Triple

T5371355
Position Surface form Disambiguated ID Type / Status
Subject North Hesse E108856 entity
Predicate hasCity P316 FINISHED
Object Wolfhagen
Wolfhagen is a small town in the German state of Hesse, known for its historic half-timbered buildings and location near the Habichtswald Nature Park.
 E518155 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: Wolfhagen | Statement: [North Hesse, hasCity, Wolfhagen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wolfhagen
Context triple: [North Hesse, hasCity, Wolfhagen]
  • A. Helmscherode
    Helmscherode is a small locality in Germany known as the birthplace of Wilhelm Keitel, a senior military leader of Nazi Germany.
  • B. Marlenheim
    Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
  • C. Rheinhausen
    Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
  • D. Ziegenhain
    Ziegenhain is a historic town in the German state of Hesse, known for its medieval fortifications and role in regional conflicts.
  • E. Wilhelmsruh
    Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
  • 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: Wolfhagen
Triple: [North Hesse, hasCity, Wolfhagen]
Generated description
Wolfhagen is a small town in the German state of Hesse, known for its historic half-timbered buildings and location near the Habichtswald Nature Park.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wolfhagen
Target entity description: Wolfhagen is a small town in the German state of Hesse, known for its historic half-timbered buildings and location near the Habichtswald Nature Park.
  • A. Helmscherode
    Helmscherode is a small locality in Germany known as the birthplace of Wilhelm Keitel, a senior military leader of Nazi Germany.
  • B. Marlenheim
    Marlenheim is a commune in northeastern France’s Alsace region, known as a historic wine-producing village and gateway to the area’s renowned vineyards and scenic countryside.
  • C. Rheinhausen
    Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
  • D. Ziegenhain
    Ziegenhain is a historic town in the German state of Hesse, known for its medieval fortifications and role in regional conflicts.
  • E. Wilhelmsruh
    Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
  • 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_69bd440c77948190aad2a5f39b7b80f5 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd86aa0f5c8190ba96554e75696f8e completed March 20, 2026, 5:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf3a94acc48190a4c0ea39c6b8a405 completed March 22, 2026, 12:40 a.m.
NEDg Description generation batch_69bf3b2a1a888190b4c4191ec6b98b42 completed March 22, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_69bf3b8170748190b2e7e427323d55f9 completed March 22, 2026, 12:44 a.m.
Created at: March 20, 2026, 2:02 p.m.