Triple

T17654074
Position Surface form Disambiguated ID Type / Status
Subject Schwarzer Weg (Tegelort) E429573 entity
Predicate locatedIn P40 FINISHED
Object Tegelort NE NERFINISHED

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: Tegelort | Statement: [Schwarzer Weg (Tegelort), locatedIn, Tegelort]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tegelort
Context triple: [Schwarzer Weg (Tegelort), locatedIn, Tegelort]
  • A. 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.
  • B. 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.
  • C. Teterow
    Teterow is a small historic town in northeastern Germany known for its medieval architecture and location in the Mecklenburg Lake District.
  • D. Prisdorf
    Prisdorf is a small municipality in the district of Pinneberg in Schleswig-Holstein, northern Germany.
  • E. Neubukow
    Neubukow is a small town in northern Germany best known as the birthplace of archaeologist Heinrich Schliemann.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889e2c2608190b762e76d9b2262f1 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46e3ed8b08190a00efdad9740bf6f completed April 19, 2026, 5:55 a.m.
Created at: April 10, 2026, 6:05 a.m.