Triple
T11090379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aleksandra Khokhlova |
E262234
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | VGIK |
E40646
|
NE FINISHED |
How this triple was built (2 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: VGIK | Statement: [Aleksandra Khokhlova, employer, VGIK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VGIK Context triple: [Aleksandra Khokhlova, employer, VGIK]
-
A.
VGIK
chosen
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.
VKSU
VKSU is a public university located in Ara, Bihar, India, offering undergraduate and postgraduate programs across various disciplines.
-
C.
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.
-
D.
VIKI
VIKI is the central artificial intelligence system and primary antagonist in the science fiction film "I, Robot," overseeing and controlling robots under a rigid interpretation of the Three Laws of Robotics.
-
E.
VU
VU is a major research university in Amsterdam, Netherlands, known for its wide range of academic programs and emphasis on interdisciplinary and socially engaged scholarship.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799e96ca08190838c8a04d1eb2a16 |
completed | April 9, 2026, 12:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e7c586808190a576803b7406a49e |
completed | April 18, 2026, 8:21 p.m. |
Created at: April 8, 2026, 9:27 p.m.