Triple

T17449851
Position Surface form Disambiguated ID Type / Status
Subject TSA Pre✓ E424885 entity
Predicate relatedProgram P37 FINISHED
Object SENTRI NE NERFINISHED

How this triple was built (2 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: SENTRI | Statement: [TSA Pre✓, relatedProgram, SENTRI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SENTRI
Context triple: [TSA Pre✓, relatedProgram, SENTRI]
  • A. SENTRI chosen
    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.
  • B. 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.
  • C. Tsentaroy
    Tsentaroy is a village in the Chechen Republic of Russia, known as the ancestral home and power base of the Kadyrov family.
  • D. SERNANP
    SERNANP is Peru’s national authority responsible for managing and conserving the country’s system of protected natural areas.
  • E. SATENA
    SATENA is a Colombian state-owned regional airline that primarily serves remote and underserved areas across the country.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889db0ba481908402409af3b37917 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e4513d00f48190802a3bdc8c8f4db5 completed April 19, 2026, 3:51 a.m.
Created at: April 10, 2026, 5:47 a.m.