Triple

T746429
Position Surface form Disambiguated ID Type / Status
Subject Black Forest E15351 entity
Predicate highestPoint P210 FINISHED
Object Feldberg
Feldberg is the tallest mountain in Germany’s Black Forest region, known for its scenic landscapes and popular hiking and skiing opportunities.
E89914 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: Feldberg | Statement: [Black Forest, highestPoint, Feldberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Feldberg
Context triple: [Black Forest, highestPoint, Feldberg]
  • A. Erzhausen
    Erzhausen is a small municipality in the state of Hesse in central Germany, located near Darmstadt and part of the Rhine-Main metropolitan region.
  • B. Brocken
    Brocken is a prominent mountain in central Germany’s Harz range, known for its harsh climate, folklore, and role in literature and cultural history.
  • C. Hallbergmoos
    Hallbergmoos is a municipality in Bavaria, Germany, known for hosting major aerospace and technology companies near Munich.
  • D. Schöngarth
    Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
  • E. Konradshöhe
    Konradshöhe is a leafy, riverside locality in the northwest of Berlin known for its tranquil, village-like character within the borough of Reinickendorf.
  • 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: Feldberg
Triple: [Black Forest, highestPoint, Feldberg]
Generated description
Feldberg is the tallest mountain in Germany’s Black Forest region, known for its scenic landscapes and popular hiking and skiing opportunities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Feldberg
Target entity description: Feldberg is the tallest mountain in Germany’s Black Forest region, known for its scenic landscapes and popular hiking and skiing opportunities.
  • A. Erzhausen
    Erzhausen is a small municipality in the state of Hesse in central Germany, located near Darmstadt and part of the Rhine-Main metropolitan region.
  • B. Brocken
    Brocken is a prominent mountain in central Germany’s Harz range, known for its harsh climate, folklore, and role in literature and cultural history.
  • C. Hallbergmoos
    Hallbergmoos is a municipality in Bavaria, Germany, known for hosting major aerospace and technology companies near Munich.
  • D. Schöngarth
    Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
  • E. Konradshöhe
    Konradshöhe is a leafy, riverside locality in the northwest of Berlin known for its tranquil, village-like character within the borough of Reinickendorf.
  • 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_69a49358aa308190adbc9b5a0a2adcf9 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a62ca1d081908e3191411f86498d completed March 1, 2026, 8:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69a65e4086e481908d0ea29729d92d67 completed March 3, 2026, 4:06 a.m.
NEDg Description generation batch_69a65eeaa34481909b0deac860fbadfc completed March 3, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_69a65f7b09b08190bf38f02a912b2276 completed March 3, 2026, 4:11 a.m.
Created at: March 1, 2026, 7:37 p.m.