Triple

T14918490
Position Surface form Disambiguated ID Type / Status
Subject Rhine–Ruhr to Randstad rail corridor E371444 entity
Predicate connectsCity P4245 FINISHED
Object Hilversum 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: Hilversum | Statement: [Rhine–Ruhr to Randstad rail corridor, connectsCity, Hilversum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hilversum
Context triple: [Rhine–Ruhr to Randstad rail corridor, connectsCity, Hilversum]
  • A. Hilversum chosen
    Hilversum is a Dutch city known as the country’s main media and broadcasting center, located in the province of North Holland.
  • B. Tilburg
    Tilburg is a city in the southern Netherlands known historically as an industrial and textile center and now as a regional cultural and educational hub.
  • C. Utrecht
    Utrecht is a small town in South Africa’s KwaZulu-Natal province, known for its scenic surroundings and historical significance dating back to the 19th century.
  • D. Utrecht
    Utrecht is a historic city and province in the central Netherlands, known for its medieval old town, canals, and role as a religious and cultural center.
  • E. Zoetermeer
    Zoetermeer is a modern, rapidly grown satellite city of The Hague in the western Netherlands, known for its residential neighborhoods and light-rail connections.
  • 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_69d85cc7ea3481908228b5acb7d06f12 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded62f76bc81909ebc8899096cd1a0 completed April 15, 2026, 12:05 a.m.
Created at: April 10, 2026, 2:33 a.m.