Triple
T15227312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nilsson |
E363907
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Lena Nilsson
Lena Nilsson is a Swedish actress known for her work in film, television, and theater.
|
E1155643
|
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 Nilsson | Statement: [Nilsson, hasNotableBearer, Lena Nilsson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lena Nilsson Context triple: [Nilsson, hasNotableBearer, Lena Nilsson]
-
A.
Lena Nyman
Lena Nyman was a Swedish actress known for her emotionally intense and nuanced performances in both film and theater, particularly in influential Scandinavian cinema of the 1960s and 1970s.
-
B.
Emma Nilsson
Emma Nilsson is a person bearing the Swedish surname Nilsson, which is common in Scandinavian countries.
-
C.
Kristina Lugn
Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
-
D.
Greta Lundgren
Greta Lundgren is a daughter of Swedish actor and martial artist Dolph Lundgren.
-
E.
Sara Esberg
Sara Esberg is a television producer known for her executive production work on series such as the psychological horror show "Swarm."
- 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 Nilsson Triple: [Nilsson, hasNotableBearer, Lena Nilsson]
Generated description
Lena Nilsson is a Swedish actress known for her work in film, television, and theater.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lena Nilsson Target entity description: Lena Nilsson is a Swedish actress known for her work in film, television, and theater.
-
A.
Lena Nyman
Lena Nyman was a Swedish actress known for her emotionally intense and nuanced performances in both film and theater, particularly in influential Scandinavian cinema of the 1960s and 1970s.
-
B.
Emma Nilsson
Emma Nilsson is a person bearing the Swedish surname Nilsson, which is common in Scandinavian countries.
-
C.
Kristina Lugn
Kristina Lugn was a Swedish poet, playwright, and member of the Swedish Academy known for her darkly humorous and psychologically incisive works.
-
D.
Greta Lundgren
Greta Lundgren is a daughter of Swedish actor and martial artist Dolph Lundgren.
-
E.
Sara Esberg
Sara Esberg is a television producer known for her executive production work on series such as the psychological horror show "Swarm."
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078bb32881909927561c6c072546 |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff1a63464c8190afab59257c6a2095 |
completed | May 9, 2026, 11:28 a.m. |
| NEDg | Description generation | batch_69ff1b3c563481908418411a977df343 |
completed | May 9, 2026, 11:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff1c0ad5448190903dc38f78512f3b |
completed | May 9, 2026, 11:35 a.m. |
Created at: April 10, 2026, 3:12 a.m.