Triple
T6361646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The House of the Dead |
E143122
|
entity |
| Predicate | publisher |
P29
|
FINISHED |
| Object |
Vremya
Vremya is a Russian publishing house known for releasing literary works, including notable Russian novels.
|
E587944
|
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: Vremya | Statement: [The House of the Dead, publisher, Vremya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vremya Context triple: [The House of the Dead, publisher, Vremya]
-
A.
Smutnoye vremya
Smutnoye vremya is the Russian name for the Time of Troubles, a period of political crisis, famine, and foreign intervention in Russia between the late 16th and early 17th centuries.
-
B.
Yuriatin
Yuriatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Yuri Zhivago’s life and relationships.
-
C.
Nečas
Nečas is a Czech surname most notably borne by Petr Nečas, a former Prime Minister of the Czech Republic.
-
D.
Vremya pokažet
Vremya pokažet is a Russian political talk show known for its live discussions and debates on current social and international issues.
-
E.
Nzaman
Nzaman is a dialect of the Fang language spoken by Fang communities in Central Africa.
- 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: Vremya Triple: [The House of the Dead, publisher, Vremya]
Generated description
Vremya is a Russian publishing house known for releasing literary works, including notable Russian novels.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vremya Target entity description: Vremya is a Russian publishing house known for releasing literary works, including notable Russian novels.
-
A.
Smutnoye vremya
Smutnoye vremya is the Russian name for the Time of Troubles, a period of political crisis, famine, and foreign intervention in Russia between the late 16th and early 17th centuries.
-
B.
Yuriatin
Yuriatin is a fictional Russian town in Boris Pasternak’s novel "Doctor Zhivago," serving as a key setting in Yuri Zhivago’s life and relationships.
-
C.
Nečas
Nečas is a Czech surname most notably borne by Petr Nečas, a former Prime Minister of the Czech Republic.
-
D.
Vremya pokažet
Vremya pokažet is a Russian political talk show known for its live discussions and debates on current social and international issues.
-
E.
Nzaman
Nzaman is a dialect of the Fang language spoken by Fang communities in Central Africa.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067fa0d0c819098d01545849142fc |
completed | March 22, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d6d906481908b5883bff18ceec8 |
completed | March 27, 2026, 7:10 a.m. |
| NEDg | Description generation | batch_69c62e2072808190a4f2dd262b631c88 |
completed | March 27, 2026, 7:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c62f1bbdac8190b0cff9fbcddd68a7 |
completed | March 27, 2026, 7:17 a.m. |
Created at: March 22, 2026, 4:32 p.m.