Triple

T20634950
Position Surface form Disambiguated ID Type / Status
Subject Saab XWD E507051 entity
Predicate category P87 FINISHED
Object Saab technologies 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: Saab technologies | Statement: [Saab XWD, category, Saab technologies]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saab technologies
Context triple: [Saab XWD, category, Saab technologies]
  • A. Saab AB chosen
    Saab AB is a Swedish aerospace and defense company known for developing military aircraft, advanced defense systems, and security solutions.
  • B. Saab safety concept
    Saab safety concept is the Swedish automaker’s integrated approach to vehicle safety, combining advanced structural design, active and passive protection systems, and innovative features to reduce injuries and enhance occupant protection.
  • C. Saab Kockums
    Saab Kockums is a Swedish shipyard and defense company best known for designing and building advanced submarines and naval vessels.
  • D. Saab Automobile
    Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
  • E. Saab XWD
    Saab XWD is Saab’s advanced all-wheel-drive system designed to enhance traction, handling, and stability in various driving conditions.
  • 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_69e0b4bd4a0081908d4e97a590a33fb2 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6ad0e5fc481909e4f0dd7fb1203fc completed April 20, 2026, 10:47 p.m.
Created at: April 16, 2026, 11:42 a.m.