Triple

T3381716
Position Surface form Disambiguated ID Type / Status
Subject Berlin Brandenburg Airport E71200 entity
Predicate locatedIn P40 FINISHED
Object Schönefeld E180745 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: Schönefeld | Statement: [Berlin Brandenburg Airport, locatedIn, Schönefeld]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Schönefeld
Context triple: [Berlin Brandenburg Airport, locatedIn, Schönefeld]
  • A. Schönefeld chosen
    Schönefeld is a municipality just southeast of Berlin in the German state of Brandenburg, known for hosting the Berlin Brandenburg Airport.
  • B. Tegel
    Tegel is a locality in the Reinickendorf borough of Berlin, Germany, historically known for its manor associated with the Humboldt family and later for the former Berlin Tegel Airport.
  • C. Berlin Brandenburg Airport
    Berlin Brandenburg Airport is the main international airport serving Germany’s capital region, designed to replace and consolidate Berlin’s former commercial airports.
  • D. Tempelhof Airport
    Tempelhof Airport is a historic Berlin airfield best known as a central hub of the Berlin Airlift during the Cold War.
  • E. Leipzig/Halle Airport
    Leipzig/Halle Airport is a major international airport in eastern Germany that serves the cities of Leipzig and Halle and functions as an important cargo and passenger hub.
  • 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_69ad85a8fd9c819095ecedf838d2bf1b completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb5e9af608190bfb228ef99a87bb7 completed March 8, 2026, 5:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b37e5353ec819089d2736493936c46 completed March 13, 2026, 3:02 a.m.
Created at: March 8, 2026, 3:14 p.m.