Triple

T14185091
Position Surface form Disambiguated ID Type / Status
Subject Netherlands Institute for Sound and Vision E351554 entity
Predicate locatedIn P40 FINISHED
Object Hilversum E475829 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: Hilversum | Statement: [Netherlands Institute for Sound and Vision, locatedIn, Hilversum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hilversum
Context triple: [Netherlands Institute for Sound and Vision, locatedIn, 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 (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_69d8278834a08190b0f1784e58d7b99c completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61cd5778819092a03597bcdcc182 completed April 14, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01232149f88190b385fca6a7588d7b completed May 11, 2026, 12:30 a.m.
Created at: April 10, 2026, 1:03 a.m.