Triple
T7245293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Criminal Code of the RSFSR |
E156455
|
entity |
| Predicate | appliedUniformlyAcross |
P4880
|
FINISHED |
| Object | RSFSR territory |
—
|
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: RSFSR territory | Statement: [Criminal Code of the RSFSR, appliedUniformlyAcross, RSFSR territory]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedUniformlyAcross Context triple: [Criminal Code of the RSFSR, appliedUniformlyAcross, RSFSR territory]
-
A.
appliesAcross
Indicates that a condition, rule, or property holds uniformly over multiple items, cases, or contexts.
-
B.
usedUniformlyAcrossCountry
chosen
Indicates that something is applied or practiced in the same way throughout the entire country without regional variation.
-
C.
usedAcross
Indicates that something is utilized or applied in multiple different contexts, locations, or domains.
-
D.
appliedAs
Indicates that one entity submitted itself or was put forward for consideration in a particular role, position, or context relative to another entity.
-
E.
usesUniform
Indicates that one entity regularly wears or employs a standardized set of clothing or equipment designated as a uniform.
- 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_69c68827b5e481908dc05e145b2c92d4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea596fdc8190b2115363f1033441 |
completed | March 27, 2026, 8:36 p.m. |
| PD | Predicate disambiguation | batch_69c6e7666ffc81908bf643d8257e6337 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:56 p.m.