Triple
T4981711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giorgio |
E111900
|
entity |
| Predicate | hasCognate |
P2525
|
FINISHED |
| Object |
Jørgen
Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
|
E484121
|
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: Jørgen | Statement: [Giorgio, hasCognate, Jørgen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jørgen Context triple: [Giorgio, hasCognate, Jørgen]
-
A.
Søren
Søren is a masculine given name of Scandinavian origin, most famously borne by the Danish philosopher Søren Kierkegaard.
-
B.
Christoffer Reedtz
Christoffer Reedtz is a Danish businessman and football data analyst best known as the owner of English football club Notts County.
-
C.
Henrik Christensen
Henrik Christensen is a prominent robotics researcher and academic known for his influential contributions to computer vision, autonomous systems, and robotics education.
-
D.
Morten
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
-
E.
Kristian Levring
Kristian Levring is a Danish film director and screenwriter known for his visually striking, often bleak dramas and as one of the co-founders of the Dogme 95 movement.
- 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: Jørgen Triple: [Giorgio, hasCognate, Jørgen]
Generated description
Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jørgen Target entity description: Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
-
A.
Søren
Søren is a masculine given name of Scandinavian origin, most famously borne by the Danish philosopher Søren Kierkegaard.
-
B.
Christoffer Reedtz
Christoffer Reedtz is a Danish businessman and football data analyst best known as the owner of English football club Notts County.
-
C.
Henrik Christensen
Henrik Christensen is a prominent robotics researcher and academic known for his influential contributions to computer vision, autonomous systems, and robotics education.
-
D.
Morten
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
-
E.
Kristian Levring
Kristian Levring is a Danish film director and screenwriter known for his visually striking, often bleak dramas and as one of the co-founders of the Dogme 95 movement.
- 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_69bd441adc208190b70a033a0741d01e |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd725310088190a44b5c02658edc52 |
completed | March 20, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be8a0f90048190998dad99555891c0 |
completed | March 21, 2026, 12:07 p.m. |
| NEDg | Description generation | batch_69be8aec16748190922d3b9de523b1ae |
completed | March 21, 2026, 12:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be8b80af18819091efdfe242b7b477 |
completed | March 21, 2026, 12:13 p.m. |
Created at: March 20, 2026, 1:33 p.m.