Triple

T1202009
Position Surface form Disambiguated ID Type / Status
Subject Meuse E25802 entity
Predicate passesThrough P225 FINISHED
Object Venlo E458091 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: Venlo | Statement: [Meuse, passesThrough, Venlo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Venlo
Context triple: [Meuse, passesThrough, Venlo]
  • A. Venlo chosen
    Venlo is a historic city in the southeastern Netherlands, located near the German border on the river Meuse and known as a regional economic and logistics hub.
  • B. Roermond
    Roermond is a historic city in the southeastern Netherlands known for its medieval architecture, prominent churches, and large designer outlet shopping center.
  • C. Zwolle
    Zwolle is a historic Dutch city in the eastern Netherlands known for its medieval center, cultural heritage, and regional economic importance.
  • D. Culemborg
    Culemborg is a historic town in the Dutch province of Gelderland, known for its medieval center and role in the early Dutch colonial era.
  • E. Apeldoorn
    Apeldoorn is a city in the province of Gelderland in the Netherlands, known for the royal palace Het Loo and its historical ties to the Dutch monarchy.
  • 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_69a4942b30f08190a91c60573e16b5ef completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd9fece4819089a6a2d61e61fa2e completed March 1, 2026, 10:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfb6fb95008190af52d015903bca5c completed March 22, 2026, 9:31 a.m.
Created at: March 1, 2026, 7:46 p.m.