Triple
T10297412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Västmanland County |
E241527
|
entity |
| Predicate | hasUrbanArea |
P316
|
FINISHED |
| Object |
Arboga
Arboga is a historic small town in central Sweden known for its medieval heritage and well-preserved old town.
|
E857515
|
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: Arboga | Statement: [Västmanland County, hasUrbanArea, Arboga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arboga Context triple: [Västmanland County, hasUrbanArea, Arboga]
-
A.
Rimforsa
Rimforsa is a small locality in Kinda Municipality in Östergötland County, Sweden.
-
B.
Gislaved
Gislaved is a tire brand known for producing reliable winter and all-season tires, particularly popular in Northern and Central Europe.
-
C.
Svalöv
Svalöv is a small locality and municipality in Skåne County in southern Sweden, known for its rural landscape and agricultural surroundings.
-
D.
Östhammar
Östhammar is a small coastal town and municipality in eastern Sweden known for its archipelago, historic wooden buildings, and proximity to the Forsmark nuclear power plant.
-
E.
Djursholm
Djursholm is an affluent suburban district of Stockholm, Sweden, known for its villas, garden-city planning, and status as one of the country’s wealthiest residential areas.
- 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: Arboga Triple: [Västmanland County, hasUrbanArea, Arboga]
Generated description
Arboga is a historic small town in central Sweden known for its medieval heritage and well-preserved old town.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Arboga Target entity description: Arboga is a historic small town in central Sweden known for its medieval heritage and well-preserved old town.
-
A.
Rimforsa
Rimforsa is a small locality in Kinda Municipality in Östergötland County, Sweden.
-
B.
Gislaved
Gislaved is a tire brand known for producing reliable winter and all-season tires, particularly popular in Northern and Central Europe.
-
C.
Svalöv
Svalöv is a small locality and municipality in Skåne County in southern Sweden, known for its rural landscape and agricultural surroundings.
-
D.
Östhammar
Östhammar is a small coastal town and municipality in eastern Sweden known for its archipelago, historic wooden buildings, and proximity to the Forsmark nuclear power plant.
-
E.
Djursholm
Djursholm is an affluent suburban district of Stockholm, Sweden, known for its villas, garden-city planning, and status as one of the country’s wealthiest residential areas.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2ebd258819099fadddcd13099fc |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d75018383481909abbba8247a93f8e |
completed | April 9, 2026, 7:07 a.m. |
| NEDg | Description generation | batch_69d7618b0f2481908149596dc86d4593 |
completed | April 9, 2026, 8:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d77015ae688190870976309e2b912b |
completed | April 9, 2026, 9:23 a.m. |
Created at: April 6, 2026, 11:43 a.m.