Triple

T2229934
Position Surface form Disambiguated ID Type / Status
Subject Lufthansa E48740 entity
Predicate subsidiary P258 FINISHED
Object Eurowings E72036 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: Eurowings | Statement: [Lufthansa, subsidiary, Eurowings]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eurowings
Context triple: [Lufthansa, subsidiary, Eurowings]
  • A. Eurowings chosen
    Eurowings is a German low-cost airline and Lufthansa subsidiary that operates short- and long-haul flights across Europe and selected international destinations.
  • B. Lufthansa CityLine
    Lufthansa CityLine is a German regional airline and Lufthansa subsidiary that operates short- and medium-haul routes across Europe, primarily feeding traffic into Lufthansa’s main hubs.
  • C. S7 Airlines
    S7 Airlines is a major Russian airline based in Novosibirsk that operates extensive domestic and international routes, particularly across Russia, Europe, and Asia.
  • D. Wizz Air
    Wizz Air is a Hungarian ultra-low-cost airline known for operating an extensive network of budget flights across Europe and surrounding regions.
  • E. Transavia
    Transavia is a Dutch low-cost airline operating scheduled and charter flights across Europe and North Africa.
  • 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_69a88aa51b388190949868ec9766e587 completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc069e0ac8190bcda8cba9f5c7a5d completed March 7, 2026, 6:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6b001ee481909b28aea25ad7b906 completed March 9, 2026, 6:38 a.m.
Created at: March 4, 2026, 7:47 p.m.