Triple
T23080730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bøler |
E575460
|
entity |
| Predicate | hasNearbyLake |
P17985
|
FINISHED |
| Object |
Ulsrudvann
Ulsrudvann is a popular recreational lake in Oslo, Norway, known for its swimming spots, forested surroundings, and easy access from nearby residential areas.
|
E1571918
|
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: Ulsrudvann | Statement: [Bøler, hasNearbyLake, Ulsrudvann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ulsrudvann Context triple: [Bøler, hasNearbyLake, Ulsrudvann]
-
A.
Bjørnevatn
Bjørnevatn is a village in northern Norway known for its mining history and proximity to the Russian border.
-
B.
Vangsvatnet
Vangsvatnet is a scenic lake in the municipality of Voss in western Norway, known for its surrounding mountains and popularity for outdoor and water sports activities.
-
C.
Røssvatnet
Røssvatnet is one of Norway’s largest lakes, located in the northern part of the country and known for its scenic surroundings and hydroelectric significance.
-
D.
Lundevatnet
Lundevatnet is a lake located in the municipality of Gjesdal in Rogaland county, southwestern Norway.
-
E.
Snåsavatnet
Snåsavatnet is one of Norway’s largest lakes, located in Trøndelag county and known for its scenic surroundings and rich freshwater ecosystem.
- 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: Ulsrudvann Triple: [Bøler, hasNearbyLake, Ulsrudvann]
Generated description
Ulsrudvann is a popular recreational lake in Oslo, Norway, known for its swimming spots, forested surroundings, and easy access from nearby residential areas.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ulsrudvann Target entity description: Ulsrudvann is a popular recreational lake in Oslo, Norway, known for its swimming spots, forested surroundings, and easy access from nearby residential areas.
-
A.
Bjørnevatn
Bjørnevatn is a village in northern Norway known for its mining history and proximity to the Russian border.
-
B.
Vangsvatnet
Vangsvatnet is a scenic lake in the municipality of Voss in western Norway, known for its surrounding mountains and popularity for outdoor and water sports activities.
-
C.
Røssvatnet
Røssvatnet is one of Norway’s largest lakes, located in the northern part of the country and known for its scenic surroundings and hydroelectric significance.
-
D.
Lundevatnet
Lundevatnet is a lake located in the municipality of Gjesdal in Rogaland county, southwestern Norway.
-
E.
Snåsavatnet
Snåsavatnet is one of Norway’s largest lakes, located in Trøndelag county and known for its scenic surroundings and rich freshwater ecosystem.
- 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_69e245be28d48190ad1348d5a73db37d |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c67e06881908d24d6267bb49553 |
completed | April 29, 2026, 4:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c23dca4c08190a9619956a45aa17a |
completed | May 19, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_6a0c271d8fc48190a818c73660218022 |
completed | May 19, 2026, 9:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c2951a718819088c1c7d8435586ec |
completed | May 19, 2026, 9:11 a.m. |
Created at: April 17, 2026, 3:56 p.m.