Triple
T13716694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Women |
E328918
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Belyaev
Belyaev is a central male character in Ivan Turgenev’s play "Two Women" (also known as "A Month in the Country"), whose romantic entanglements drive much of the drama’s emotional conflict.
|
E1058243
|
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: Belyaev | Statement: [Two Women, hasCharacter, Belyaev]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belyaev Context triple: [Two Women, hasCharacter, Belyaev]
-
A.
Mikhail Belyaev
Mikhail Belyaev was a Russian Imperial general who served as the last War Minister of the Russian Empire during World War I.
-
B.
Eugene Belyaev
Eugene Belyaev is a Russian software engineer and entrepreneur best known as a co-founder of the software development tools company JetBrains.
-
C.
Belka
Belka is a Polish surname most notably borne by Marek Belka, an economist and former Prime Minister of Poland.
-
D.
Lyova
Lyova is a Russian diminutive form of the male given name Lev.
-
E.
Muravyov-Karsky
Muravyov-Karsky was a 19th-century Russian general best known for his prominent role in the Russo-Turkish wars and the conquest of Kars in the Caucasus.
- 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: Belyaev Triple: [Two Women, hasCharacter, Belyaev]
Generated description
Belyaev is a central male character in Ivan Turgenev’s play "Two Women" (also known as "A Month in the Country"), whose romantic entanglements drive much of the drama’s emotional conflict.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Belyaev Target entity description: Belyaev is a central male character in Ivan Turgenev’s play "Two Women" (also known as "A Month in the Country"), whose romantic entanglements drive much of the drama’s emotional conflict.
-
A.
Mikhail Belyaev
Mikhail Belyaev was a Russian Imperial general who served as the last War Minister of the Russian Empire during World War I.
-
B.
Eugene Belyaev
Eugene Belyaev is a Russian software engineer and entrepreneur best known as a co-founder of the software development tools company JetBrains.
-
C.
Belka
Belka is a Polish surname most notably borne by Marek Belka, an economist and former Prime Minister of Poland.
-
D.
Lyova
Lyova is a Russian diminutive form of the male given name Lev.
-
E.
Muravyov-Karsky
Muravyov-Karsky was a 19th-century Russian general best known for his prominent role in the Russo-Turkish wars and the conquest of Kars in the Caucasus.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4398f0448190810c840a82228706 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d5878948190a2aaab2ba31bd1ed |
completed | May 3, 2026, 7:09 p.m. |
| NEDg | Description generation | batch_69f79e9e6ff88190b031fb1403cacabc |
completed | May 3, 2026, 7:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7a2d6e7ec81908a4cbc324e793c24 |
completed | May 3, 2026, 7:32 p.m. |
Created at: April 9, 2026, 9:54 p.m.