Triple
T17431714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ossi |
E423886
|
entity |
| Predicate | hasTraditionalRuralCharacter |
P113659
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Ossi, hasTraditionalRuralCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTraditionalRuralCharacter Context triple: [Ossi, hasTraditionalRuralCharacter, true]
-
A.
hasRuralLandscapeType
Indicates that an entity is associated with or characterized by a specific type of rural landscape.
-
B.
semiRuralCharacter
Indicates that a place or area has characteristics intermediate between rural and urban, combining elements of both environments.
-
C.
hasRuralLifestyle
chosen
Indicates that an entity lives in or regularly engages in a way of life characteristic of rural areas, such as farming, low population density, and countryside-oriented activities.
-
D.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
E.
hasTraditionalSettlementType
Indicates that an entity is associated with a specific traditional or historically established type of human settlement (e.g., village, town, hamlet).
- 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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4490072b48190a39b1ac7bb5eb035 |
completed | April 19, 2026, 3:16 a.m. |
| PD | Predicate disambiguation | batch_69e3b030eac481909b8402719cc3102e |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:46 a.m.