Triple
T15027949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SUNY Upstate Medical University |
E378264
|
entity |
| Predicate | offersDegree |
P49
|
FINISHED |
| Object | MD |
E450854
|
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: MD | Statement: [SUNY Upstate Medical University, offersDegree, MD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MD Context triple: [SUNY Upstate Medical University, offersDegree, MD]
-
A.
MD
MD is the station code used to identify Maitland railway station in New South Wales, Australia.
-
B.
MD
MD is the official two-letter United States Postal Service abbreviation used to designate the state of Maryland.
-
C.
MD
chosen
MD is a postgraduate medical degree focused on advanced clinical training and specialization for physicians.
-
D.
MD
MD is the vehicle registration code used on license plates for the Austrian district of Mödling.
-
E.
MA
MA is the stock ticker symbol for Mastercard Incorporated, a leading global payments and financial services company.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e0e8c88190ac6f5786b4d4040f |
completed | April 15, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dd967588190821cf47e9734db21 |
completed | May 9, 2026, 2:37 a.m. |
Created at: April 10, 2026, 2:58 a.m.