Triple
T3676379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stockholm metro |
E78001
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Skärholmen
Skärholmen is a suburban district in southwestern Stockholm, Sweden, known for its large shopping center and residential areas.
|
E378324
|
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: Skärholmen | Statement: [Stockholm metro, hasStation, Skärholmen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Skärholmen Context triple: [Stockholm metro, hasStation, Skärholmen]
-
A.
Skarpö
Skarpö is an island in the Stockholm archipelago of Sweden, situated within Vaxholm Municipality and known for its coastal scenery and residential character.
-
B.
Kvarnholmen
Kvarnholmen is a former industrial island district in the Stockholm area that has been transformed into a modern residential and waterfront neighborhood.
-
C.
Nakkholmen
Nakkholmen is a small inhabited island known for its traditional wooden cabins and recreational use, located in the Oslofjord near Oslo, Norway.
-
D.
Lindholmen
Lindholmen is a waterfront district in Gothenburg, Sweden, known as a major hub for education, research, and technology companies.
-
E.
Lindholmen
Lindholmen is a small locality in Vallentuna Municipality in Stockholm County, Sweden, known for its residential character and proximity to natural and historical sites.
- 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: Skärholmen Triple: [Stockholm metro, hasStation, Skärholmen]
Generated description
Skärholmen is a suburban district in southwestern Stockholm, Sweden, known for its large shopping center and residential areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Skärholmen Target entity description: Skärholmen is a suburban district in southwestern Stockholm, Sweden, known for its large shopping center and residential areas.
-
A.
Skarpö
Skarpö is an island in the Stockholm archipelago of Sweden, situated within Vaxholm Municipality and known for its coastal scenery and residential character.
-
B.
Kvarnholmen
Kvarnholmen is a former industrial island district in the Stockholm area that has been transformed into a modern residential and waterfront neighborhood.
-
C.
Nakkholmen
Nakkholmen is a small inhabited island known for its traditional wooden cabins and recreational use, located in the Oslofjord near Oslo, Norway.
-
D.
Lindholmen
Lindholmen is a waterfront district in Gothenburg, Sweden, known as a major hub for education, research, and technology companies.
-
E.
Lindholmen
Lindholmen is a small locality in Vallentuna Municipality in Stockholm County, Sweden, known for its residential character and proximity to natural and historical sites.
- 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_69ad85e18c1c8190be8aafb227f39f48 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc462ffdc8190896e9f98f648e2f3 |
completed | March 8, 2026, 6:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b48859949081908fd8291bf6a3372b |
completed | March 13, 2026, 9:57 p.m. |
| NEDg | Description generation | batch_69b48aede85481909f4fc17c3968f285 |
completed | March 13, 2026, 10:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4b95fae2c819091d8cc1d86eb091d |
completed | March 14, 2026, 1:26 a.m. |
Created at: March 8, 2026, 3:25 p.m.