Triple
T1785751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elle |
E39387
|
entity |
| Predicate | hasEdition |
P35
|
FINISHED |
| Object |
Elle Russia
Elle Russia is the Russian-language edition of the international fashion and lifestyle magazine Elle, featuring content on style, beauty, culture, and celebrity.
|
E198232
|
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 Russia | Statement: [Elle, hasEdition, Elle Russia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elle Russia Context triple: [Elle, hasEdition, Elle Russia]
-
A.
Olga
Olga is a female given name of Russian origin, historically borne by several notable figures including Russian grand duchesses and saints.
-
B.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
C.
La Russa
La Russa is an Italian surname most prominently associated with Hall of Fame Major League Baseball manager Tony La Russa.
-
D.
Anastasia
Anastasia is a 1956 historical drama film starring Ingrid Bergman as an amnesiac woman who may be the surviving daughter of Russia’s last tsar.
-
E.
Nadezhda
Nadezhda is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and meaning "hope."
- 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 Russia Triple: [Elle, hasEdition, Elle Russia]
Generated description
Elle Russia is the Russian-language edition of the international fashion and lifestyle magazine Elle, featuring content on style, beauty, culture, and celebrity.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elle Russia Target entity description: Elle Russia is the Russian-language edition of the international fashion and lifestyle magazine Elle, featuring content on style, beauty, culture, and celebrity.
-
A.
Olga
Olga is a female given name of Russian origin, historically borne by several notable figures including Russian grand duchesses and saints.
-
B.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
C.
La Russa
La Russa is an Italian surname most prominently associated with Hall of Fame Major League Baseball manager Tony La Russa.
-
D.
Anastasia
Anastasia is a 1956 historical drama film starring Ingrid Bergman as an amnesiac woman who may be the surviving daughter of Russia’s last tsar.
-
E.
Nadezhda
Nadezhda is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and meaning "hope."
- 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.