Triple
T1785762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elle |
E39387
|
entity |
| Predicate | hasEdition |
P35
|
FINISHED |
| Object |
Elle Sweden
Elle Sweden is the Swedish edition of the international fashion and lifestyle magazine Elle, featuring content tailored to Swedish readers and culture.
|
E198239
|
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: Elle Sweden | Statement: [Elle, hasEdition, Elle Sweden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elle Sweden Context triple: [Elle, hasEdition, Elle Sweden]
-
A.
Sara Esberg
Sara Esberg is a television producer known for her executive production work on series such as the psychological horror show "Swarm."
-
B.
Greta
Greta is a small town located within the Hunter Region of New South Wales, Australia.
-
C.
Greta
Greta is a feminine given name, commonly used as a diminutive or variant of names like Margaret in various European languages.
-
D.
Hanna Alström
Hanna Alström is a Swedish actress best known internationally for her role as Princess Tilde in the action-comedy film "Kingsman: The Secret Service" and its sequel.
-
E.
Majgull Axelsson
Majgull Axelsson is a Swedish journalist and award-winning author known for her socially engaged novels that often explore themes of injustice and marginalized lives.
- 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: Elle Sweden Triple: [Elle, hasEdition, Elle Sweden]
Generated description
Elle Sweden is the Swedish edition of the international fashion and lifestyle magazine Elle, featuring content tailored to Swedish readers and culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elle Sweden Target entity description: Elle Sweden is the Swedish edition of the international fashion and lifestyle magazine Elle, featuring content tailored to Swedish readers and culture.
-
A.
Sara Esberg
Sara Esberg is a television producer known for her executive production work on series such as the psychological horror show "Swarm."
-
B.
Greta
Greta is a small town located within the Hunter Region of New South Wales, Australia.
-
C.
Greta
Greta is a feminine given name, commonly used as a diminutive or variant of names like Margaret in various European languages.
-
D.
Hanna Alström
Hanna Alström is a Swedish actress best known internationally for her role as Princess Tilde in the action-comedy film "Kingsman: The Secret Service" and its sequel.
-
E.
Majgull Axelsson
Majgull Axelsson is a Swedish journalist and award-winning author known for her socially engaged novels that often explore themes of injustice and marginalized lives.
- 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_69a88630519c8190a17addd83c4a3ef4 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa650d304481908ad9bff3eadf7da6 |
completed | March 6, 2026, 5:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada9a476448190b072361fe4b41537 |
completed | March 8, 2026, 4:53 p.m. |
| NEDg | Description generation | batch_69adab05cf6c81909f4713664f508ad9 |
completed | March 8, 2026, 4:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adaeb20390819098bad8951ec00d00 |
completed | March 8, 2026, 5:15 p.m. |
Created at: March 4, 2026, 7:31 p.m.