Triple
T35424465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vologodsky District |
E1023880
|
entity |
| Predicate | hasTypeInRussianLaw |
P69225
|
FINISHED |
| Object | municipal district |
—
|
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: municipal district | Statement: [Vologodsky District, hasTypeInRussianLaw, municipal district]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeInRussianLaw Context triple: [Vologodsky District, hasTypeInRussianLaw, municipal district]
-
A.
containsLawType
chosen
Indicates that one entity includes or is associated with a specific type or category of law.
-
B.
hasLegalSystemType
Indicates that an entity possesses or is governed by a particular type or form of legal system.
-
C.
containsLaw
Indicates that one entity (such as a document, code, or jurisdiction) includes or encompasses a specific law within it.
-
D.
legalCodeType
Indicates the specific category or classification of a legal code that applies to an entity or situation.
-
E.
hasLegalStatusInSaudiLaw
Indicates that an entity possesses a specific legal status or recognition under the laws and regulations of Saudi Arabia.
- 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_69f76df6704081909900c60be10d5849 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff795d25d08190b7584c72be39d309 |
completed | May 9, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69ff78a90fbc8190a62c57456dc1d4ad |
completed | May 9, 2026, 6:10 p.m. |
Created at: May 3, 2026, 4:03 p.m.