Triple
T3709316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheka |
E80968
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
VChK
VChK is the Russian abbreviation for the Cheka, the Soviet Union’s first secret police and state security organization established after the 1917 Revolution.
|
E381726
|
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: VChK | Statement: [Cheka, shortName, VChK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VChK Context triple: [Cheka, shortName, VChK]
-
A.
VGIK
VGIK is Russia’s renowned national film school and one of the world’s oldest film institutes, known for training influential filmmakers such as Sergei Eisenstein.
-
B.
VKO
VKO is the IATA airport code for Vnukovo International Airport, one of Moscow’s major international airports in Russia.
-
C.
Avangard Omsk
Avangard Omsk is a prominent professional ice hockey club from Omsk, Russia, known as one of the country’s most successful and historically significant teams.
-
D.
Shaposhnikov
Shaposhnikov is a Russian surname most notably associated with Soviet military leader Boris Shaposhnikov.
-
E.
Kurskaya
Kurskaya is a Moscow Metro station on the Koltsevaya (Circle) Line, serving as a major transfer hub in the city’s rapid transit 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: VChK Triple: [Cheka, shortName, VChK]
Generated description
VChK is the Russian abbreviation for the Cheka, the Soviet Union’s first secret police and state security organization established after the 1917 Revolution.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: VChK Target entity description: VChK is the Russian abbreviation for the Cheka, the Soviet Union’s first secret police and state security organization established after the 1917 Revolution.
-
A.
VGIK
VGIK is Russia’s renowned national film school and one of the world’s oldest film institutes, known for training influential filmmakers such as Sergei Eisenstein.
-
B.
VKO
VKO is the IATA airport code for Vnukovo International Airport, one of Moscow’s major international airports in Russia.
-
C.
Avangard Omsk
Avangard Omsk is a prominent professional ice hockey club from Omsk, Russia, known as one of the country’s most successful and historically significant teams.
-
D.
Shaposhnikov
Shaposhnikov is a Russian surname most notably associated with Soviet military leader Boris Shaposhnikov.
-
E.
Kurskaya
Kurskaya is a Moscow Metro station on the Koltsevaya (Circle) Line, serving as a major transfer hub in the city’s rapid transit 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_69ad8b1793888190a5f70e4b21dc05a1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adc58233788190be3b912a50f61443 |
completed | March 8, 2026, 6:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4ce052660819089ad899a20b8a720 |
completed | March 14, 2026, 2:55 a.m. |
| NEDg | Description generation | batch_69b4cf85b968819085dad34a80767984 |
completed | March 14, 2026, 3:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4cfecf2bc8190afb3bd8ebfd3cc64 |
completed | March 14, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:33 p.m.