Triple
T15456731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seibu Department Store |
E371788
|
entity |
| Predicate | typicalStoreFeature |
P5084
|
FINISHED |
| Object | multiple themed floors |
—
|
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: multiple themed floors | Statement: [Seibu Department Store, typicalStoreFeature, multiple themed floors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalStoreFeature Context triple: [Seibu Department Store, typicalStoreFeature, multiple themed floors]
-
A.
storeType
Indicates the category or kind of store associated with an entity, such as its retail or service type.
-
B.
reportedlyStores
Indicates that an entity is said or believed, based on reports or claims, to store or hold another entity, without confirming that this storage actually occurs.
-
C.
typicalStorePlacement
Indicates the usual or standard physical location where an item is placed or displayed within a store.
-
D.
typicalFeatures
chosen
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
E.
storefront
Indicates the physical or virtual front-facing location where a business presents and offers its goods or services to customers.
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f146a2c8190882741af3ec15268 |
completed | April 16, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ded28276f481908c2038bb301e57cf |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:31 a.m.