Triple
T5881566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Three Sisters |
E130759
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Chebutykin
Chebutykin is the aging, disillusioned army doctor whose cynicism and emotional detachment embody the themes of lost hope and stagnation in Anton Chekhov’s play "Three Sisters."
|
E555774
|
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: Chebutykin | Statement: [Three Sisters, mainCharacter, Chebutykin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chebutykin Context triple: [Three Sisters, mainCharacter, Chebutykin]
-
A.
Nikitin
Nikitin is a Russian surname borne by numerous notable figures in fields such as art, science, and sports.
-
B.
Vyazemsky
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
C.
Kuntsevskaya
Kuntsevskaya is a Moscow Metro station on the Big Circle Line serving the Kuntsevo District in western Moscow.
-
D.
Lukyanov
Lukyanov is a Russian surname borne by various notable figures in politics, science, and the arts.
-
E.
Kozlov
Kozlov is a historic Russian town, now known as Michurinsk, that developed as a significant regional center of trade and agriculture.
- 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: Chebutykin Triple: [Three Sisters, mainCharacter, Chebutykin]
Generated description
Chebutykin is the aging, disillusioned army doctor whose cynicism and emotional detachment embody the themes of lost hope and stagnation in Anton Chekhov’s play "Three Sisters."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chebutykin Target entity description: Chebutykin is the aging, disillusioned army doctor whose cynicism and emotional detachment embody the themes of lost hope and stagnation in Anton Chekhov’s play "Three Sisters."
-
A.
Nikitin
Nikitin is a Russian surname borne by numerous notable figures in fields such as art, science, and sports.
-
B.
Vyazemsky
Vyazemsky is a small town in Russia’s Far Eastern Federal District, serving as an administrative center within Khabarovsk Krai.
-
C.
Kuntsevskaya
Kuntsevskaya is a Moscow Metro station on the Big Circle Line serving the Kuntsevo District in western Moscow.
-
D.
Lukyanov
Lukyanov is a Russian surname borne by various notable figures in politics, science, and the arts.
-
E.
Kozlov
Kozlov is a historic Russian town, now known as Michurinsk, that developed as a significant regional center of trade and agriculture.
- 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_69c0085523688190bfd487479ce819e6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03635a41c819086f9a5df242777f2 |
completed | March 22, 2026, 6:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0bff29200819080c7d9269ad80c53 |
completed | March 23, 2026, 4:22 a.m. |
| NEDg | Description generation | batch_69c0c1db6d548190ba4be143aa7c7905 |
completed | March 23, 2026, 4:30 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c0c2bdd44881909aa85589d31e771a |
completed | March 23, 2026, 4:34 a.m. |
Created at: March 22, 2026, 3:57 p.m.