Triple

T6807530
Position Surface form Disambiguated ID Type / Status
Subject Omagh E156344 entity
Predicate hasTwinTown P919 FINISHED
Object Rheine E293864 NE FINISHED

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: Rheine | Statement: [Omagh, hasTwinTown, Rheine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rheine
Context triple: [Omagh, hasTwinTown, Rheine]
  • A. Rheine chosen
    Rheine is a German city in the state of North Rhine-Westphalia, known for its historical town center and location along the River Ems.
  • B. Rhein II
    Rhein II is a large-scale color photograph by German visual artist Andreas Gursky, renowned for its minimalist depiction of the Rhine River and for once being the most expensive photograph ever sold at auction.
  • C. Roer
    The Roer is a river in Western Europe that flows through parts of Belgium, Germany, and the Netherlands before joining the Meuse.
  • D. Lippe
    Lippe is a historical region in northwestern Germany that once formed a small principality and later a Free State within the German Reich.
  • E. Lippe
    The Lippe is a river in western Germany that flows through North Rhine-Westphalia and is a right-bank tributary of the Rhine.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c68826e6a48190a3d220b541e639de completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d30a006081908996e31aa7ced0ac completed March 27, 2026, 6:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69c79428801c8190817a95d94ff180b9 completed March 28, 2026, 8:41 a.m.
Created at: March 27, 2026, 2:16 p.m.