Triple

T15736249
Position Surface form Disambiguated ID Type / Status
Subject Tempelhof E381479 entity
Predicate hasLandmark P105 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: [Tempelhof, hasLandmark, Tempelhofer Feld]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tempelhofer Feld
Context triple: [Tempelhof, hasLandmark, 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. Spandauer Forst
    Spandauer Forst is a large forest and nature reserve in the northwest of Berlin, known for its rich biodiversity, wetlands, and extensive network of walking trails.
  • 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_69d86d9cdb648190bf3171be0bd7d872 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fd586a88190aa1b1b88368d386f completed April 16, 2026, 2:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff8300a4248190ba52573b57f31b36 completed May 9, 2026, 6:54 p.m.
Created at: April 10, 2026, 4:46 a.m.