Triple
T316479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sergei Eisenstein |
E7716
|
entity |
| Predicate | taughtAt |
P1203
|
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: [Sergei Eisenstein, taughtAt, VGIK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VGIK Context triple: [Sergei Eisenstein, taughtAt, 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.
GVB
GVB is Amsterdam’s primary public transport company, operating the city’s trams, buses, metro, and ferries.
-
C.
Gori
Gori is a city in central Georgia best known as the birthplace of Soviet leader Joseph Stalin.
-
D.
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.
-
E.
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.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea65ca7081908093e6aaaf2d34f7 |
completed | Feb. 28, 2026, 1:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3c8b8d7d88190b43f7b6b0289445f |
completed | March 1, 2026, 5:03 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.