Triple

T28872070
Position Surface form Disambiguated ID Type / Status
Subject DICOM Transfer Syntax E732166 entity
Predicate hasExample P1259 FINISHED
Object Explicit VR Little Endian
Explicit VR Little Endian is a DICOM transfer syntax that encodes data elements with explicit value representations and uses little-endian byte ordering for storing medical imaging information.
 E1840348 NE FINISHED

How this triple was built (2 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: Explicit VR Little Endian | Statement: [DICOM Transfer Syntax, hasExample, Explicit VR Little Endian]
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: Explicit VR Little Endian
Triple: [DICOM Transfer Syntax, hasExample, Explicit VR Little Endian]
Generated description
Explicit VR Little Endian is a DICOM transfer syntax that encodes data elements with explicit value representations and uses little-endian byte ordering for storing medical imaging information.

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_69f05b06807c81909b4bbd4c20403a2b completed April 28, 2026, 7 a.m.
NER Named-entity recognition batch_69f65a47793481908efd891e27e541bf completed May 2, 2026, 8:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24d3fabe5c819088a22bd89d3a0c0c completed June 7, 2026, 2:14 a.m.
NEDg Description generation batch_6a24d9370ef48190845aa485c0356b6f completed June 7, 2026, 2:36 a.m.
NED2 Entity disambiguation (via description) batch_6a24dd68154481909a11f3fa37288d2c completed June 7, 2026, 2:54 a.m.
Created at: April 28, 2026, 7:33 a.m.