Triple
T25284228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Makwanpur District |
E633893
|
entity |
| Predicate | hasNumberOfRuralMunicipalities |
P57461
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Makwanpur District, hasNumberOfRuralMunicipalities, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfRuralMunicipalities Context triple: [Makwanpur District, hasNumberOfRuralMunicipalities, 8]
-
A.
hasNumberOfRuralSettlements
Indicates the quantity of rural settlements associated with or contained within a given entity.
-
B.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
C.
hasNumberOfRuralGminas
chosen
Indicates the total count of rural gminas (rural administrative districts) associated with a given territorial unit or entity.
-
D.
hasRuralCommunes
Indicates that an entity possesses, includes, or is associated with one or more rural communes.
-
E.
hasLongRuralSectionsIn
Indicates that something (such as a route or infrastructure) contains extended stretches that pass through rural areas within a specified region or location.
- 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_69e75a9402fc81909362ca85277c06d9 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f638d11c988190af7fd4572b08e038 |
completed | May 2, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69f63706b6008190993577193c85ff50 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 21, 2026, 1:19 p.m.