Triple

T20459043
Position Surface form Disambiguated ID Type / Status
Subject Hangelsberg E501872 entity
Predicate hasOfficialName P66 FINISHED
Object Hangelsberg NE NERFINISHED

How this triple was built (2 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: Hangelsberg | Statement: [Hangelsberg, hasOfficialName, Hangelsberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hangelsberg
Context triple: [Hangelsberg, hasOfficialName, Hangelsberg]
  • A. Hangelsberg chosen
    Hangelsberg is a village in the German state of Brandenburg, known as a district of the municipality Grünheide (Mark) in the Oder-Spree region.
  • B. Sendenhorst
    Sendenhorst is a small town in the German state of North Rhine-Westphalia, known for its rural character and location in the Münsterland region.
  • C. Hornsberg
    Hornsberg is a waterfront residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
  • D. Hermannsberg
    Hermannsberg is a location in Germany known, among other things, as the place where the influential German educator Kurt Hahn died.
  • E. Wackersberg
    Wackersberg is a rural Bavarian municipality in southern Germany, known for its scenic Alpine foothills and traditional village character.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4ad4940819098cf2ff6413574e5 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e696a4652c8190acf79fa2e285e436 completed April 20, 2026, 9:12 p.m.
Created at: April 16, 2026, 11:33 a.m.