Triple

T5355756
Position Surface form Disambiguated ID Type / Status
Subject Baumwerder E102686 entity
Predicate locatedNear P294 FINISHED
Object Tegel E2522 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: Tegel | Statement: [Baumwerder, locatedNear, Tegel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tegel
Context triple: [Baumwerder, locatedNear, Tegel]
  • A. Tegel chosen
    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.
  • B. Schönefeld
    Schönefeld is a municipality just southeast of Berlin in the German state of Brandenburg, known for hosting the Berlin Brandenburg Airport.
  • C. Tempelhof Airport
    Tempelhof Airport is a historic Berlin airfield best known as a central hub of the Berlin Airlift during the Cold War.
  • D. Tempelhof
    Tempelhof is a historic district in Berlin, Germany, best known for the former Tempelhof Airport and its vast airfield now used as a public park.
  • E. Berlin Schönefeld Airport
    Berlin Schönefeld Airport was Berlin’s former secondary international airport located southeast of the city, which was eventually incorporated into the new Berlin Brandenburg Airport complex.
  • 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_69bd43d8f7248190b64c140734b5c9a8 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd862f0ea48190bec78690ab3bee51 completed March 20, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf21df856c819099cf9047b87d6db8 completed March 21, 2026, 10:55 p.m.
Created at: March 20, 2026, 2:01 p.m.