Triple
T7488775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limfjord |
E176948
|
entity |
| Predicate | hasInlet |
P23365
|
FINISHED |
| Object | Løgstør Bredning |
E669307
|
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: Løgstør Bredning | Statement: [Limfjord, hasInlet, Løgstør Bredning]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Løgstør Bredning Context triple: [Limfjord, hasInlet, Løgstør Bredning]
-
A.
Løgstør
chosen
Løgstør is a small Danish town in northern Jutland known for its historic harbor, maritime heritage, and location along the Limfjord.
-
B.
Bragernes
Bragernes is a historic former town and district that now forms the northern part of the city of Drammen in Norway.
-
C.
Løkken
Løkken is a Danish seaside town known for its sandy beaches, coastal dunes, and popular summer tourism on the North Sea.
-
D.
Blangsted
Blangsted is a surname most notably associated with Folmar Blangsted, a film editor.
-
E.
Birkelunden
Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
- 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_69c69f24ac508190bb98fe927c0bd065 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f55abcd481909e42ca857fe46cd1 |
completed | March 27, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c84eed875c81908922057730834a84 |
completed | March 28, 2026, 9:58 p.m. |
Created at: March 27, 2026, 3:43 p.m.