Triple

T8867446
Position Surface form Disambiguated ID Type / Status
Subject Department of Law, Free University of Berlin E211054 entity
Predicate locatedIn P40 FINISHED
Object Dahlem E538658 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: Dahlem | Statement: [Department of Law, Free University of Berlin, locatedIn, Dahlem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dahlem
Context triple: [Department of Law, Free University of Berlin, locatedIn, Dahlem]
  • A. Dahlem chosen
    Dahlem is a district in southwestern Berlin known for its concentration of research institutes, universities, and cultural institutions.
  • B. Wilmersdorf
    Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
  • C. Sorpedamm
    Sorpedamm is a reservoir dam in North Rhine-Westphalia, Germany, primarily used for water supply, flood control, and recreation.
  • D. Charlottenburg
    Charlottenburg is a historic district in western Berlin, Germany, known for its baroque Charlottenburg Palace and role as a former independent city before incorporation into Berlin.
  • E. Munich-Haidhausen
    Munich-Haidhausen is a historic and centrally located district of Munich known for its charming old streets, vibrant cultural scene, and mix of residential and governmental buildings.
  • 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_69ca838d3c7c8190a849566d5afd2b11 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6108530c819084559f4de669ce20 completed April 1, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfa0dafdf48190abc1fe1a8397e339 completed April 3, 2026, 11:13 a.m.
Created at: March 30, 2026, 6:51 p.m.