Triple

T14954049
Position Surface form Disambiguated ID Type / Status
Subject Jacob Adriaensz Backer E372873 entity
Predicate placeOfBirth P1 FINISHED
Object Harlingen E89980 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: Harlingen | Statement: [Jacob Adriaensz Backer, placeOfBirth, Harlingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harlingen
Context triple: [Jacob Adriaensz Backer, placeOfBirth, Harlingen]
  • A. Harlingen chosen
    Harlingen is a historic port city in the Dutch province of Friesland, known for its maritime heritage and traditional canalside architecture.
  • B. Harlingen
    Harlingen is a mid-sized city in the Rio Grande Valley of South Texas known as a regional hub for transportation, healthcare, and commerce near the U.S.–Mexico border.
  • C. Heiligenhafen
    Heiligenhafen is a coastal town in northern Germany on the Baltic Sea, known for its fishing harbor, beaches, and tourism.
  • D. Vienenburg
    Vienenburg is a district of Goslar in Lower Saxony, Germany, known for its historic town center and proximity to the Harz Mountains.
  • E. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • 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_69d85cca979481908747d2a81eba1cea completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6cb336c8190b8a55106fa8fc500 completed April 15, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe7e9c71cc8190aff9165a6f97981a completed May 9, 2026, 12:23 a.m.
Created at: April 10, 2026, 2:40 a.m.