Triple
T20988629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristiansand Airport, Kjevik |
E516957
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Topdalen
Topdalen is a valley area in Kristiansand, Norway, known for the river Topdalselva and its proximity to Kristiansand Airport, Kjevik.
|
E1466024
|
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: Topdalen | Statement: [Kristiansand Airport, Kjevik, locatedNear, Topdalen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Topdalen Context triple: [Kristiansand Airport, Kjevik, locatedNear, Topdalen]
-
A.
Thamerdal
Thamerdal is a residential neighborhood within the Dutch town of Uithoorn in the province of North Holland.
-
B.
Teveldalen
Teveldalen is a small village in the municipality of Meråker in Trøndelag county, central Norway, known for its scenic valley landscape near the Swedish border.
-
C.
Högdalen
Högdalen is a suburban district in southern Stockholm, Sweden, known for its residential areas and local shopping center.
-
D.
Nydalen
Nydalen is a modern riverside neighborhood in Oslo, Norway, known for its business district, educational institutions, and redeveloped industrial areas.
-
E.
Valldal
Valldal is a village in western Norway known for its scenic fjord landscape and strawberry farming, situated in the county of Møre og Romsdal.
- 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: Topdalen Triple: [Kristiansand Airport, Kjevik, locatedNear, Topdalen]
Generated description
Topdalen is a valley area in Kristiansand, Norway, known for the river Topdalselva and its proximity to Kristiansand Airport, Kjevik.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Topdalen Target entity description: Topdalen is a valley area in Kristiansand, Norway, known for the river Topdalselva and its proximity to Kristiansand Airport, Kjevik.
-
A.
Thamerdal
Thamerdal is a residential neighborhood within the Dutch town of Uithoorn in the province of North Holland.
-
B.
Teveldalen
Teveldalen is a small village in the municipality of Meråker in Trøndelag county, central Norway, known for its scenic valley landscape near the Swedish border.
-
C.
Högdalen
Högdalen is a suburban district in southern Stockholm, Sweden, known for its residential areas and local shopping center.
-
D.
Nydalen
Nydalen is a modern riverside neighborhood in Oslo, Norway, known for its business district, educational institutions, and redeveloped industrial areas.
-
E.
Valldal
Valldal is a village in western Norway known for its scenic fjord landscape and strawberry farming, situated in the county of Møre og Romsdal.
- 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_69e0b4ffac148190bbade9f0eceb660b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6fbe5208c8190b8b843b3778589d3 |
completed | April 21, 2026, 4:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a095a44adc081908734a4fd2ed66092 |
completed | May 17, 2026, 6:03 a.m. |
| NEDg | Description generation | batch_6a095c10ffec8190ac2948155a98127c |
completed | May 17, 2026, 6:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a095ccae2108190b1a679e581d6f61d |
completed | May 17, 2026, 6:14 a.m. |
Created at: April 16, 2026, 1:49 p.m.