Triple

T2486860
Position Surface form Disambiguated ID Type / Status
Subject Secretariat of Health (Mexico) E55946 entity
Predicate shortName P43 FINISHED
Object SSA
SSA is the commonly used acronym for Mexico’s federal Secretariat of Health, the government ministry responsible for national public health policy and services.
 E271235 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: SSA | Statement: [Secretariat of Health (Mexico), shortName, SSA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SSA
Context triple: [Secretariat of Health (Mexico), shortName, SSA]
  • A. SSA
    SSA is a professional scientific organization dedicated to advancing the study and understanding of earthquakes and seismic phenomena.
  • B. SSA
    SSA is the U.S. federal agency responsible for administering Social Security programs, including retirement, disability, and survivors benefits.
  • C. SAA
    SAA is the ICAO airline designator for South African Airways, the flag carrier airline of South Africa.
  • D. SAS
    SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
  • E. SAS
    SAS is a major Scandinavian airline group that provides passenger and cargo air transport services primarily across Europe and to intercontinental destinations.
  • 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: SSA
Triple: [Secretariat of Health (Mexico), shortName, SSA]
Generated description
SSA is the commonly used acronym for Mexico’s federal Secretariat of Health, the government ministry responsible for national public health policy and services.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SSA
Target entity description: SSA is the commonly used acronym for Mexico’s federal Secretariat of Health, the government ministry responsible for national public health policy and services.
  • A. SSA
    SSA is the U.S. federal agency responsible for administering Social Security programs, including retirement, disability, and survivors benefits.
  • B. SSA
    SSA is a professional scientific organization dedicated to advancing the study and understanding of earthquakes and seismic phenomena.
  • C. SAA
    SAA is the ICAO airline designator for South African Airways, the flag carrier airline of South Africa.
  • D. SAS
    SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
  • E. SAS
    SAS is a major Scandinavian airline group that provides passenger and cargo air transport services primarily across Europe and to intercontinental destinations.
  • 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_69ab49e670a88190b928e08302381710 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd1782ca081909645164a6acf0ea0 completed March 7, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69af17ba433481908eccda5c6c6246be completed March 9, 2026, 6:55 p.m.
NEDg Description generation batch_69af1bbf545081908e1e51b5c7e4a196 completed March 9, 2026, 7:13 p.m.
NED2 Entity disambiguation (via description) batch_69af1c9c35648190bdeab34ce4032d26 completed March 9, 2026, 7:16 p.m.
Created at: March 6, 2026, 9:45 p.m.