Triple

T14030039
Position Surface form Disambiguated ID Type / Status
Subject Portrait of Johan de Witt E337562 entity
Predicate depictsFamilyName P98396 FINISHED
Object de Witt E50699 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: de Witt | Statement: [Portrait of Johan de Witt, depictsFamilyName, de Witt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: de Witt
Context triple: [Portrait of Johan de Witt, depictsFamilyName, de Witt]
  • A. Tomblaine
    Tomblaine is a commune in northeastern France, near Nancy, known for hosting the home stadium of the AS Nancy football club.
  • B. De Witt chosen
    De Witt is a Dutch surname most famously associated with Johan de Witt, a prominent 17th-century statesman of the Dutch Republic.
  • C. Benjamin Motte
    Benjamin Motte was an 18th-century London publisher best known for issuing the first edition of Jonathan Swift’s satirical novel "Gulliver’s Travels."
  • D. Anna de Witt
    Anna de Witt was a member of the prominent Dutch De Witt family of the 17th century, known primarily as a daughter of the influential statesman Johan de Witt.
  • E. De La Motte
    De La Motte is a French-origin surname historically associated with various notable figures, including early 20th-century American silent film actress Marguerite De La Motte.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2fa9f8248190930954d609dee5f1 completed April 14, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbc335a474819084c310b10e0ded9a completed May 6, 2026, 10:39 p.m.
Created at: April 9, 2026, 10:20 p.m.