Triple
T27085860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Service for State Registration, Cadastre and Cartography |
E686032
|
entity |
| Predicate | hasRussianAcronym |
P82788
|
FINISHED |
| Object | Росреестр |
—
|
NE NERFINISHED |
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: Росреестр | Statement: [Federal Service for State Registration, Cadastre and Cartography, hasRussianAcronym, Росреестр]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRussianAcronym Context triple: [Federal Service for State Registration, Cadastre and Cartography, hasRussianAcronym, Росреестр]
-
A.
hasRussianVersion
Indicates that something has a corresponding version or counterpart that is in the Russian language.
-
B.
hasAcronymVariantLanguage
Indicates that a language has an alternative form represented as an acronym variant.
-
C.
acronymOfNativeName
chosen
Indicates that one term is an acronym formed from the native-language version of another name.
-
D.
hasAcronymInItalian
Indicates that an entity is associated with an acronym specifically in the Italian language.
-
E.
hasAcronymOrigin
Indicates that an acronym is derived from or originates from a specific longer expression or name.
- 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_69ef148940ec819097b5c20fbfbf7c81 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c663be481908f233d25d28713a4 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 8:37 a.m.