Triple

T15057543
Position Surface form Disambiguated ID Type / Status
Subject All India Institute of Medical Sciences Patna E379534 entity
Predicate offersProgram P178 FINISHED
Object MS
MS is a postgraduate medical degree focused on advanced surgical training and specialization for doctors.
 E1135017 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: MS | Statement: [All India Institute of Medical Sciences Patna, offersProgram, MS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MS
Context triple: [All India Institute of Medical Sciences Patna, offersProgram, MS]
  • A. MS
    MS is the official vehicle registration code used on license plates for the German city of Münster.
  • B. MS
    MS is the New York Stock Exchange ticker symbol for Morgan Stanley, a leading global investment bank and financial services firm.
  • C. MS
    MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
  • D. MS
    MS is the station code for Chennai Egmore, one of the major railway terminals in Chennai, India.
  • E. MS
    MS is the official vehicle registration code for the Brazilian state of Mato Grosso do Sul, whose capital is Campo Grande.
  • 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: MS
Triple: [All India Institute of Medical Sciences Patna, offersProgram, MS]
Generated description
MS is a postgraduate medical degree focused on advanced surgical training and specialization for doctors.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MS
Target entity description: MS is a postgraduate medical degree focused on advanced surgical training and specialization for doctors.
  • A. MS
    MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
  • B. MS
    MS is the vehicle registration code used on license plates for vehicles registered in Târgu Mureș, a city in Romania.
  • C. MS
    MS is the New York Stock Exchange ticker symbol for Morgan Stanley, a leading global investment bank and financial services firm.
  • D. MS
    MS is the station code for Main Street station, a transit stop identified by this abbreviated designation.
  • E. MS
    MS is the official vehicle registration code used on license plates for the German city of Münster.
  • 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_69d85cd64d108190853797a95c11cc45 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69deda937f788190899d81bbb2084443 completed April 15, 2026, 12:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5c11fb4819086c4b85a8d29ccf7 completed May 9, 2026, 3:10 a.m.
NEDg Description generation batch_69fea70a10b88190aac09e3b690afa6f completed May 9, 2026, 3:16 a.m.
NED2 Entity disambiguation (via description) batch_69fea82a80888190bbb0de39a8959a6e completed May 9, 2026, 3:21 a.m.
Created at: April 10, 2026, 3:01 a.m.