Triple

T1810090
Position Surface form Disambiguated ID Type / Status
Subject SAS-4 E40311 entity
Predicate predecessor P97 FINISHED
Object SAS-3
SAS-3 was an earlier satellite in NASA’s Small Astronomy Satellite program, dedicated primarily to X-ray astronomy observations of cosmic sources.
 E209470 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-3 | Statement: [SAS-4, predecessor, SAS-3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SAS-3
Context triple: [SAS-4, predecessor, SAS-3]
  • A. SAS-3
    SAS-3 is the third-generation Serial Attached SCSI (SAS) interface standard that significantly increases data transfer rates and performance for enterprise storage systems.
  • B. SAS-1
    SAS-1 is the first generation of the Serial Attached SCSI (SAS) interface standard used for high-speed data transfer between storage devices and host systems.
  • C. SAS-4
    SAS-4 is the fourth-generation Serial Attached SCSI (SAS) interface standard that significantly increases data transfer speeds and bandwidth for enterprise storage systems.
  • D. SAS-2
    SAS-2 was a NASA Small Astronomy Satellite launched in 1972 that conducted pioneering gamma-ray astronomy observations of cosmic sources.
  • E. SAS-2
    SAS-2 is a version of the SAS (Statistical Analysis System) software suite used for advanced data management, statistical analysis, and reporting.
  • 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-3
Triple: [SAS-4, predecessor, SAS-3]
Generated description
SAS-3 was an earlier satellite in NASA’s Small Astronomy Satellite program, dedicated primarily to X-ray astronomy observations of cosmic sources.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SAS-3
Target entity description: SAS-3 was an earlier satellite in NASA’s Small Astronomy Satellite program, dedicated primarily to X-ray astronomy observations of cosmic sources.
  • A. SAS-3
    SAS-3 is the third-generation Serial Attached SCSI (SAS) interface standard that significantly increases data transfer rates and performance for enterprise storage systems.
  • B. SAS-1
    SAS-1 is the first generation of the Serial Attached SCSI (SAS) interface standard used for high-speed data transfer between storage devices and host systems.
  • C. SAS-4
    SAS-4 is the fourth-generation Serial Attached SCSI (SAS) interface standard that significantly increases data transfer speeds and bandwidth for enterprise storage systems.
  • D. SAS-2
    SAS-2 was a NASA Small Astronomy Satellite launched in 1972 that conducted pioneering gamma-ray astronomy observations of cosmic sources.
  • E. SAS-2
    SAS-2 is a version of the SAS (Statistical Analysis System) software suite used for advanced data management, statistical analysis, and reporting.
  • 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_69a88643a3388190a612f2ebe1fb29e7 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa65c461e881908070cb80d9092981 completed March 6, 2026, 5:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69addf3fd6bc81908b762644fb588f0a completed March 8, 2026, 8:42 p.m.
NEDg Description generation batch_69addfcecdf48190a325eb5c8b10f238 completed March 8, 2026, 8:45 p.m.
NED2 Entity disambiguation (via description) batch_69ade0ba34ac8190ac94f7dbb5778f70 completed March 8, 2026, 8:48 p.m.
Created at: March 4, 2026, 7:32 p.m.