Triple
T3894945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Åre |
E88146
|
entity |
| Predicate | hasMountain |
P10602
|
FINISHED |
| Object |
Åreskutan
Åreskutan is a prominent mountain in central Sweden known for hosting one of Scandinavia’s most popular ski resorts and extensive alpine facilities.
|
E396716
|
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: Åreskutan | Statement: [Åre, hasMountain, Åreskutan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Åreskutan Context triple: [Åre, hasMountain, Åreskutan]
-
A.
Skarpäng
Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
-
B.
Ågotnes
Ågotnes is a village and industrial hub on the island of Sotra in Vestland county, Norway.
-
C.
Flemingsberg
Flemingsberg is a district in the southern Stockholm urban area known for its major university campus, hospital, and commuter rail hub.
-
D.
Storslett
Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
-
E.
Mortensrud
Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
- 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: Åreskutan Triple: [Åre, hasMountain, Åreskutan]
Generated description
Åreskutan is a prominent mountain in central Sweden known for hosting one of Scandinavia’s most popular ski resorts and extensive alpine facilities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Åreskutan Target entity description: Åreskutan is a prominent mountain in central Sweden known for hosting one of Scandinavia’s most popular ski resorts and extensive alpine facilities.
-
A.
Skarpäng
Skarpäng is a residential urban area within Täby Municipality in Stockholm County, Sweden.
-
B.
Ågotnes
Ågotnes is a village and industrial hub on the island of Sotra in Vestland county, Norway.
-
C.
Flemingsberg
Flemingsberg is a district in the southern Stockholm urban area known for its major university campus, hospital, and commuter rail hub.
-
D.
Storslett
Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
-
E.
Mortensrud
Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
- 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_69aed9466d548190939f5217a23ed4ac |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeecd197148190b5b24f7097c6049a |
completed | March 9, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51c99bab88190bd63f5b9b3950001 |
completed | March 14, 2026, 8:30 a.m. |
| NEDg | Description generation | batch_69b51d61e89081909e1df16631274746 |
completed | March 14, 2026, 8:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b51dcb5cb88190ad859cde9f10918a |
completed | March 14, 2026, 8:35 a.m. |
Created at: March 9, 2026, 3:21 p.m.