Triple

T6078409
Position Surface form Disambiguated ID Type / Status
Subject Tilburg University E135459 entity
Predicate city P40 FINISHED
Object Tilburg E134398 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: Tilburg | Statement: [Tilburg University, city, Tilburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tilburg
Context triple: [Tilburg University, city, Tilburg]
  • A. Tilburg chosen
    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.
  • B. Eindhoven
    Eindhoven is a major city in the southern Netherlands known for its industrial and technological significance, particularly as a hub for electronics and design.
  • C. 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.
  • D. Nijmegen
    Nijmegen is a historic Dutch city near the German border that played a crucial strategic role during World War II, particularly in the Allied advance in 1944.
  • E. Hilversum
    Hilversum is a Dutch city known as the country’s main media and broadcasting center, located in the province of North Holland.
  • 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_69c0087ad31c8190ab936e0ff28614b6 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c057706d9881909b52093282593886 completed March 22, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69f018324bf88190bcd2bf168b1065d3 completed April 28, 2026, 2:15 a.m.
Created at: March 22, 2026, 4:11 p.m.