Triple
T6304153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vestre Aker |
E141330
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Huseby
Huseby is a residential area in Oslo, Norway, known for its green surroundings and location within the Vestre Aker borough.
|
E592023
|
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: Huseby | Statement: [Vestre Aker, contains, Huseby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huseby Context triple: [Vestre Aker, contains, Huseby]
-
A.
Ullensaker
Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
-
B.
Oslo East
Oslo East is the eastern part of Norway’s capital city, often associated with working-class neighborhoods, cultural diversity, and a strong local football supporter culture.
-
C.
Drammen
Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
-
D.
Bærum
Bærum is a wealthy suburban municipality just west of Oslo, Norway, known for its high standard of living and residential communities.
-
E.
Håvik
Håvik is a small coastal village located in Karmøy municipality in Rogaland county, southwestern Norway.
- 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: Huseby Triple: [Vestre Aker, contains, Huseby]
Generated description
Huseby is a residential area in Oslo, Norway, known for its green surroundings and location within the Vestre Aker borough.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Huseby Target entity description: Huseby is a residential area in Oslo, Norway, known for its green surroundings and location within the Vestre Aker borough.
-
A.
Ullensaker
Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
-
B.
Oslo East
Oslo East is the eastern part of Norway’s capital city, often associated with working-class neighborhoods, cultural diversity, and a strong local football supporter culture.
-
C.
Drammen
Drammen is a city and municipality in southeastern Norway known for its riverside setting along the Drammenselva and its role as a regional commercial and transport hub.
-
D.
Bærum
Bærum is a wealthy suburban municipality just west of Oslo, Norway, known for its high standard of living and residential communities.
-
E.
Håvik
Håvik is a small coastal village located in Karmøy municipality in Rogaland county, southwestern Norway.
- 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_69c008cf0ad4819095def81e2bd42f9f |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0645f26a881909d5746151c0843cc |
completed | March 22, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c640aa8b608190ab834e77613f5218 |
completed | March 27, 2026, 8:32 a.m. |
| NEDg | Description generation | batch_69c641a5a6888190aa1ca79a64a9d3bb |
completed | March 27, 2026, 8:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6420f40d0819097332359671a8a08 |
completed | March 27, 2026, 8:38 a.m. |
Created at: March 22, 2026, 4:28 p.m.