Triple
T10364229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Morten Søborg |
E244210
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object |
Søborg
Søborg is a Danish surname most notably borne by individuals such as cinematographer Morten Søborg.
|
E863627
|
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: Søborg | Statement: [Morten Søborg, hasFamilyName, Søborg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Søborg Context triple: [Morten Søborg, hasFamilyName, Søborg]
-
A.
Sønderborg
Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
-
B.
Oksbøl
Oksbøl is a town in southwestern Jutland, Denmark, known for its military training areas and historical role as a garrison location.
-
C.
Svendborg
Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
-
D.
Sorø
Sorø is a historic Danish town on the island of Zealand, known for its medieval abbey, prestigious Sorø Academy, and scenic lakeside setting.
-
E.
Nyborg
Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
- 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: Søborg Triple: [Morten Søborg, hasFamilyName, Søborg]
Generated description
Søborg is a Danish surname most notably borne by individuals such as cinematographer Morten Søborg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Søborg Target entity description: Søborg is a Danish surname most notably borne by individuals such as cinematographer Morten Søborg.
-
A.
Sønderborg
Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
-
B.
Oksbøl
Oksbøl is a town in southwestern Jutland, Denmark, known for its military training areas and historical role as a garrison location.
-
C.
Svendborg
Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
-
D.
Sorø
Sorø is a historic Danish town on the island of Zealand, known for its medieval abbey, prestigious Sorø Academy, and scenic lakeside setting.
-
E.
Nyborg
Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
- 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e964a53c8190b748e80850e96656 |
completed | April 7, 2026, 11:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87e64109881908c42a4fbcfd057be |
completed | April 10, 2026, 4:36 a.m. |
| NEDg | Description generation | batch_69d886c325c4819089dac35eb26e7961 |
completed | April 10, 2026, 5:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d88dbbe97c8190861e08f3ff39f91b |
completed | April 10, 2026, 5:42 a.m. |
Created at: April 6, 2026, noon