Triple

T11337596
Position Surface form Disambiguated ID Type / Status
Subject Secretariat of Energy (Mexico) E268509 entity
Predicate shortName P43 FINISHED
Object SENER
SENER is Mexico’s federal government ministry responsible for national energy policy, including the regulation and development of the country’s oil, gas, and electricity sectors.
 E918381 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: SENER | Statement: [Secretariat of Energy (Mexico), shortName, SENER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SENER
Context triple: [Secretariat of Energy (Mexico), shortName, SENER]
  • A. SANEF
    SANEF is a major French motorway concession and operating company responsible for managing and maintaining several toll highways in northern and eastern France.
  • B. SERNANP
    SERNANP is Peru’s national authority responsible for managing and conserving the country’s system of protected natural areas.
  • C. Senesky
    Senesky is a surname most notably associated with George Senesky, an American professional basketball player and coach in the mid-20th century.
  • D. SEV
    SEV is the National Rail station code for Sevenoaks railway station in Kent, England.
  • E. SENTRI
    SENTRI is a U.S. Customs and Border Protection trusted traveler program that provides expedited processing for pre-approved, low-risk travelers entering the United States at land border crossings.
  • 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: SENER
Triple: [Secretariat of Energy (Mexico), shortName, SENER]
Generated description
SENER is Mexico’s federal government ministry responsible for national energy policy, including the regulation and development of the country’s oil, gas, and electricity sectors.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SENER
Target entity description: SENER is Mexico’s federal government ministry responsible for national energy policy, including the regulation and development of the country’s oil, gas, and electricity sectors.
  • A. SANEF
    SANEF is a major French motorway concession and operating company responsible for managing and maintaining several toll highways in northern and eastern France.
  • B. SERNANP
    SERNANP is Peru’s national authority responsible for managing and conserving the country’s system of protected natural areas.
  • C. Senesky
    Senesky is a surname most notably associated with George Senesky, an American professional basketball player and coach in the mid-20th century.
  • D. SEV
    SEV is the National Rail station code for Sevenoaks railway station in Kent, England.
  • E. SENTRI
    SENTRI is a U.S. Customs and Border Protection trusted traveler program that provides expedited processing for pre-approved, low-risk travelers entering the United States at land border crossings.
  • 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_69d6aacb1f0881908c84a349fd1be047 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7ea008b5081908e6c6c6fc29ef936 completed April 9, 2026, 6:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5264174d481908db15fb9f644f21d completed April 19, 2026, 7 p.m.
NEDg Description generation batch_69e52c84518881909e6e2a593348a81a completed April 19, 2026, 7:27 p.m.
NED2 Entity disambiguation (via description) batch_69e531d23ba481909de04fc49ccd9f1e completed April 19, 2026, 7:49 p.m.
Created at: April 8, 2026, 9:33 p.m.