Triple
T7319001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German Bight |
E168488
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Juist |
E159875
|
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: Juist | Statement: [German Bight, hasPart, Juist]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juist Context triple: [German Bight, hasPart, Juist]
-
A.
Juist
chosen
Juist is a small, car-free barrier island in the North Sea off the coast of Germany, known for its long sandy beaches, dunes, and tranquil spa tourism.
-
B.
Just
Just is the given name of Just Fontaine, the legendary French footballer who holds the record for most goals scored in a single FIFA World Cup tournament.
-
C.
Justify
Justify is an American Thoroughbred racehorse best known for winning the 2018 Triple Crown.
-
D.
Rättvik
Rättvik is a small town in central Sweden known for its traditional Swedish culture, lakeside setting on Lake Siljan, and historic wooden pier.
-
E.
Gelykheid
Gelykheid was a Dutch warship that was captured by the British Royal Navy during the 1797 Battle of Camperdown in the French Revolutionary Wars.
- 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_69c7eefcd7148190818d581cbde9aff1 |
completed | March 28, 2026, 3:08 p.m. |
Created at: March 27, 2026, 3:02 p.m.