Triple

T10950713
Position Surface form Disambiguated ID Type / Status
Subject EDDH E258717 entity
Predicate operator P179 FINISHED
Object Flughafen Hamburg GmbH E894679 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: Flughafen Hamburg GmbH | Statement: [EDDH, operator, Flughafen Hamburg GmbH]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Flughafen Hamburg GmbH
Context triple: [EDDH, operator, Flughafen Hamburg GmbH]
  • A. Flughafen Hamburg GmbH chosen
    Flughafen Hamburg GmbH is the company responsible for managing and operating Hamburg Airport, one of Germany’s major international airports.
  • B. Flughafen Leipzig/Halle GmbH
    Flughafen Leipzig/Halle GmbH is the company responsible for managing and operating Leipzig/Halle Airport in Germany.
  • C. Mitteldeutsche Flughafen AG
    Mitteldeutsche Flughafen AG is a German airport operating company that manages major airports in the central German region, including Dresden Airport.
  • D. Flughafen Berlin Brandenburg GmbH
    Flughafen Berlin Brandenburg GmbH is the state-owned company responsible for owning, managing, and developing Berlin’s airport infrastructure, including Berlin Brandenburg Airport.
  • E. Berliner Flughafen-Gesellschaft
    Berliner Flughafen-Gesellschaft was the municipal company responsible for operating Berlin’s airports, notably during the Cold War era.
  • 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_69d6aa88500c819097d7032ca578e74f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d770ed2f1c819081ec58457f57889d completed April 9, 2026, 9:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69e2d733f7d88190b45df5c155ff5a46 completed April 18, 2026, 12:58 a.m.
Created at: April 8, 2026, 9:23 p.m.