Triple
T4872357
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lidingö |
E109113
|
entity |
| Predicate | hasSportsClub |
P346
|
FINISHED |
| Object |
Lidingö SK
Lidingö SK is a Swedish multi-sport club based in Lidingö, known especially for its athletics and orienteering activities.
|
E479210
|
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: Lidingö SK | Statement: [Lidingö, hasSportsClub, Lidingö SK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lidingö SK Context triple: [Lidingö, hasSportsClub, Lidingö SK]
-
A.
IFK Lidingö
IFK Lidingö is a Swedish multi-sport club based on the island of Lidingö, best known for its activities in athletics, orienteering, and football.
-
B.
Linköping HC
Linköping HC is a professional Swedish ice hockey club based in Linköping that competes in the country’s top-tier leagues.
-
C.
Östersunds FK
Östersunds FK is a Swedish professional football club known for its rapid rise through the leagues and notable performances in domestic and European competitions.
-
D.
Linköpings FC
Linköpings FC is a Swedish professional women's football club based in Linköping that competes in the top tier Damallsvenskan league.
-
E.
Djurgården
Djurgården is a central Stockholm island known for its parks, museums, and major attractions like the Vasa Museum and Skansen.
- 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: Lidingö SK Triple: [Lidingö, hasSportsClub, Lidingö SK]
Generated description
Lidingö SK is a Swedish multi-sport club based in Lidingö, known especially for its athletics and orienteering activities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lidingö SK Target entity description: Lidingö SK is a Swedish multi-sport club based in Lidingö, known especially for its athletics and orienteering activities.
-
A.
IFK Lidingö
IFK Lidingö is a Swedish multi-sport club based on the island of Lidingö, best known for its activities in athletics, orienteering, and football.
-
B.
Linköping HC
Linköping HC is a professional Swedish ice hockey club based in Linköping that competes in the country’s top-tier leagues.
-
C.
Östersunds FK
Östersunds FK is a Swedish professional football club known for its rapid rise through the leagues and notable performances in domestic and European competitions.
-
D.
Linköpings FC
Linköpings FC is a Swedish professional women's football club based in Linköping that competes in the top tier Damallsvenskan league.
-
E.
Djurgården
Djurgården is a central Stockholm island known for its parks, museums, and major attractions like the Vasa Museum and Skansen.
- 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_69bd440d96a48190b0c87069adef2af1 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6d9e27908190a0c4540ee2559c4b |
completed | March 20, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6fb25f008190ab9b7cc904b540c9 |
completed | March 21, 2026, 10:15 a.m. |
| NEDg | Description generation | batch_69be71ef01148190af12e1b9a2869612 |
completed | March 21, 2026, 10:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be7242fc5c8190bc63ad852f937590 |
completed | March 21, 2026, 10:26 a.m. |
Created at: March 20, 2026, 1:27 p.m.