Triple

T12406296
Position Surface form Disambiguated ID Type / Status
Subject Bovenden E296395 entity
Predicate hasMunicipalPart P84684 FINISHED
Object Harste
Harste is a village and municipal district of Bovenden in Lower Saxony, Germany.
E992970 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: Harste | Statement: [Bovenden, hasMunicipalPart, Harste]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harste
Context triple: [Bovenden, hasMunicipalPart, Harste]
  • A. Herbesthal
    Herbesthal is a village in eastern Belgium, historically known for its former international railway station near the German border.
  • B. Haldenstein
    Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
  • C. Stutterheim
    Stutterheim is a small town in South Africa’s Eastern Cape province, known for its forestry, agriculture, and scenic setting near the Amathole Mountains.
  • D. Hesselberg
    Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
  • E. Kornhain
    Kornhain is a village-level subdivision of the town of Wurzen in the German state of Saxony.
  • 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: Harste
Triple: [Bovenden, hasMunicipalPart, Harste]
Generated description
Harste is a village and municipal district of Bovenden in Lower Saxony, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Harste
Target entity description: Harste is a village and municipal district of Bovenden in Lower Saxony, Germany.
  • A. Herbesthal
    Herbesthal is a village in eastern Belgium, historically known for its former international railway station near the German border.
  • B. Haldenstein
    Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
  • C. Stutterheim
    Stutterheim is a small town in South Africa’s Eastern Cape province, known for its forestry, agriculture, and scenic setting near the Amathole Mountains.
  • D. Hesselberg
    Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
  • E. Kornhain
    Kornhain is a village-level subdivision of the town of Wurzen in the German state of Saxony.
  • 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_69d6ad9f464c81909db36d7e96e34b9e completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d48f1908190918551c794f98fe3 completed April 10, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65ea28b508190a2467b9af195e4ed completed May 2, 2026, 8:29 p.m.
NEDg Description generation batch_69f661369d608190b7b4f8b2bcf6e9b3 completed May 2, 2026, 8:40 p.m.
NED2 Entity disambiguation (via description) batch_69f661b369fc81909ced522d7057589e completed May 2, 2026, 8:42 p.m.
Created at: April 8, 2026, 9:55 p.m.