Triple
T1825691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mosfilm |
E40647
|
entity |
| Predicate | digitizes |
P22918
|
FINISHED |
| Object | Soviet-era films |
—
|
LITERAL 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: Soviet-era films | Statement: [Mosfilm, digitizes, Soviet-era films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: digitizes Context triple: [Mosfilm, digitizes, Soviet-era films]
-
A.
digitizedBy
chosen
Indicates that a physical or analog item has been converted into digital form by a particular agent or organization.
-
B.
hasDigitalForm
Indicates that something exists or is available in a digital or electronic format.
-
C.
hasDigitalEncoding
Indicates that one entity is represented, stored, or expressed using a specific digital code or encoding scheme provided by another entity.
-
D.
recognizesText
Indicates that one entity detects and correctly identifies written or printed text present in or associated with another entity.
-
E.
hasDigitalArchive
Indicates that an entity maintains or is associated with a collection of materials stored in a digital archive.
- F. None of above.
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_69a8864644bc8190b2358ab897194ac1 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb45402688190b9a535b14030c354 |
completed | March 7, 2026, 5:15 a.m. |
| PD | Predicate disambiguation | batch_69abafd6a9948190ac2b2743db6f8f69 |
completed | March 7, 2026, 4:55 a.m. |
Created at: March 4, 2026, 7:32 p.m.