Triple
T12983962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Vättern |
E321719
|
entity |
| Predicate | adjacentCity |
P5707
|
FINISHED |
| Object |
Askersund
Askersund is a small Swedish town in Örebro County known for its picturesque harbor setting on the northern shores of Lake Vättern.
|
E1012567
|
NE FINISHED |
How this triple was built (4 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: Askersund | Statement: [Lake Vättern, adjacentCity, Askersund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Askersund Context triple: [Lake Vättern, adjacentCity, Askersund]
-
A.
Svinesund
Svinesund is a strait forming part of the border between Norway and Sweden, best known for its bridges and role as a major road crossing between the two countries.
-
B.
Bogesund
Bogesund is a locality in Sweden known for its surrounding archipelago landscape, forests, and recreational natural areas.
-
C.
Ginnerup
Ginnerup is a small village in Denmark best known as the birthplace of former Danish Prime Minister and NATO Secretary General Anders Fogh Rasmussen.
-
D.
Løgstør
Løgstør is a small Danish town in northern Jutland known for its historic harbor, maritime heritage, and location along the Limfjord.
-
E.
Abildsø
Abildsø is a residential neighborhood in the borough of Østensjø in Oslo, Norway, known for its green areas and proximity to the lake Østensjøvannet.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Askersund Triple: [Lake Vättern, adjacentCity, Askersund]
Generated description
Askersund is a small Swedish town in Örebro County known for its picturesque harbor setting on the northern shores of Lake Vättern.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Askersund Target entity description: Askersund is a small Swedish town in Örebro County known for its picturesque harbor setting on the northern shores of Lake Vättern.
-
A.
Svinesund
Svinesund is a strait forming part of the border between Norway and Sweden, best known for its bridges and role as a major road crossing between the two countries.
-
B.
Bogesund
Bogesund is a locality in Sweden known for its surrounding archipelago landscape, forests, and recreational natural areas.
-
C.
Ginnerup
Ginnerup is a small village in Denmark best known as the birthplace of former Danish Prime Minister and NATO Secretary General Anders Fogh Rasmussen.
-
D.
Løgstør
Løgstør is a small Danish town in northern Jutland known for its historic harbor, maritime heritage, and location along the Limfjord.
-
E.
Abildsø
Abildsø is a residential neighborhood in the borough of Østensjø in Oslo, Norway, known for its green areas and proximity to the lake Østensjøvannet.
- F. None of above. chosen
Provenance (5 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e5e3f208190abd2d4b4d5114834 |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6b8f4320881909d66eaa48f888fab |
completed | May 3, 2026, 2:54 a.m. |
| NEDg | Description generation | batch_69f6b9dc31ec819093c89ff0a1ccbfa1 |
completed | May 3, 2026, 2:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6baacd7548190af5514923a0dee26 |
completed | May 3, 2026, 3:02 a.m. |
Created at: April 9, 2026, 8:39 p.m.