Triple
T13799327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Vänern |
E331596
|
entity |
| Predicate | hasIsland |
P970
|
FINISHED |
| Object |
Hammarö
Hammarö is a Swedish island and municipality in Värmland County, known for its forests, coastline, and proximity to the city of Karlstad.
|
E1086615
|
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: Hammarö | Statement: [Lake Vänern, hasIsland, Hammarö]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hammarö Context triple: [Lake Vänern, hasIsland, Hammarö]
-
A.
Söderhamn
Söderhamn is a coastal town in east-central Sweden known for its historical wooden architecture and role as the administrative and commercial center of the surrounding region.
-
B.
Värmdö
Värmdö is a large island and municipality in the Stockholm archipelago of Sweden, known for its coastal landscapes, holiday homes, and proximity to Stockholm.
-
C.
Vaxholm
Vaxholm is a small coastal town and municipality in the Stockholm archipelago of eastern Sweden, known for its historic fortress and picturesque waterfront.
-
D.
Sandviken
Sandviken is an industrial town in central Sweden, best known as the historic home of the steel company Sandvik.
-
E.
Fredrikshamn
Fredrikshamn (Hamina) is a coastal town in southeastern Finland that historically served as an important military and trading center.
- 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: Hammarö Triple: [Lake Vänern, hasIsland, Hammarö]
Generated description
Hammarö is a Swedish island and municipality in Värmland County, known for its forests, coastline, and proximity to the city of Karlstad.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hammarö Target entity description: Hammarö is a Swedish island and municipality in Värmland County, known for its forests, coastline, and proximity to the city of Karlstad.
-
A.
Söderhamn
Söderhamn is a coastal town in east-central Sweden known for its historical wooden architecture and role as the administrative and commercial center of the surrounding region.
-
B.
Värmdö
Värmdö is a large island and municipality in the Stockholm archipelago of Sweden, known for its coastal landscapes, holiday homes, and proximity to Stockholm.
-
C.
Vaxholm
Vaxholm is a small coastal town and municipality in the Stockholm archipelago of eastern Sweden, known for its historic fortress and picturesque waterfront.
-
D.
Sandviken
Sandviken is an industrial town in central Sweden, best known as the historic home of the steel company Sandvik.
-
E.
Fredrikshamn
Fredrikshamn (Hamina) is a coastal town in southeastern Finland that historically served as an important military and trading center.
- 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de025ce9148190b23370f6a522ff7a |
completed | April 14, 2026, 9:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd192957008190b525778430b56ca0 |
completed | May 7, 2026, 10:58 p.m. |
| NEDg | Description generation | batch_69fd1fb29ef88190bfa15c163ca392ed |
completed | May 7, 2026, 11:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd2006221081908ab46e1eadb52e3d |
completed | May 7, 2026, 11:28 p.m. |
Created at: April 9, 2026, 10:11 p.m.