Triple
T3189670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Svealand |
E66788
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Borlänge
Borlänge is an industrial town in central Sweden’s Dalarna County, known for its steel production, logistics hub, and role as a regional commercial center.
|
E336559
|
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: Borlänge | Statement: [Svealand, contains, Borlänge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Borlänge Context triple: [Svealand, contains, Borlänge]
-
A.
Bollnäs
Bollnäs is a small Swedish town known for its scenic lakeside setting, traditional wooden architecture, and strong bandy sports culture.
-
B.
Bollstanäs
Bollstanäs is a residential locality in Sweden situated within the suburban area of Upplands Väsby, north of Stockholm.
-
C.
Korsnäs
Korsnäs is a small coastal municipality in western Finland known for its Swedish-speaking majority and traditional Ostrobothnian rural culture.
-
D.
Tungelsta
Tungelsta is a locality in Stockholm County, Sweden, known for its residential character and commuter connections within the Haninge area.
-
E.
Hovsjö
Hovsjö is a residential district in the city of Södertälje, Sweden, known for its large-scale housing estates and diverse population.
- 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: Borlänge Triple: [Svealand, contains, Borlänge]
Generated description
Borlänge is an industrial town in central Sweden’s Dalarna County, known for its steel production, logistics hub, and role as a regional commercial center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Borlänge Target entity description: Borlänge is an industrial town in central Sweden’s Dalarna County, known for its steel production, logistics hub, and role as a regional commercial center.
-
A.
Bollnäs
Bollnäs is a small Swedish town known for its scenic lakeside setting, traditional wooden architecture, and strong bandy sports culture.
-
B.
Bollstanäs
Bollstanäs is a residential locality in Sweden situated within the suburban area of Upplands Väsby, north of Stockholm.
-
C.
Korsnäs
Korsnäs is a small coastal municipality in western Finland known for its Swedish-speaking majority and traditional Ostrobothnian rural culture.
-
D.
Tungelsta
Tungelsta is a locality in Stockholm County, Sweden, known for its residential character and commuter connections within the Haninge area.
-
E.
Hovsjö
Hovsjö is a residential district in the city of Södertälje, Sweden, known for its large-scale housing estates and diverse population.
- 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_69ad8587c1bc8190a2595f2c22ee1001 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada6e67e948190afbd9cc6a3ade415 |
completed | March 8, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24b9a9bc88190b7090bda8fe6260c |
completed | March 12, 2026, 5:14 a.m. |
| NEDg | Description generation | batch_69b24d677ca8819094cb03360ac885da |
completed | March 12, 2026, 5:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b25178f3c08190be78bdbd0cdfc5f3 |
completed | March 12, 2026, 5:39 a.m. |
Created at: March 8, 2026, 3:07 p.m.