Triple

T3381696
Position Surface form Disambiguated ID Type / Status
Subject Berlin Tegel Airport E71199 entity
Predicate servedCity P3936 FINISHED
Object Oranienburg E217564 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: Oranienburg | Statement: [Berlin Tegel Airport, servedCity, Oranienburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oranienburg
Context triple: [Berlin Tegel Airport, servedCity, Oranienburg]
  • A. Oranienburg chosen
    Oranienburg is a town in Brandenburg, Germany, historically known as the site of the Nazi Sachsenhausen concentration camp.
  • B. Köthen
    Köthen is a town in the German state of Saxony-Anhalt, historically known as the residence of the Princes of Anhalt and as a significant center of Baroque music, including Johann Sebastian Bach’s tenure there.
  • C. Dessau
    Dessau is a German city best known for its association with the Bauhaus movement and its iconic modernist architecture.
  • D. Eilenburg
    Eilenburg is a small historic town in the German state of Saxony, situated on the Mulde River northeast of Leipzig.
  • E. Neustrelitz
    Neustrelitz is a town in northeastern Germany known for hosting a key research center of the German Aerospace Center (DLR), particularly focused on satellite data and space-related technologies.
  • 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_69bd567a48b881908e4bfec70943eb60 completed March 20, 2026, 2:15 p.m.
Created at: March 8, 2026, 3:14 p.m.