Triple

T10450741
Position Surface form Disambiguated ID Type / Status
Subject Berliner Bezirk Spandau E246414 entity
Predicate hasPart P35 FINISHED
Object Staaken E379226 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: Staaken | Statement: [Berliner Bezirk Spandau, hasPart, Staaken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Staaken
Context triple: [Berliner Bezirk Spandau, hasPart, Staaken]
  • A. Staaken chosen
    Staaken is a locality in western Berlin, Germany, known for its residential areas and historical role as part of the Spandau district near the former inner-German border.
  • B. Steenbergen
    Steenbergen is a municipality and town in the Dutch province of North Brabant, known for its rural landscape and proximity to several major waterways.
  • C. Steenbergen
    Steenbergen is a small village located in the municipality of Noordenveld in the Dutch province of Drenthe.
  • D. Molenstad
    Molenstad is the Dutch nickname for the town of Winschoten, referring to its notable association with windmills.
  • E. Langerak
    Langerak is a small village in the Dutch province of South Holland, known for its rural character and location along the Lek River.
  • 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_69d381c04fe08190957c26c526a3b05a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4fe0a6a548190a54212912f618e4e completed April 7, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69d87efedf6c8190aa4b7bbe5f160eeb completed April 10, 2026, 4:39 a.m.
Created at: April 6, 2026, 12:17 p.m.