Triple

T254212
Position Surface form Disambiguated ID Type / Status
Subject Southwest Airlines E5400 entity
Predicate safetyRecordReputation P753 FINISHED
Object strong safety culture LITERAL FINISHED

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: strong safety culture | Statement: [Southwest Airlines, safetyRecordReputation, strong safety culture]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: safetyRecordReputation
Context triple: [Southwest Airlines, safetyRecordReputation, strong safety culture]
  • A. protectedBy
    Indicates that one entity provides protection, defense, or safeguarding for another entity.
  • B. estimatedStrength
    Indicates that a value represents an approximate or inferred level, magnitude, or intensity of something rather than a precisely measured strength.
  • C. criminalStatus
    Indicates the legal condition of an entity with respect to criminal law, such as whether they are accused, convicted, or cleared of a crime.
  • D. hasCheckAndBalanceWith
    Indicates that two entities mutually monitor, limit, or counterbalance each other's powers or actions to prevent dominance or abuse.
  • E. recognizedFor chosen
    Indicates that one entity is acknowledged, credited, or honored for a particular achievement, quality, contribution, or work associated with another entity.
  • F. None of above.

Provenance (3 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_69a2580a64ac8190ad76e34bb0715b5e completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25d548cac819081b1636b2a057c62 completed Feb. 28, 2026, 3:13 a.m.
PD Predicate disambiguation batch_69a25b678d6c81909780e1995c1ca691 completed Feb. 28, 2026, 3:05 a.m.
Created at: Feb. 28, 2026, 2:55 a.m.