Triple
T6331931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jørgen Brahe |
E142400
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jørgen |
E484121
|
NE FINISHED |
How this triple was built (2 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: [Jørgen Brahe, givenName, Jørgen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jørgen Context triple: [Jørgen Brahe, givenName, Jørgen]
-
A.
Jørgen
chosen
Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
-
B.
Søren
Søren is a masculine given name of Scandinavian origin, most famously borne by the Danish philosopher Søren Kierkegaard.
-
C.
Christoffer Reedtz
Christoffer Reedtz is a Danish businessman and football data analyst best known as the owner of English football club Notts County.
-
D.
Jorgen Holmboe
Jorgen Holmboe was a Norwegian-American meteorologist known for his contributions to dynamic meteorology and weather forecasting theory.
-
E.
Henrik Christensen
Henrik Christensen is a prominent robotics researcher and academic known for his influential contributions to computer vision, autonomous systems, and robotics education.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c008d4d8e88190ad301c05b08722ac |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0651634b08190b54860ba0a70f5c4 |
completed | March 22, 2026, 9:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6041f713c8190b27ba54181049377 |
completed | March 27, 2026, 4:14 a.m. |
Created at: March 22, 2026, 4:30 p.m.