Triple

T10968507
Position Surface form Disambiguated ID Type / Status
Subject Waldeck-Frankenberg E259169 entity
Predicate containsTown P847 FINISHED
Object Battonnberg
Battonnberg is a small town in the Waldeck-Frankenberg district of the German state of Hesse.
E897432 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: Battonnberg | Statement: [Waldeck-Frankenberg, containsTown, Battonnberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Battonnberg
Context triple: [Waldeck-Frankenberg, containsTown, Battonnberg]
  • A. Schorisse
    Schorisse is a village in East Flanders, Belgium, that now forms part of the municipality of Maarkedal.
  • B. Moensberg
    Moensberg is a residential neighborhood in the municipality of Uccle in the Brussels-Capital Region of Belgium.
  • C. Bütgenbach
    Bütgenbach is a municipality in eastern Belgium’s German-speaking Community, known for its scenic lake, outdoor recreation, and proximity to the strategic Elsenborn Ridge.
  • D. Rixheim
    Rixheim is a commune in northeastern France’s Grand Est region, known historically for its wallpaper manufacturing industry.
  • E. Leuchtenberg
    Leuchtenberg is a small municipality in Bavaria, Germany, known for its historic hilltop castle ruins and scenic Upper Palatinate countryside.
  • 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: Battonnberg
Triple: [Waldeck-Frankenberg, containsTown, Battonnberg]
Generated description
Battonnberg is a small town in the Waldeck-Frankenberg district of the German state of Hesse.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Battonnberg
Target entity description: Battonnberg is a small town in the Waldeck-Frankenberg district of the German state of Hesse.
  • A. Schorisse
    Schorisse is a village in East Flanders, Belgium, that now forms part of the municipality of Maarkedal.
  • B. Moensberg
    Moensberg is a residential neighborhood in the municipality of Uccle in the Brussels-Capital Region of Belgium.
  • C. Bütgenbach
    Bütgenbach is a municipality in eastern Belgium’s German-speaking Community, known for its scenic lake, outdoor recreation, and proximity to the strategic Elsenborn Ridge.
  • D. Rixheim
    Rixheim is a commune in northeastern France’s Grand Est region, known historically for its wallpaper manufacturing industry.
  • E. Leuchtenberg
    Leuchtenberg is a small municipality in Bavaria, Germany, known for its historic hilltop castle ruins and scenic Upper Palatinate countryside.
  • 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_69d6aa895f4c8190887a15460ef622f4 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7719800388190943a0bffa48a2731 completed April 9, 2026, 9:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69e2d78156c48190a956dc22b9832bcb completed April 18, 2026, 12:59 a.m.
NEDg Description generation batch_69e2ff1ffb8c8190ba97f3c2e3c8c601 completed April 18, 2026, 3:48 a.m.
NED2 Entity disambiguation (via description) batch_69e3261cc4f48190ba0e5645f37cd4b5 completed April 18, 2026, 6:35 a.m.
Created at: April 8, 2026, 9:24 p.m.