Triple
T35612792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Библиотека имени Ленина |
E1029082
|
entity |
| Predicate | типСобственности |
P7585
|
FINISHED |
| Object | государственная |
—
|
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: государственная | Statement: [Библиотека имени Ленина, типСобственности, государственная]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: типСобственности Context triple: [Библиотека имени Ленина, типСобственности, государственная]
-
A.
ownershipType
chosen
Indicates the specific nature or category of the ownership relationship that one entity holds over another.
-
B.
propertyType_id
Indicates a relationship that specifies the type or category of a property associated with an entity.
-
C.
типСтроения
Indicates the specific kind or category of building or structure associated with an entity.
-
D.
propertyTypeOf
Indicates that one entity is the type or category of property to which another entity belongs.
-
E.
propertyType_y
Indicates that one entity has a specific type or category of property in relation to another entity.
- 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_69f76e0653ec81909b1b813c126c6574 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79ecbd7dc8190ad5a206b51394814 |
completed | May 3, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69f79e4bdbcc8190be7a0d2cf8a77b64 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:05 p.m.