Triple

T1991731
Position Surface form Disambiguated ID Type / Status
Subject American Airlines Flight 77 E43264 entity
Predicate securityEvent P30019 FINISHED
Object hijacking 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: hijacking | Statement: [American Airlines Flight 77, securityEvent, hijacking]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: securityEvent
Context triple: [American Airlines Flight 77, securityEvent, hijacking]
  • A. hasSecurityEvent chosen
    Indicates that a security-related incident or event is associated with, or has occurred for, a given entity.
  • B. security
    Indicates that an entity provides protection, safety measures, or safeguards to another entity or against specific threats or risks.
  • C. securityEnvironment
    Indicates the overall conditions, threats, and protective measures that characterize the safety and risk context in which an entity operates.
  • D. securityProgram
    Indicates that an entity is associated with, participates in, or is governed by a security program, such as a set of policies, controls, or initiatives related to safety or protection.
  • E. securityFeature
    Indicates that an entity provides, embodies, or is associated with a mechanism or property intended to enhance safety, protection, or defense against threats or vulnerabilities.
  • 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_69a88714cf2c819081644be450b8356e completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8ee02dc81908fec9fd8df7a4f40 completed March 7, 2026, 5:34 a.m.
PD Predicate disambiguation batch_69abb79ad6888190be99943a9c73cf3e completed March 7, 2026, 5:28 a.m.
Created at: March 4, 2026, 7:37 p.m.