Triple
T2972630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Galeria Kaufhof |
E80314
|
entity |
| Predicate | hasStoreLocationType |
P28105
|
FINISHED |
| Object | urban centers |
—
|
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: urban centers | Statement: [Galeria Kaufhof, hasStoreLocationType, urban centers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStoreLocationType Context triple: [Galeria Kaufhof, hasStoreLocationType, urban centers]
-
A.
hasMainLocationType
chosen
Indicates that an entity is associated with a primary or predominant type of location that characterizes where it is mainly situated or operates.
-
B.
hasRetailPresenceIn
Indicates that an entity conducts retail operations or maintains a retail outlet, store, or sales presence within a specified location.
-
C.
storeType
Indicates the category or kind of store associated with an entity, such as its retail or service type.
-
D.
hasTeamStore
Indicates that an entity operates, is associated with, or provides access to a specific team-related retail store.
-
E.
hasShopsOn
Indicates that one entity (typically a street, area, or building) contains or is lined with shops located on or along it.
- 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_69ad8b14ffe881908ffed62f9595c867 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad998656948190ba79d7196d735f34 |
completed | March 8, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69ad96105a708190a9ec4838cbcb1207 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:58 p.m.