Triple
T30882267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaufhaus des Westens |
E786650
|
entity |
| Predicate | hasSalesArea |
P25135
|
FINISHED |
| Object | approximately 60,000 square metres |
—
|
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: approximately 60,000 square metres | Statement: [Kaufhaus des Westens, hasSalesArea, approximately 60,000 square metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSalesArea Context triple: [Kaufhaus des Westens, hasSalesArea, approximately 60,000 square metres]
-
A.
hasSalesComponent
Indicates that something includes, involves, or is associated with a sales-related element or function.
-
B.
hasRetailArea
chosen
Indicates that an entity possesses or includes a designated space used for retail or commercial sales activities.
-
C.
hasServiceAreas
Indicates that an entity provides services within, or is operational across, specific geographic or functional areas.
-
D.
isPartOfBusinessArea
Indicates that one entity belongs to, is included within, or falls under the scope of a particular business area.
-
E.
hasKeyBusinessArea
Indicates that an entity is associated with or operates within a particular primary business area or domain.
- 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_69f224bae17c8190bb3a6a28e3d019df |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f73223675481908c1bc3208c0f5284 |
completed | May 3, 2026, 11:31 a.m. |
| PD | Predicate disambiguation | batch_69f7317690108190b3aae2cd2e1d069e |
completed | May 3, 2026, 11:28 a.m. |
Created at: April 29, 2026, 8:48 p.m.