Triple
T14057116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vanløse |
E338246
|
entity |
| Predicate | hasFootballClub |
P346
|
FINISHED |
| Object |
Vanløse IF
Vanløse IF is a Danish football club known for representing the Vanløse district of Copenhagen in the national league system.
|
E1079007
|
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: Vanløse IF | Statement: [Vanløse, hasFootballClub, Vanløse IF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vanløse IF Context triple: [Vanløse, hasFootballClub, Vanløse IF]
-
A.
Sandvikens IF
Sandvikens IF is a Swedish sports club best known for its football team, which has a long tradition and regional significance in Sandviken.
-
B.
Lørenskog IF
Lørenskog IF is a Norwegian sports club best known for its football team, based in Lørenskog near Oslo.
-
C.
Hvidovre IF
Hvidovre IF is a Danish football club known for developing notable players, including legendary goalkeeper Peter Schmeichel.
-
D.
Stattena IF
Stattena IF is a Swedish football club known for its role in developing players such as renowned coach and former international striker Pia Sundhage.
-
E.
Lommel SK
Lommel SK is a Belgian professional football club that competes in the country’s league system and serves as part of City Football Group’s global network of teams.
- 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: Vanløse IF Triple: [Vanløse, hasFootballClub, Vanløse IF]
Generated description
Vanløse IF is a Danish football club known for representing the Vanløse district of Copenhagen in the national league system.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vanløse IF Target entity description: Vanløse IF is a Danish football club known for representing the Vanløse district of Copenhagen in the national league system.
-
A.
Sandvikens IF
Sandvikens IF is a Swedish sports club best known for its football team, which has a long tradition and regional significance in Sandviken.
-
B.
Lørenskog IF
Lørenskog IF is a Norwegian sports club best known for its football team, based in Lørenskog near Oslo.
-
C.
Hvidovre IF
Hvidovre IF is a Danish football club known for developing notable players, including legendary goalkeeper Peter Schmeichel.
-
D.
Stattena IF
Stattena IF is a Swedish football club known for its role in developing players such as renowned coach and former international striker Pia Sundhage.
-
E.
Lommel SK
Lommel SK is a Belgian professional football club that competes in the country’s league system and serves as part of City Football Group’s global network of teams.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de3c8e6d008190af8892f34c5cefbd |
completed | April 14, 2026, 1:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb6608cf8819087ed5d890b82650a |
completed | May 7, 2026, 3:57 p.m. |
| NEDg | Description generation | batch_69fcc91405888190ba808b2051f57f19 |
completed | May 7, 2026, 5:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcc9be3f448190b2edd96c4159f923 |
completed | May 7, 2026, 5:19 p.m. |
Created at: April 9, 2026, 10:20 p.m.