Triple

T8755315
Position Surface form Disambiguated ID Type / Status
Subject XDS E208058 entity
Predicate hasVariant P455 FINISHED
Object XDS-MS
XDS-MS is a variant of the XDS software package tailored for processing and analyzing macromolecular crystallography diffraction data, often with specialized features or optimizations.
 E208058 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: XDS-MS | Statement: [XDS, hasVariant, XDS-MS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: XDS-MS
Context triple: [XDS, hasVariant, XDS-MS]
  • A. XDS
    XDS (Cross-Enterprise Document Sharing) is an IHE IT Infrastructure profile that defines standards-based methods for registering, storing, and sharing clinical documents across healthcare enterprises.
  • B. BR-MS
    BR-MS is the ISO 3166-2 subdivision code that uniquely identifies the Brazilian state of Mato Grosso do Sul.
  • C. SCIEX
    SCIEX is a leading analytical instrumentation company best known for its mass spectrometry and capillary electrophoresis technologies used in life sciences and analytical laboratories.
  • D. XRF
    XRF is the IATA airport-style code assigned to Liverpool Lime Street railway station in Liverpool, England.
  • E. XDR
    XDR is a high-speed generation of InfiniBand technology designed to significantly increase data transfer bandwidth and performance in interconnect networks.
  • 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: XDS-MS
Triple: [XDS, hasVariant, XDS-MS]
Generated description
XDS-MS is a variant of the XDS software package tailored for processing and analyzing macromolecular crystallography diffraction data, often with specialized features or optimizations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: XDS-MS
Target entity description: XDS-MS is a variant of the XDS software package tailored for processing and analyzing macromolecular crystallography diffraction data, often with specialized features or optimizations.
  • A. XDS chosen
    XDS (Cross-Enterprise Document Sharing) is an IHE IT Infrastructure profile that defines standards-based methods for registering, storing, and sharing clinical documents across healthcare enterprises.
  • B. BR-MS
    BR-MS is the ISO 3166-2 subdivision code that uniquely identifies the Brazilian state of Mato Grosso do Sul.
  • C. SCIEX
    SCIEX is a leading analytical instrumentation company best known for its mass spectrometry and capillary electrophoresis technologies used in life sciences and analytical laboratories.
  • D. XRF
    XRF is the IATA airport-style code assigned to Liverpool Lime Street railway station in Liverpool, England.
  • E. XDR
    XDR is a high-speed generation of InfiniBand technology designed to significantly increase data transfer bandwidth and performance in interconnect networks.
  • 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_69ca835cd6b08190bd7c63db92f53c86 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5dd83088819082cf54adc0c04243 completed March 31, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf43305664819085e762e42b138754 completed April 3, 2026, 4:33 a.m.
NEDg Description generation batch_69cf452b237c8190958f7b42e9611e7b completed April 3, 2026, 4:42 a.m.
NED2 Entity disambiguation (via description) batch_69cf45e6f4108190ac6955264b466abb completed April 3, 2026, 4:45 a.m.
Created at: March 30, 2026, 6:39 p.m.