Triple
T18113356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lunner |
E433535
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object |
Harestuvatnet
Harestuvatnet is a lake in Lunner municipality in Viken county, Norway, known for its surrounding forests and outdoor recreation opportunities.
|
E1308976
|
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: Harestuvatnet | Statement: [Lunner, hasLake, Harestuvatnet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harestuvatnet Context triple: [Lunner, hasLake, Harestuvatnet]
-
A.
Lovatnet
Lovatnet is a scenic glacial lake in western Norway, renowned for its turquoise waters, dramatic surrounding mountains, and tragic history of devastating rockslide-induced tsunamis.
-
B.
Oldevatnet
Oldevatnet is a scenic glacial lake in the Nordfjord region of Vestland county, western Norway, known for its turquoise waters and surrounding steep mountains.
-
C.
Lundevatnet
Lundevatnet is a lake located in the municipality of Gjesdal in Rogaland county, southwestern Norway.
-
D.
Juklavatnet
Juklavatnet is a lake in western Norway known for its scenic glacial surroundings and mountainous landscape.
-
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: Harestuvatnet Triple: [Lunner, hasLake, Harestuvatnet]
Generated description
Harestuvatnet is a lake in Lunner municipality in Viken county, Norway, known for its surrounding forests and outdoor recreation opportunities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Harestuvatnet Target entity description: Harestuvatnet is a lake in Lunner municipality in Viken county, Norway, known for its surrounding forests and outdoor recreation opportunities.
-
A.
Lovatnet
Lovatnet is a scenic glacial lake in western Norway, renowned for its turquoise waters, dramatic surrounding mountains, and tragic history of devastating rockslide-induced tsunamis.
-
B.
Oldevatnet
Oldevatnet is a scenic glacial lake in the Nordfjord region of Vestland county, western Norway, known for its turquoise waters and surrounding steep mountains.
-
C.
Lundevatnet
Lundevatnet is a lake located in the municipality of Gjesdal in Rogaland county, southwestern Norway.
-
D.
Juklavatnet
Juklavatnet is a lake in western Norway known for its scenic glacial surroundings and mountainous landscape.
-
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_69d8b90916008190a1f110bd7ced5473 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ddd3fd9c81909bfe95927f7553e3 |
completed | April 19, 2026, 1:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a03853dfaf081908775a259728c458e |
completed | May 12, 2026, 7:53 p.m. |
| NEDg | Description generation | batch_6a038a1bb75c81908211dfb2d938a9bd |
completed | May 12, 2026, 8:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a038a81f0148190864adc746d251b75 |
completed | May 12, 2026, 8:16 p.m. |
Created at: April 10, 2026, 10:28 a.m.