Triple

T13088158
Position Surface form Disambiguated ID Type / Status
Subject Interflug E310390 entity
Predicate shortName P43 FINISHED
Object Interflug E310390 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: Interflug | Statement: [Interflug, shortName, Interflug]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Interflug
Context triple: [Interflug, shortName, Interflug]
  • A. Interflug chosen
    Interflug was the state-owned national airline of East Germany, operating international and domestic flights primarily within the Eastern Bloc during the Cold War.
  • B. 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.
  • C. 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.
  • D. Crossair
    Crossair was a former Swiss regional airline that served as the main predecessor to Swiss International Air Lines after the collapse of Swissair.
  • E. Lufthansa
    Lufthansa is Germany’s largest airline and a major global carrier known for its extensive international network and role in shaping modern airline alliances.
  • 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_69d806a733548190989cfd4ce981ca33 completed April 9, 2026, 8:05 p.m.
NER Named-entity recognition batch_69d981378dd08190b4f00e4e5df0e480 completed April 10, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e27417308190b388be4a31ce4b5d completed May 3, 2026, 5:51 a.m.
Created at: April 9, 2026, 9:02 p.m.