Triple
T2453258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Järvenpää |
E53754
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Hörby
Hörby is a small municipality in southern Sweden’s Skåne County, known for its rural landscape and traditional Swedish town character.
|
E269060
|
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: Hörby | Statement: [Järvenpää, hasTwinTown, Hörby]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hörby Context triple: [Järvenpää, hasTwinTown, Hörby]
-
A.
Vårby
Vårby is a suburban district in the southern Stockholm area of Sweden, known for its residential neighborhoods and proximity to Lake Mälaren.
-
B.
Västerhaninge
Västerhaninge is a suburban locality in Stockholm County, Sweden, known as a residential community within the Haninge area.
-
C.
Viggbyholm
Viggbyholm is a residential urban area in the northern Stockholm region of Sweden, known for its proximity to water, green spaces, and commuter connections into central Stockholm.
-
D.
Strängnäs
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
-
E.
Mönsterås
Mönsterås is a small coastal town and municipality in Kalmar County, southeastern Sweden, known for its Baltic Sea shoreline and traditional Swedish countryside.
- 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: Hörby Triple: [Järvenpää, hasTwinTown, Hörby]
Generated description
Hörby is a small municipality in southern Sweden’s Skåne County, known for its rural landscape and traditional Swedish town character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hörby Target entity description: Hörby is a small municipality in southern Sweden’s Skåne County, known for its rural landscape and traditional Swedish town character.
-
A.
Vårby
Vårby is a suburban district in the southern Stockholm area of Sweden, known for its residential neighborhoods and proximity to Lake Mälaren.
-
B.
Västerhaninge
Västerhaninge is a suburban locality in Stockholm County, Sweden, known as a residential community within the Haninge area.
-
C.
Viggbyholm
Viggbyholm is a residential urban area in the northern Stockholm region of Sweden, known for its proximity to water, green spaces, and commuter connections into central Stockholm.
-
D.
Strängnäs
Strängnäs is a historic Swedish town known for its medieval cathedral and picturesque location on the shores of Lake Mälaren.
-
E.
Mönsterås
Mönsterås is a small coastal town and municipality in Kalmar County, southeastern Sweden, known for its Baltic Sea shoreline and traditional Swedish countryside.
- 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_69ab495d227c8190b26ae6548eeb1019 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd0f699308190910a41520dc9efdb |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aef0c544788190bb0e8c5ae4a32ff0 |
completed | March 9, 2026, 4:09 p.m. |
| NEDg | Description generation | batch_69aef53740508190893b14bb1b411a30 |
completed | March 9, 2026, 4:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69aef9594024819088e7afc0e64429ff |
completed | March 9, 2026, 4:46 p.m. |
Created at: March 6, 2026, 9:43 p.m.