Triple

T15704516
Position Surface form Disambiguated ID Type / Status
Subject Cauberg E380672 entity
Predicate startsIn P389 FINISHED
Object Valkenburg town centre E380675 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: Valkenburg town centre | Statement: [Cauberg, startsIn, Valkenburg town centre]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valkenburg town centre
Context triple: [Cauberg, startsIn, Valkenburg town centre]
  • A. Valkenburg
    Valkenburg is a village in the Dutch province of South Holland, known for its historic charm and proximity to the North Sea coast.
  • B. Vredenburg
    Vredenburg is a town on South Africa’s West Coast that serves as a regional commercial and service hub near Saldanha Bay.
  • C. Vredenburg
    Vredenburg is a former name of the Muziekcentrum Vredenburg, a prominent concert and music venue in Utrecht, Netherlands.
  • D. Valkenburg aan de Geul chosen
    Valkenburg aan de Geul is a historic town in the hilly Limburg region of the Netherlands, known for its cycling heritage, marlstone caves, and medieval castle ruins.
  • E. Vallendar
    Vallendar is a small town on the Rhine River in western Germany, known for hosting the prestigious WHU – Otto Beisheim School of Management.
  • 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_69d86d9bf930819082b30cf6d169297c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04f6fc3608190a85b25755f5345db completed April 16, 2026, 2:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff757997348190b29a9b55ba08169f completed May 9, 2026, 5:57 p.m.
Created at: April 10, 2026, 4:45 a.m.