Triple

T7108567
Position Surface form Disambiguated ID Type / Status
Subject Luisenstädtischer Friedhof E165651 entity
Predicate locatedNear P294 FINISHED
Object Tempelhofer Feld E62023 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: Tempelhofer Feld | Statement: [Luisenstädtischer Friedhof, locatedNear, Tempelhofer Feld]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tempelhofer Feld
Context triple: [Luisenstädtischer Friedhof, locatedNear, Tempelhofer Feld]
  • A. Tempelhofer Feld chosen
    Tempelhofer Feld is a vast public park and former airport in Berlin, Germany, known for its open runways, recreational spaces, and historical significance, including its role in the Berlin Airlift.
  • B. Tegeler Forst
    Tegeler Forst is a large forested area in the Berlin district of Tegel, known for its natural landscapes, walking trails, and recreational opportunities.
  • C. Mauerpark
    Mauerpark is a popular public park and cultural hotspot in Berlin, known for its lively flea market, street performances, and open-air karaoke.
  • D. Englischer Garten
    Englischer Garten is a large public park in Munich, Germany, renowned for its expansive green spaces, beer gardens, and riverside surfing on the Eisbach.
  • E. Tiergarten
    Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
  • 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_69c6888120f081908f8f01b201dc4a4c completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e5bcf3e08190bd8c6cf896c416c4 completed March 27, 2026, 8:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c79cb4840881908196e447618b38b0 completed March 28, 2026, 9:17 a.m.
Created at: March 27, 2026, 2:43 p.m.