Triple
T4581783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jutland |
E101870
|
entity |
| Predicate | nativeName |
P15
|
FINISHED |
| Object | Jylland |
E458858
|
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: Jylland | Statement: [Jutland, nativeName, Jylland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jylland Context triple: [Jutland, nativeName, Jylland]
-
A.
Jylland
chosen
Jylland is a historical region in Denmark located on the Jutland Peninsula, known for its rural landscapes, coastal areas, and cultural heritage.
-
B.
North Jutlandic Island
North Jutlandic Island is a large island in northern Denmark separated from the rest of Jutland by the Limfjord and known for its coastal landscapes and tourism.
-
C.
Vestland
Vestland is a county in western Norway known for its dramatic fjords, coastal landscapes, and the city of Bergen.
-
D.
Schleswig
Schleswig is a historic town in northern Germany known for its Viking heritage, medieval cathedral, and location on the Schlei inlet.
-
E.
Lolland
Lolland is a large, predominantly agricultural island in southeastern Denmark known for its flat landscape and sugar beet production.
- 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_69bd43d4ce208190b53158c882b222e3 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd590116e88190b8495b2a78cf3fb6 |
completed | March 20, 2026, 2:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be033d90c48190b69a91db06b86998 |
completed | March 21, 2026, 2:32 a.m. |
Created at: March 20, 2026, 1:10 p.m.