Triple

T129362
Position Surface form Disambiguated ID Type / Status
Subject Fürth E2619 entity
Predicate hasLandmark P105 FINISHED
Object Grüner Markt
Grüner Markt is a central marketplace and public square in the Bavarian city of Fürth, known for its local vendors and historic urban setting.
 E14685 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: Grüner Markt | Statement: [Fürth, hasLandmark, Grüner Markt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Grüner Markt
Context triple: [Fürth, hasLandmark, Grüner Markt]
  • A. Luitpoldhain
    Luitpoldhain is a large park in Nuremberg, Germany, historically known for its use as a major site for Nazi Party rallies and now serving as a public recreational and event space.
  • B. Vondelpark
    Vondelpark is Amsterdam’s largest and most famous urban park, known for its expansive green spaces, ponds, and cultural events.
  • C. Schöneberg
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • D. Rathaus Schöneberg
    Rathaus Schöneberg is a historic town hall in Berlin best known as the site of John F. Kennedy’s famous 1963 “Ich bin ein Berliner” speech.
  • E. Roter Turm
    Roter Turm is a historic medieval tower and prominent architectural landmark in the city center of Chemnitz, Germany.
  • 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: Grüner Markt
Triple: [Fürth, hasLandmark, Grüner Markt]
Generated description
Grüner Markt is a central marketplace and public square in the Bavarian city of Fürth, known for its local vendors and historic urban setting.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Grüner Markt
Target entity description: Grüner Markt is a central marketplace and public square in the Bavarian city of Fürth, known for its local vendors and historic urban setting.
  • A. Luitpoldhain
    Luitpoldhain is a large park in Nuremberg, Germany, historically known for its use as a major site for Nazi Party rallies and now serving as a public recreational and event space.
  • B. Vondelpark
    Vondelpark is Amsterdam’s largest and most famous urban park, known for its expansive green spaces, ponds, and cultural events.
  • C. Schöneberg
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • D. Rathaus Schöneberg
    Rathaus Schöneberg is a historic town hall in Berlin best known as the site of John F. Kennedy’s famous 1963 “Ich bin ein Berliner” speech.
  • E. Roter Turm
    Roter Turm is a historic medieval tower and prominent architectural landmark in the city center of Chemnitz, Germany.
  • 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_69a2520c0f3481908b0ed054a2fca8d0 completed Feb. 28, 2026, 2:25 a.m.
NER Named-entity recognition batch_69a2576518e0819096b35d8af7a4d1bd completed Feb. 28, 2026, 2:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2a3d5c7bc8190ba8b36db41a1a57f completed Feb. 28, 2026, 8:14 a.m.
NEDg Description generation batch_69a2a4f0e7c081908966789fd07e59a2 completed Feb. 28, 2026, 8:18 a.m.
NED2 Entity disambiguation (via description) batch_69a2a58757d081908c7c5469c485f1cc completed Feb. 28, 2026, 8:21 a.m.
Created at: Feb. 28, 2026, 2:30 a.m.