Triple

T18453945
Position Surface form Disambiguated ID Type / Status
Subject Medical Council of India E450855 entity
Predicate shortName P43 FINISHED
Object MCI
MCI was the former statutory body responsible for establishing and maintaining high standards of medical education and registration of medical practitioners in India.
 E1325880 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: MCI | Statement: [Medical Council of India, shortName, MCI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MCI
Context triple: [Medical Council of India, shortName, MCI]
  • A. MCI
    MCI is a major commercial airport serving the Kansas City metropolitan area in Missouri, United States.
  • B. MCI-Shirley
    MCI-Shirley is a medium- and minimum-security state prison for men located in Shirley, Massachusetts.
  • C. MCA
    MCA is the UK government executive agency responsible for maritime safety, search and rescue coordination, and preventing pollution from ships in UK waters.
  • D. MCA
    MCA was a major American record label and entertainment company known for signing prominent artists and producing a wide range of popular music releases.
  • E. MCA
    MCA is a postgraduate professional degree in computer applications that focuses on advanced software development, programming, and IT skills.
  • 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: MCI
Triple: [Medical Council of India, shortName, MCI]
Generated description
MCI was the former statutory body responsible for establishing and maintaining high standards of medical education and registration of medical practitioners in India.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MCI
Target entity description: MCI was the former statutory body responsible for establishing and maintaining high standards of medical education and registration of medical practitioners in India.
  • A. MCI
    MCI is a major commercial airport serving the Kansas City metropolitan area in Missouri, United States.
  • B. MCI-Shirley
    MCI-Shirley is a medium- and minimum-security state prison for men located in Shirley, Massachusetts.
  • C. MCA
    MCA is the UK government executive agency responsible for maritime safety, search and rescue coordination, and preventing pollution from ships in UK waters.
  • D. MCA
    MCA was a major American record label and entertainment company known for signing prominent artists and producing a wide range of popular music releases.
  • E. MCA
    MCA is a postgraduate professional degree in computer applications that focuses on advanced software development, programming, and IT skills.
  • 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_69d8d38345688190b565eac2e4cd7935 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5264a49ec8190aa43381d93a55e91 completed April 19, 2026, 7 p.m.
NED1 Entity disambiguation (via context triple) batch_6a040fe5eb1081909ecdf53f4b6b9370 completed May 13, 2026, 5:45 a.m.
NEDg Description generation batch_6a043a81cd58819089df50804870952e completed May 13, 2026, 8:46 a.m.
NED2 Entity disambiguation (via description) batch_6a043c0580048190afd9fe46e6863918 completed May 13, 2026, 8:53 a.m.
Created at: April 10, 2026, 11:31 a.m.