Triple
T22682902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Veitvet |
E560827
|
entity |
| Predicate | hasNeighbourhood |
P4813
|
FINISHED |
| Object |
Tonsenhagen
Tonsenhagen is a residential neighborhood in Oslo, Norway, known for its post-war apartment blocks, green spaces, and proximity to the Lillomarka forest.
|
E1549409
|
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: Tonsenhagen | Statement: [Veitvet, hasNeighbourhood, Tonsenhagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tonsenhagen Context triple: [Veitvet, hasNeighbourhood, Tonsenhagen]
-
A.
Hjorthagen
Hjorthagen is a residential district in northeastern Stockholm, Sweden, known for its mix of historic workers’ housing and modern developments near the Royal National City Park and the Värtan harbor area.
-
B.
Trætteberg
Trætteberg is a Norwegian surname most notably associated with heraldist Hallvard Trætteberg.
-
C.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
D.
Hodenhagen
Hodenhagen is a small municipality in Lower Saxony, Germany, known for its rural setting along the Aller River and proximity to attractions like the Serengeti Park safari zoo.
-
E.
Hallsberg
Hallsberg is a Swedish railway town in Örebro County known as a major junction in the national rail network.
- 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: Tonsenhagen Triple: [Veitvet, hasNeighbourhood, Tonsenhagen]
Generated description
Tonsenhagen is a residential neighborhood in Oslo, Norway, known for its post-war apartment blocks, green spaces, and proximity to the Lillomarka forest.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tonsenhagen Target entity description: Tonsenhagen is a residential neighborhood in Oslo, Norway, known for its post-war apartment blocks, green spaces, and proximity to the Lillomarka forest.
-
A.
Hjorthagen
Hjorthagen is a residential district in northeastern Stockholm, Sweden, known for its mix of historic workers’ housing and modern developments near the Royal National City Park and the Värtan harbor area.
-
B.
Trætteberg
Trætteberg is a Norwegian surname most notably associated with heraldist Hallvard Trætteberg.
-
C.
Hesselberg
Hesselberg is a prominent hill in Bavaria, Germany, known as the highest elevation of the Franconian Alb region.
-
D.
Hodenhagen
Hodenhagen is a small municipality in Lower Saxony, Germany, known for its rural setting along the Aller River and proximity to attractions like the Serengeti Park safari zoo.
-
E.
Hallsberg
Hallsberg is a Swedish railway town in Örebro County known as a major junction in the national rail network.
- 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_69e2454d71b48190a1f80af9f82b6fcf |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1786204d88190a837a5f04e16e94c |
completed | April 29, 2026, 3:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b73f4db988190a651faf7f454c6c8 |
completed | May 18, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_6a0b7551043c81908323a86af4db77ce |
completed | May 18, 2026, 8:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b764c2de88190bb1024206607d107 |
completed | May 18, 2026, 8:27 p.m. |
Created at: April 17, 2026, 3:12 p.m.