Triple
T316454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sergei Eisenstein |
E7716
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object |
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.
|
E40646
|
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: VGIK | Statement: [Sergei Eisenstein, employer, VGIK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VGIK Context triple: [Sergei Eisenstein, employer, VGIK]
-
A.
GVB
GVB is Amsterdam’s primary public transport company, operating the city’s trams, buses, metro, and ferries.
-
B.
Gori
Gori is a city in central Georgia best known as the birthplace of Soviet leader Joseph Stalin.
-
C.
KGB
The KGB was the Soviet Union’s main security and intelligence organization, responsible for state security, espionage, and political repression during much of the Cold War.
-
D.
Tverya
Tverya is the Hebrew name for Tiberias, an ancient city in northern Israel on the western shore of the Sea of Galilee known for its religious significance and hot springs.
-
E.
Golus
Golus is a Yiddish term referring to the Jewish exile and dispersion from their ancestral homeland, encompassing both the physical diaspora and its spiritual-historical implications.
- 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: VGIK Triple: [Sergei Eisenstein, employer, VGIK]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: VGIK Target entity description: 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.
-
A.
GVB
GVB is Amsterdam’s primary public transport company, operating the city’s trams, buses, metro, and ferries.
-
B.
Gori
Gori is a city in central Georgia best known as the birthplace of Soviet leader Joseph Stalin.
-
C.
KGB
The KGB was the Soviet Union’s main security and intelligence organization, responsible for state security, espionage, and political repression during much of the Cold War.
-
D.
Tverya
Tverya is the Hebrew name for Tiberias, an ancient city in northern Israel on the western shore of the Sea of Galilee known for its religious significance and hot springs.
-
E.
Golus
Golus is a Yiddish term referring to the Jewish exile and dispersion from their ancestral homeland, encompassing both the physical diaspora and its spiritual-historical implications.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea6462148190825acc57f6d2adaf |
completed | Feb. 28, 2026, 1:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3c41c00888190861d2b6d4bfc8904 |
completed | March 1, 2026, 4:44 a.m. |
| NEDg | Description generation | batch_69a3c5cca244819082e9565558f23dbe |
completed | March 1, 2026, 4:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3c64599ac81908e30e5452cc91b97 |
completed | March 1, 2026, 4:53 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.