Triple

T8837295
Position Surface form Disambiguated ID Type / Status
Subject Drensteinfurt E210296 entity
Predicate hasPart P35 FINISHED
Object Drensteinfurt town centre E210296 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: Drensteinfurt town centre | Statement: [Drensteinfurt, hasPart, Drensteinfurt town centre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Drensteinfurt town centre
Context triple: [Drensteinfurt, hasPart, Drensteinfurt town centre]
  • A. Drensteinfurt chosen
    Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland region.
  • B. Adendorf
    Adendorf is a village-sized district within the municipality of Wachtberg in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
  • C. Dornstadt
    Dornstadt is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, located near the city of Ulm.
  • D. Domplatz
    Domplatz is the central cathedral square in Salzburg’s historic Old Town, known for its baroque architecture and role as a focal point for religious and cultural events.
  • E. Burgplatz
    Burgplatz is a historic central square in Düsseldorf’s Old Town, known for its riverside location on the Rhine and remnants of the former city castle.
  • 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_69ca8388549c819095fd94eadefbb007 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc606adde08190825dbdabd199c025 completed April 1, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69cf898a478c81908f138a78f331b87d completed April 3, 2026, 9:34 a.m.
Created at: March 30, 2026, 6:48 p.m.