Triple

T1205979
Position Surface form Disambiguated ID Type / Status
Subject Grunewald forest E25888 entity
Predicate contains P35 FINISHED
Object Teufelsberg
Teufelsberg is an artificial hill in Berlin built from World War II rubble, best known for its former U.S. listening station and panoramic city views.
 E137770 NE FINISHED

How this triple was built (4 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: Teufelsberg | Statement: [Grunewald forest, contains, Teufelsberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Teufelsberg
Context triple: [Grunewald forest, contains, Teufelsberg]
  • A. Alt-Tempelhof
    Alt-Tempelhof is an underground station on Berlin’s U-Bahn network serving the Tempelhof district in the southern part of the city.
  • B. Roter Turm
    Roter Turm is a historic medieval tower and prominent architectural landmark in the city center of Chemnitz, Germany.
  • C. Bad Godesberg
    Bad Godesberg is a district in the city of Bonn, Germany, known for its affluent residential areas, former diplomatic missions, and scenic location along the Rhine River.
  • D. Spandau
    Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
  • E. Tiergarten
    Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Teufelsberg
Triple: [Grunewald forest, contains, Teufelsberg]
Generated description
Teufelsberg is an artificial hill in Berlin built from World War II rubble, best known for its former U.S. listening station and panoramic city views.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Teufelsberg
Target entity description: Teufelsberg is an artificial hill in Berlin built from World War II rubble, best known for its former U.S. listening station and panoramic city views.
  • A. Alt-Tempelhof
    Alt-Tempelhof is an underground station on Berlin’s U-Bahn network serving the Tempelhof district in the southern part of the city.
  • B. Roter Turm
    Roter Turm is a historic medieval tower and prominent architectural landmark in the city center of Chemnitz, Germany.
  • C. Bad Godesberg
    Bad Godesberg is a district in the city of Bonn, Germany, known for its affluent residential areas, former diplomatic missions, and scenic location along the Rhine River.
  • D. Spandau
    Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
  • E. Tiergarten
    Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
  • F. None of above. chosen

Provenance (5 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_69a4942b30f08190a91c60573e16b5ef completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bdc314c88190b1b5953834bfce7b completed March 1, 2026, 10:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac7f3f4c508190bdc3d436d393daf9 completed March 7, 2026, 7:40 p.m.
NEDg Description generation batch_69ac7fc59a488190adbdf156aaff8c03 completed March 7, 2026, 7:43 p.m.
NED2 Entity disambiguation (via description) batch_69ac806a6d748190acc5cdfa8fb90a64 completed March 7, 2026, 7:45 p.m.
Created at: March 1, 2026, 7:46 p.m.