Triple

T6035117
Position Surface form Disambiguated ID Type / Status
Subject Tilburg E134398 entity
Predicate hasMayor P185 FINISHED
Object Theo Weterings E554093 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: Theo Weterings | Statement: [Tilburg, hasMayor, Theo Weterings]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Theo Weterings
Context triple: [Tilburg, hasMayor, Theo Weterings]
  • A. Theo Weterings chosen
    Theo Weterings is a Dutch politician who serves as the mayor of the municipality of Best in the Netherlands.
  • B. Jos Wienen
    Jos Wienen is a Dutch politician who serves as the mayor of the city of Haarlem in the Netherlands.
  • C. Peter Noorwits
    Peter Noorwits was a Dutch architect known for his work on prominent ecclesiastical buildings in the Netherlands, including the Nieuwe Kerk in The Hague.
  • D. Chris de Weijer
    Chris de Weijer is a Dutch architect best known as one of the founding partners of the internationally renowned architecture firm Mecanoo.
  • E. Christian Huitema
    Christian Huitema is a French computer scientist and Internet pioneer known for his influential work on networking protocols and IPv6 transition technologies.
  • 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_69c00875db5c819099dd5bb833ec43c2 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c056b33a7c8190ad6282286199b192 completed March 22, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c11388aec881908408d5844c96ea2d completed March 23, 2026, 10:18 a.m.
Created at: March 22, 2026, 4:08 p.m.