Triple

T2516792
Position Surface form Disambiguated ID Type / Status
Subject Secretariat of Foreign Affairs (Mexico) E55430 entity
Predicate shortName P43 FINISHED
Object SRE
SRE is the Spanish acronym for Mexico’s Secretariat of Foreign Affairs, the federal ministry responsible for conducting the country’s international relations and diplomacy.
 E275894 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: SRE | Statement: [Secretariat of Foreign Affairs (Mexico), shortName, SRE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SRE
Context triple: [Secretariat of Foreign Affairs (Mexico), shortName, SRE]
  • A. SOAR
    SOAR is the commonly used abbreviation for the U.S. Army’s elite 160th Special Operations Aviation Regiment, known for its specialized nighttime and low-level aviation missions in support of special operations forces.
  • B. SWE
    SWE is the three-letter ISO 3166-1 alpha-3 country code representing Sweden.
  • C. SDM
    SDM is the ICAO airline designator used to identify Rossiya Airlines in international aviation operations.
  • D. New Relic
    New Relic is a software analytics and application performance monitoring company that provides tools for tracking and optimizing the performance of web and mobile applications.
  • E. SVC
    SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
  • 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: SRE
Triple: [Secretariat of Foreign Affairs (Mexico), shortName, SRE]
Generated description
SRE is the Spanish acronym for Mexico’s Secretariat of Foreign Affairs, the federal ministry responsible for conducting the country’s international relations and diplomacy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SRE
Target entity description: SRE is the Spanish acronym for Mexico’s Secretariat of Foreign Affairs, the federal ministry responsible for conducting the country’s international relations and diplomacy.
  • A. SOAR
    SOAR is the commonly used abbreviation for the U.S. Army’s elite 160th Special Operations Aviation Regiment, known for its specialized nighttime and low-level aviation missions in support of special operations forces.
  • B. SWE
    SWE is the three-letter ISO 3166-1 alpha-3 country code representing Sweden.
  • C. SDM
    SDM is the ICAO airline designator used to identify Rossiya Airlines in international aviation operations.
  • D. New Relic
    New Relic is a software analytics and application performance monitoring company that provides tools for tracking and optimizing the performance of web and mobile applications.
  • E. SVC
    SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
  • 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_69ab49e4749c8190813311efd1630f1b completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd20f8d0c8190bfdcb99a12f59d59 completed March 7, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69af2b9aa5cc81908c2e09ce18f2e98e completed March 9, 2026, 8:20 p.m.
NEDg Description generation batch_69af508c28f48190afc4aa1bc3c9adf3 completed March 9, 2026, 10:58 p.m.
NED2 Entity disambiguation (via description) batch_69af5155f85081908dd4a1859d0f7907 completed March 9, 2026, 11:01 p.m.
Created at: March 6, 2026, 9:46 p.m.