Triple
T8418841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IHE MHD |
E198796
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | MHD |
E198796
|
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: MHD | Statement: [IHE MHD, hasAbbreviation, MHD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MHD Context triple: [IHE MHD, hasAbbreviation, MHD]
-
A.
MHD
MHD is a French rapper known for pioneering the Afro trap genre, blending hip-hop with African musical influences.
-
B.
HdM
HdM is the commonly used abbreviation for Stuttgart Media University, a German university specializing in media, information, and communication studies.
-
C.
MDC
MDC is a Zimbabwean opposition political party known as the Movement for Democratic Change, which has played a major role in challenging the long-standing rule of ZANU–PF.
-
D.
IHE MHD
chosen
IHE MHD (Mobile Access to Health Documents) is an IHE profile that defines a standardized, RESTful, FHIR-based way to access, query, and exchange clinical documents and metadata in healthcare systems.
-
E.
MDH
MDH is the commonly used abbreviation for the Faculty of Medicine, Dentistry and Health, an academic division focused on education and research in medical, dental and health sciences.
- 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_69ca8312d63c8190bf133b676b44a385 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb84c7d6e48190a2bbde89c5d42af6 |
completed | March 31, 2026, 8:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce03476b288190b3df8f9f4d502ea8 |
completed | April 2, 2026, 5:48 a.m. |
Created at: March 30, 2026, 6:06 p.m.