Triple
T9160493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Randers |
E219807
|
entity |
| Predicate | hasSportsClub |
P346
|
FINISHED |
| Object |
Randers FC
Randers FC is a professional Danish football club based in the city of Randers that competes in the Danish Superliga.
|
E782607
|
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: Randers FC | Statement: [Randers, hasSportsClub, Randers FC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Randers FC Context triple: [Randers, hasSportsClub, Randers FC]
-
A.
Brøndby
Brøndby is a suburban municipality in the western part of the Copenhagen metropolitan area in Denmark, known for its residential districts and the football club Brøndby IF.
-
B.
Hammersborg
Hammersborg is a central neighborhood in Oslo, Norway, known for housing key government buildings and cultural institutions.
-
C.
FC Midtjylland
FC Midtjylland is a Danish professional football club known for its data-driven approach to player recruitment and performance, competing in the top tier of Danish football.
-
D.
Vejle Boldklub
Vejle Boldklub is a Danish professional football club known for its historic success in the national league and cup competitions.
-
E.
Odense Boldklub
Odense Boldklub is a Danish professional football club based in the city of Odense, known for competing in the top tiers of Danish football.
- 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: Randers FC Triple: [Randers, hasSportsClub, Randers FC]
Generated description
Randers FC is a professional Danish football club based in the city of Randers that competes in the Danish Superliga.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Randers FC Target entity description: Randers FC is a professional Danish football club based in the city of Randers that competes in the Danish Superliga.
-
A.
Brøndby
Brøndby is a suburban municipality in the western part of the Copenhagen metropolitan area in Denmark, known for its residential districts and the football club Brøndby IF.
-
B.
Hammersborg
Hammersborg is a central neighborhood in Oslo, Norway, known for housing key government buildings and cultural institutions.
-
C.
FC Midtjylland
FC Midtjylland is a Danish professional football club known for its data-driven approach to player recruitment and performance, competing in the top tier of Danish football.
-
D.
Vejle Boldklub
Vejle Boldklub is a Danish professional football club known for its historic success in the national league and cup competitions.
-
E.
Odense Boldklub
Odense Boldklub is a Danish professional football club based in the city of Odense, known for competing in the top tiers of Danish football.
- 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_69ca83e3633c81908688a9fa2306ba99 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca9d953c081908d21f363801aaae4 |
completed | April 1, 2026, 5:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0547073cc8190999fe640c7ccd373 |
completed | April 3, 2026, 11:59 p.m. |
| NEDg | Description generation | batch_69d05560d6888190b4faf3406f9ff9f2 |
completed | April 4, 2026, 12:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d059161a1881909ceaaf8b0893dbea |
completed | April 4, 2026, 12:19 a.m. |
Created at: March 30, 2026, 7:21 p.m.