Triple
T36787683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | «А зори здесь тихие» |
E908965
|
entity |
| Predicate | hasFemaleCharactersMajority |
P139120
|
FINISHED |
| Object | да |
—
|
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: да | Statement: [«А зори здесь тихие», hasFemaleCharactersMajority, да]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFemaleCharactersMajority Context triple: [«А зори здесь тихие», hasFemaleCharactersMajority, да]
-
A.
hasFemaleCharacter
Indicates that an entity includes or features at least one female character.
-
B.
hasGenderRatioFemale
chosen
Indicates the proportion or percentage of females relative to the total population in the described group or context.
-
C.
hasStrongFemaleCharacters
Indicates that the work features prominent, well-developed female characters who display agency, complexity, and significant influence on the narrative or outcome.
-
D.
numberOfMainFemaleLeadsInWork
Indicates the number of primary female lead characters that appear in a given work.
-
E.
hasLeadCharacterGender
Indicates that the primary or lead character in a work has a specified gender.
- 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_69f76e7a937c81909ed7359641e670f6 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd02680d948190a3463fb119ba8556 |
completed | May 7, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69fcf89c69b4819082bbc564bd15137d |
completed | May 7, 2026, 8:39 p.m. |
Created at: May 3, 2026, 4:12 p.m.