Triple
T12534600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orthanc |
E299654
|
entity |
| Predicate | supportsFeature |
P203
|
FINISHED |
| Object | DICOMweb STOW-RS |
E697185
|
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: DICOMweb STOW-RS | Statement: [Orthanc, supportsFeature, DICOMweb STOW-RS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DICOMweb STOW-RS Context triple: [Orthanc, supportsFeature, DICOMweb STOW-RS]
-
A.
DICOMweb STOW-RS
chosen
DICOMweb STOW-RS is a web-based DICOM service that enables clients to store medical imaging studies and related data over HTTP using RESTful APIs.
-
B.
DICOMweb
DICOMweb is a web-based standard for accessing, storing, and exchanging medical imaging data over HTTP using RESTful services.
-
C.
Orthanc
Orthanc is an open-source, lightweight DICOM server and ecosystem designed for medical imaging storage, retrieval, and integration in healthcare and research environments.
-
D.
Orthanc
Orthanc is the black, indestructible tower of Isengard in J.R.R. Tolkien’s Middle-earth, serving as Saruman’s stronghold and seat of power.
-
E.
DICOM Part 10: Media Storage and File Format
DICOM Part 10: Media Storage and File Format is the section of the DICOM standard that defines how medical images and related data are encapsulated, structured, and stored in files for exchange and archiving across different systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d9546d3b2081908d3e0659f8f13678 |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65577e3388190b6c1c2e8dee7f6ac |
completed | May 2, 2026, 7:50 p.m. |
Created at: April 8, 2026, 9:57 p.m.