Triple

T4533549
Position Surface form Disambiguated ID Type / Status
Subject Rajiv Gandhi University of Health Sciences E106352 entity
Predicate offersDegree P49 FINISHED
Object MD
MD is a postgraduate medical degree focused on advanced clinical training and specialization for physicians.
 E450854 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: MD | Statement: [Rajiv Gandhi University of Health Sciences, offersDegree, MD]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MD
Context triple: [Rajiv Gandhi University of Health Sciences, offersDegree, MD]
  • A. MD
    MD is the station code used to identify Maitland railway station in New South Wales, Australia.
  • B. MA
    MA is the stock ticker symbol for Mastercard Incorporated, a leading global payments and financial services company.
  • C. MA
    MA is the two-letter ISO 3166-1 alpha-2 country code assigned to Morocco.
  • D. MN
    MN is the vehicle registration code used on license plates for vehicles registered in Douglas and the rest of the Isle of Man.
  • E. D. Md.
    D. Md. is the standard legal abbreviation for the United States District Court for the District of Maryland, a federal trial court within the Fourth Circuit.
  • 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: MD
Triple: [Rajiv Gandhi University of Health Sciences, offersDegree, MD]
Generated description
MD is a postgraduate medical degree focused on advanced clinical training and specialization for physicians.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MD
Target entity description: MD is a postgraduate medical degree focused on advanced clinical training and specialization for physicians.
  • A. MD
    MD is the station code used to identify Maitland railway station in New South Wales, Australia.
  • B. MA
    MA is the stock ticker symbol for Mastercard Incorporated, a leading global payments and financial services company.
  • C. MA
    MA is the two-letter ISO 3166-1 alpha-2 country code assigned to Morocco.
  • D. MN
    MN is the vehicle registration code used on license plates for vehicles registered in Douglas and the rest of the Isle of Man.
  • E. D. Md.
    D. Md. is the standard legal abbreviation for the United States District Court for the District of Maryland, a federal trial court within the Fourth Circuit.
  • F. None of above. chosen

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_69bd43f3d6e08190a91824f833d51bbe completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57a08ec4819091b84d53d7b564a7 completed March 20, 2026, 2:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdacea41bc8190b49c9d1a31d7930f completed March 20, 2026, 8:24 p.m.
NEDg Description generation batch_69bdb220bc6481908955c5c953bbfb97 completed March 20, 2026, 8:46 p.m.
NED2 Entity disambiguation (via description) batch_69bdb2871e3481908d143c52d9e7141f completed March 20, 2026, 8:48 p.m.
Created at: March 20, 2026, 1:04 p.m.