Triple

T23136895
Position Surface form Disambiguated ID Type / Status
Subject Ministry of Health (Brazil) E577346 entity
Predicate hasAbbreviation P43 FINISHED
Object MS
MS is the official abbreviation for Brazil’s federal Ministry of Health, the government body responsible for national public health policy and the Unified Health System (SUS).
E1574859 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: [Ministry of Health (Brazil), hasAbbreviation, MS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MS
Context triple: [Ministry of Health (Brazil), hasAbbreviation, 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 official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • D. MS
    MS is a postgraduate Master of Science degree typically focused on advanced study and research in scientific or technical disciplines.
  • E. MS
    MS is the two-letter IATA airline designator assigned to EgyptAir, the flag carrier of Egypt.
  • 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: [Ministry of Health (Brazil), hasAbbreviation, MS]
Generated description
MS is the official abbreviation for Brazil’s federal Ministry of Health, the government body responsible for national public health policy and the Unified Health System (SUS).
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: MS
Target entity description: MS is the official abbreviation for Brazil’s federal Ministry of Health, the government body responsible for national public health policy and the Unified Health System (SUS).
  • A. MS chosen
    MS is the commonly used abbreviation for Brazil’s Ministry of Health, the federal government body responsible for national public health policy and services.
  • B. MS
    MS is the official vehicle registration code for the Brazilian state of Mato Grosso do Sul, whose capital is Campo Grande.
  • C. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • D. MS
    MS is the vehicle registration code used on license plates for vehicles registered in Târgu Mureș, a city in Romania.
  • E. MS
    MS is a postgraduate medical degree focused on advanced surgical training and specialization for doctors.
  • F. None of above.

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_69e245f8e6248190ba3d58e068b4dccb completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18e8c33308190a44f98a7aab3b670 completed April 29, 2026, 4:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c3f3d44a8819086dc22a62313de0b completed May 19, 2026, 10:45 a.m.
NEDg Description generation batch_6a0c3fec592c8190a8d8ae47f310edd7 completed May 19, 2026, 10:48 a.m.
NED2 Entity disambiguation (via description) batch_6a0c4064012c819083b2ffe795226ab6 completed May 19, 2026, 10:50 a.m.
Created at: April 17, 2026, 4 p.m.