Triple
T7318995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German Bight |
E168488
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Heligoland |
E59562
|
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: Heligoland | Statement: [German Bight, hasPart, Heligoland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heligoland Context triple: [German Bight, hasPart, Heligoland]
-
A.
Helgoland
chosen
Helgoland is a small German archipelago in the North Sea known for its dramatic red sandstone cliffs, unique wildlife, and historical significance as a strategic naval and cultural site.
-
B.
Sylt
Sylt is a popular German North Sea island known for its long sandy beaches, distinctive dune landscapes, and status as an upscale holiday destination.
-
C.
Borkum
Borkum is a German North Sea island known for its seaside resorts, sandy beaches, and role as the westernmost of the East Frisian Islands.
-
D.
Vlieland
Vlieland is a sparsely populated Dutch Wadden Sea island known for its wide beaches, dunes, and car-free, nature-focused tourism.
-
E.
Norderney
Norderney is a popular German North Sea island known for its sandy beaches, seaside resort town, and role as a major tourist destination in Lower Saxony.
- 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_69c802ac12a8819098d855e250e00274 |
completed | March 28, 2026, 4:32 p.m. |
Created at: March 27, 2026, 3:02 p.m.