Triple
T2004558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boris Godunov |
E43550
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Shuisky
Shuisky is a scheming boyar and political intriguer in Alexander Pushkin’s historical drama and Modest Mussorgsky’s opera "Boris Godunov."
|
E259608
|
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: Shuisky | Statement: [Boris Godunov, character, Shuisky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shuisky Context triple: [Boris Godunov, character, Shuisky]
-
A.
Vyatskoye
Vyatskoye is a rural locality in Russia’s Khabarovsk Krai, historically noted as the birthplace of North Korean leader Kim Jong Il.
-
B.
Shakhovskoye
Shakhovskoye is a rural locality in Russia known primarily as the birthplace of Soviet politician Mikhail Suslov.
-
C.
Rizhskaya
Rizhskaya is a Moscow Metro station on the Big Circle Line serving the Rizhsky railway terminal area.
-
D.
Skovorodino
Skovorodino is a small town in Russia’s Far Eastern Amur Oblast, known historically as a railway junction on the Trans-Siberian Railway.
-
E.
Paveletskaya
Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
- 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: Shuisky Triple: [Boris Godunov, character, Shuisky]
Generated description
Shuisky is a scheming boyar and political intriguer in Alexander Pushkin’s historical drama and Modest Mussorgsky’s opera "Boris Godunov."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shuisky Target entity description: Shuisky is a scheming boyar and political intriguer in Alexander Pushkin’s historical drama and Modest Mussorgsky’s opera "Boris Godunov."
-
A.
Vyatskoye
Vyatskoye is a rural locality in Russia’s Khabarovsk Krai, historically noted as the birthplace of North Korean leader Kim Jong Il.
-
B.
Shakhovskoye
Shakhovskoye is a rural locality in Russia known primarily as the birthplace of Soviet politician Mikhail Suslov.
-
C.
Rizhskaya
Rizhskaya is a Moscow Metro station on the Big Circle Line serving the Rizhsky railway terminal area.
-
D.
Skovorodino
Skovorodino is a small town in Russia’s Far Eastern Amur Oblast, known historically as a railway junction on the Trans-Siberian Railway.
-
E.
Paveletskaya
Paveletskaya is a Moscow Metro station named after the nearby Paveletsky railway terminal, serving as a key transport hub in the city’s network.
- 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_69a88715dbbc8190b2299e29e955d997 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb89717c88190ba506134c671d386 |
completed | March 7, 2026, 5:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea84704a08190ac2ddf0370587f14 |
completed | March 9, 2026, 11 a.m. |
| NEDg | Description generation | batch_69aea91ce164819091aa24b287f9fb8e |
completed | March 9, 2026, 11:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aea999b864819084134c670e7c5d9c |
completed | March 9, 2026, 11:06 a.m. |
Created at: March 4, 2026, 7:37 p.m.