Triple
T13400538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turning Torso |
E319815
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Västra Hamnen |
E987225
|
NE FINISHED |
How this triple was built (2 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: Västra Hamnen | Statement: [Turning Torso, locatedIn, Västra Hamnen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Västra Hamnen Context triple: [Turning Torso, locatedIn, Västra Hamnen]
-
A.
Västra Hamnen
chosen
Västra Hamnen is a modern, waterfront district in Malmö, Sweden, known for its sustainable urban design and the landmark Turning Torso skyscraper.
-
B.
Kungsholmen
Kungsholmen is a central island and district of Stockholm known for its waterfront promenades, residential areas, and the iconic Stockholm City Hall.
-
C.
Lindholmen
Lindholmen is a small locality in Vallentuna Municipality in Stockholm County, Sweden, known for its residential character and proximity to natural and historical sites.
-
D.
Lindholmen
Lindholmen is a waterfront district in Gothenburg, Sweden, known as a major hub for education, research, and technology companies.
-
E.
Södermalm
Södermalm is a central island and district of Stockholm known for its vibrant cultural scene, historic architecture, and trendy shops, cafes, and nightlife.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbae47e99081909d8b5dba97a11988 |
completed | April 12, 2026, 2:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76b9ec6848190b8e986d849756050 |
completed | May 3, 2026, 3:37 p.m. |
Created at: April 9, 2026, 9:34 p.m.