Triple
T18441300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgy Natanson |
E450532
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Valentin and Valentina
"Valentin and Valentina" is a Soviet romantic drama film directed by Georgy Natanson, based on a play by Mikhail Roshchin about the challenges faced by two young lovers.
|
E1325713
|
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: Valentin and Valentina | Statement: [Georgy Natanson, notableWork, Valentin and Valentina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valentin and Valentina Context triple: [Georgy Natanson, notableWork, Valentin and Valentina]
-
A.
Valentinovna
Valentinovna is a Russian patronymic feminine middle name derived from the male given name Valentin.
-
B.
Valentinovich
Valentinovich is a Russian patronymic derived from the male given name Valentin, indicating "son of Valentin."
-
C.
Valery
Valery is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
D.
Valentin
Valentin is a masculine given name of Latin origin, commonly used in various European and Slavic countries.
-
E.
Valeria
Valeria was a late Roman province in the region of Pannonia, located in what is now western Hungary and parts of neighboring countries.
- 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: Valentin and Valentina Triple: [Georgy Natanson, notableWork, Valentin and Valentina]
Generated description
"Valentin and Valentina" is a Soviet romantic drama film directed by Georgy Natanson, based on a play by Mikhail Roshchin about the challenges faced by two young lovers.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Valentin and Valentina Target entity description: "Valentin and Valentina" is a Soviet romantic drama film directed by Georgy Natanson, based on a play by Mikhail Roshchin about the challenges faced by two young lovers.
-
A.
Valentinovna
Valentinovna is a Russian patronymic feminine middle name derived from the male given name Valentin.
-
B.
Valentinovich
Valentinovich is a Russian patronymic derived from the male given name Valentin, indicating "son of Valentin."
-
C.
Valery
Valery is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
D.
Valentin
Valentin is a masculine given name of Latin origin, commonly used in various European and Slavic countries.
-
E.
Valeria
Valeria was a late Roman province in the region of Pannonia, located in what is now western Hungary and parts of neighboring countries.
- 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_69d8d381d6388190a9e94e9c658174e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e51c10a86c819091196968b648fc92 |
completed | April 19, 2026, 6:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a040fdb4c648190aa56212c3c1e15ce |
completed | May 13, 2026, 5:44 a.m. |
| NEDg | Description generation | batch_6a0418c2621481909497c663272f5407 |
completed | May 13, 2026, 6:22 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a041bca3d688190972d5c0d5bba70ed |
completed | May 13, 2026, 6:35 a.m. |
Created at: April 10, 2026, 11:30 a.m.