Triple

T875093
Position Surface form Disambiguated ID Type / Status
Subject Stockholm Arlanda Airport E18898 entity
Predicate hasLounge P105 FINISHED
Object SAS Lounge E48743 NE 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: SAS Lounge | Statement: [Stockholm Arlanda Airport, hasLounge, SAS Lounge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SAS Lounge
Context triple: [Stockholm Arlanda Airport, hasLounge, SAS Lounge]
  • A. SAS
    SAS is the School of Arts and Sciences at the University of Pennsylvania, encompassing the university’s core liberal arts and sciences departments and programs.
  • B. SAS
    SAS is a widely used statistical software suite for advanced analytics, business intelligence, data management, and predictive modeling.
  • C. SAS
    SAS is a high-speed, point-to-point serial interface standard commonly used to connect enterprise storage devices like hard drives and solid-state drives to servers.
  • D. SAS
    SAS is an elite special forces unit of the British Army renowned for its covert operations, counterterrorism expertise, and rigorous selection process.
  • E. SAS Ireland chosen
    SAS Ireland is an Irish-based airline subsidiary of Scandinavian Airlines (SAS) that operates flights within Europe as part of the Star Alliance network.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69a4938db1f081909bcd1ad2713b6096 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4acae12948190923d31966c26a130 completed March 1, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7b8520a008190a15bdb93e8ce2438 completed March 4, 2026, 4:42 a.m.
Created at: March 1, 2026, 7:39 p.m.