Triple
T8625037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moscow Does Not Believe in Tears (1980 film) |
E204260
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Lyudmila |
E615016
|
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: Lyudmila | Statement: [Moscow Does Not Believe in Tears (1980 film), mainCharacter, Lyudmila]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyudmila Context triple: [Moscow Does Not Believe in Tears (1980 film), mainCharacter, Lyudmila]
-
A.
Lyudmila
Lyudmila is a Russian linguist and the former First Lady of Russia, known for being the ex-wife of President Vladimir Putin.
-
B.
Lyudmila
chosen
Lyudmila is a common Russian female given name, notably borne by figures such as Soviet World War II sniper Lyudmila Pavlichenko.
-
C.
Ludmila
Ludmila is the heroine of Alexander Pushkin’s narrative poem "Ruslan and Ludmila," known as a beautiful Kievan princess whose abduction sets the story’s adventurous plot in motion.
-
D.
Ludmilla
Ludmilla is a coastal suburb of Darwin in Australia's Northern Territory, known for its residential areas and proximity to Fannie Bay.
-
E.
Galina
Galina is a feminine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
- 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_69ca834a4ea0819094970dceb9e389f3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc472a07908190a2368975459543f9 |
completed | March 31, 2026, 10:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebbf03c688190a989f16675f6e8a6 |
completed | April 2, 2026, 6:56 p.m. |
Created at: March 30, 2026, 6:26 p.m.