Triple

T3525685
Position Surface form Disambiguated ID Type / Status
Subject Gerard Houckgeest E74532 entity
Predicate influenced P9 FINISHED
Object Emanuel de Witte E73044 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: Emanuel de Witte | Statement: [Gerard Houckgeest, influenced, Emanuel de Witte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Emanuel de Witte
Context triple: [Gerard Houckgeest, influenced, Emanuel de Witte]
  • A. Emanuel de Witte chosen
    Emanuel de Witte was a Dutch Golden Age painter renowned for his atmospheric church interiors and masterful use of light and perspective.
  • B. Theodoor Rombouts
    Theodoor Rombouts was a prominent Flemish Baroque painter known for his dynamic Caravaggesque genre scenes and dramatic use of light and shadow.
  • C. Aert van der Neer
    Aert van der Neer was a Dutch Golden Age painter renowned for his atmospheric moonlit landscapes and winter scenes.
  • D. Jan van der Heyden
    Jan van der Heyden was a 17th-century Dutch painter and inventor renowned for his detailed cityscapes and pioneering improvements in firefighting technology and street lighting.
  • E. Joachim Wtewael
    Joachim Wtewael was a Dutch Mannerist painter and draftsman of the late 16th and early 17th centuries, known for his highly detailed, vividly colored religious and mythological scenes.
  • 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_69ad85d0c5488190a3d8e02ebd01a1aa completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc6a8d0c819094d38b9c47fb67b4 completed March 8, 2026, 6:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69bb808e7f94819086fbad85d6aed33e completed March 19, 2026, 4:50 a.m.
Created at: March 8, 2026, 3:19 p.m.