Triple
T16961295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Söder |
E411433
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Lena Söder
Lena Söder is a person notable enough to be recognized as a bearer of the Swedish surname Söder.
|
E1243710
|
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: Lena Söder | Statement: [Söder, hasNotableBearer, Lena Söder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lena Söder Context triple: [Söder, hasNotableBearer, Lena Söder]
-
A.
Lena Nilsson
Lena Nilsson is a Swedish actress known for her work in film, television, and theater.
-
B.
Magdalena Andersson
Magdalena Andersson is a Swedish economist and politician who served as Sweden’s first female prime minister and leader of the Swedish Social Democratic Party.
-
C.
Annie Lööf
Annie Lööf is a Swedish politician and former leader of the Centre Party, known for her liberal-centrist stance and prominent role in national politics.
-
D.
Åsa Larsson
Åsa Larsson is a Swedish crime fiction author best known for her Rebecka Martinsson series set in northern Sweden.
-
E.
Kristina Lugn
Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
- 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: Lena Söder Triple: [Söder, hasNotableBearer, Lena Söder]
Generated description
Lena Söder is a person notable enough to be recognized as a bearer of the Swedish surname Söder.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lena Söder Target entity description: Lena Söder is a person notable enough to be recognized as a bearer of the Swedish surname Söder.
-
A.
Lena Nilsson
Lena Nilsson is a Swedish actress known for her work in film, television, and theater.
-
B.
Magdalena Andersson
Magdalena Andersson is a Swedish economist and politician who served as Sweden’s first female prime minister and leader of the Swedish Social Democratic Party.
-
C.
Annie Lööf
Annie Lööf is a Swedish politician and former leader of the Centre Party, known for her liberal-centrist stance and prominent role in national politics.
-
D.
Åsa Larsson
Åsa Larsson is a Swedish crime fiction author best known for her Rebecka Martinsson series set in northern Sweden.
-
E.
Kristina Lugn
Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
- 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_69d886c9c9d481909afe222093641cae |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d0209a9081909d9c62456bc16e14 |
completed | April 18, 2026, 6:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d46ad58c8190be9f0b36daba8162 |
completed | May 10, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_6a00d5f5f0448190be407979539fcd80 |
completed | May 10, 2026, 7:01 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00d6e1add481908cce9048e3746e7c |
completed | May 10, 2026, 7:05 p.m. |
Created at: April 10, 2026, 5:31 a.m.