Triple
T5050916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bergshamra |
E113781
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Ulriksdal
Ulriksdal is a district in Solna, Sweden, known for the historic Ulriksdal Palace and its surrounding parklands along the Edsviken inlet.
|
E511170
|
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: Ulriksdal | Statement: [Bergshamra, locatedNear, Ulriksdal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ulriksdal Context triple: [Bergshamra, locatedNear, Ulriksdal]
-
A.
Kvænangen
Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
-
B.
Arendal
Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
-
C.
Svaneke
Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
-
D.
Maarkedal
Maarkedal is a rural municipality in the Flemish Ardennes of East Flanders, Belgium, known for its hilly landscape and cycling routes.
-
E.
Solør
Solør is a traditional district in Eastern Norway known for its rural landscapes, forestry, and agriculture.
- 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: Ulriksdal Triple: [Bergshamra, locatedNear, Ulriksdal]
Generated description
Ulriksdal is a district in Solna, Sweden, known for the historic Ulriksdal Palace and its surrounding parklands along the Edsviken inlet.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ulriksdal Target entity description: Ulriksdal is a district in Solna, Sweden, known for the historic Ulriksdal Palace and its surrounding parklands along the Edsviken inlet.
-
A.
Kvænangen
Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
-
B.
Arendal
Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
-
C.
Svaneke
Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
-
D.
Maarkedal
Maarkedal is a rural municipality in the Flemish Ardennes of East Flanders, Belgium, known for its hilly landscape and cycling routes.
-
E.
Solør
Solør is a traditional district in Eastern Norway known for its rural landscapes, forestry, and agriculture.
- 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_69bd443aa1f88190abb992d138f2cf42 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7426fc8081908a8227f73168c235 |
completed | March 20, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf186790248190bc94cfbecf3cfe73 |
completed | March 21, 2026, 10:15 p.m. |
| NEDg | Description generation | batch_69bf18f90d9081908a47e69e82ed2940 |
completed | March 21, 2026, 10:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf195e7be88190a7655d18471b1662 |
completed | March 21, 2026, 10:19 p.m. |
Created at: March 20, 2026, 1:37 p.m.