Triple
T5729544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grünerløkka |
E126346
|
entity |
| Predicate | hasPark |
P105
|
FINISHED |
| Object |
Birkelunden
Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
|
E547281
|
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: Birkelunden | Statement: [Grünerløkka, hasPark, Birkelunden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Birkelunden Context triple: [Grünerløkka, hasPark, Birkelunden]
-
A.
Bragernes
Bragernes is a historic former town and district that now forms the northern part of the city of Drammen in Norway.
-
B.
Maarkedal
Maarkedal is a rural municipality in the Flemish Ardennes of East Flanders, Belgium, known for its hilly landscape and cycling routes.
-
C.
Rønne
Rønne is the largest town and administrative center of the Danish island of Bornholm, known for its historic harbor, half-timbered houses, and Baltic Sea ferry connections.
-
D.
Norderhov
Norderhov is a village in the municipality of Ringerike in Buskerud, Norway, known for its historic church and rural surroundings.
-
E.
Søllerød
Søllerød is a locality in Rudersdal Municipality, north of Copenhagen in eastern Denmark, known for its affluent residential areas and scenic natural surroundings.
- 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: Birkelunden Triple: [Grünerløkka, hasPark, Birkelunden]
Generated description
Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Birkelunden Target entity description: Birkelunden is a popular public park in Oslo’s Grünerløkka district, known for its green spaces, cultural events, and historic surroundings.
-
A.
Bragernes
Bragernes is a historic former town and district that now forms the northern part of the city of Drammen in Norway.
-
B.
Maarkedal
Maarkedal is a rural municipality in the Flemish Ardennes of East Flanders, Belgium, known for its hilly landscape and cycling routes.
-
C.
Rønne
Rønne is the largest town and administrative center of the Danish island of Bornholm, known for its historic harbor, half-timbered houses, and Baltic Sea ferry connections.
-
D.
Norderhov
Norderhov is a village in the municipality of Ringerike in Buskerud, Norway, known for its historic church and rural surroundings.
-
E.
Søllerød
Søllerød is a locality in Rudersdal Municipality, north of Copenhagen in eastern Denmark, known for its affluent residential areas and scenic natural surroundings.
- 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_69c0082f723881908ce8bb13a0c0f8b7 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c025303860819093e51f176babed71 |
completed | March 22, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c097f0978881908f2b6fb4e6d5e4cd |
completed | March 23, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69c098ba92408190b61b115540fce941 |
completed | March 23, 2026, 1:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c099b4bc4481909e7cf6886e5ccbea |
completed | March 23, 2026, 1:39 a.m. |
Created at: March 22, 2026, 3:47 p.m.