Triple
T69691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Furnes (Veurne) |
E1393
|
entity |
| Predicate | hasBuildingType |
P1844
|
FINISHED |
| Object | historic civic buildings |
—
|
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: historic civic buildings | Statement: [Furnes (Veurne), hasBuildingType, historic civic buildings]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBuildingType Context triple: [Furnes (Veurne), hasBuildingType, historic civic buildings]
-
A.
hasBuildingHeightType
Indicates the classification or type used to characterize the height of a building in the relationship.
-
B.
buildingType
chosen
Indicates the specific category or function that characterizes what kind of building something is.
-
C.
usesBuilding
Indicates that one entity makes use of, occupies, or operates within a particular building.
-
D.
hasFacilityType
Indicates that an entity possesses or is associated with a specific type or category of facility.
-
E.
hasInfrastructureType
Indicates that an entity possesses or is associated with a specific category or type of infrastructure.
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24eaa0df88190add55579b2b9fd02 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.