Triple

T7319003
Position Surface form Disambiguated ID Type / Status
Subject German Bight E168488 entity
Predicate hasPart P35 FINISHED
Object Texel E160304 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: Texel | Statement: [German Bight, hasPart, Texel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Texel
Context triple: [German Bight, hasPart, Texel]
  • A. Texel chosen
    Texel is the largest and most populated of the West Frisian Islands off the northwestern coast of the Netherlands, known for its beaches, dunes, and nature reserves.
  • B. Vlieland
    Vlieland is a sparsely populated Dutch Wadden Sea island known for its wide beaches, dunes, and car-free, nature-focused tourism.
  • C. Ameland
    Ameland is a Dutch Wadden Sea island known for its sandy beaches, dunes, and nature reserves, popular as a holiday destination in the northern Netherlands.
  • D. Kennemerland
    Kennemerland is a coastal historical region in the northwest of the Netherlands, known for its dunes, beaches, and old trading towns.
  • E. Friesland
    Friesland is a northern province of the Netherlands known for its distinct Frisian language, rich maritime history, and unique cultural traditions.
  • 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_69c68a5251508190ad68df4151cfeb04 completed March 27, 2026, 1:46 p.m.
NER Named-entity recognition batch_69c6ef18b7bc81908a9ee405d684f304 completed March 27, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c43f24881908a434773dfd79892 completed March 28, 2026, 8:38 p.m.
Created at: March 27, 2026, 3:02 p.m.