d6 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd99059138819088ae54b26df979cf completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c82c98c8190a4fd6fc3ceb4173d completed April 4, 2026, 5:38 p.m.
NEDg Description generation batch_69d14d23573c8190aeebf2fdac20a332 completed April 4, 2026, 5:40 p.m.
NED2 Entity disambiguation (via description) batch_69d14da4514481908a530b5d77ad832a completed April 4, 2026, 5:43 p.m.
Created at: March 30, 2026, 8:02 p.m.