Triple
T35365961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goldsmith’s |
E1021628
|
entity |
| Predicate | hadStoreFormat |
P4952
|
FINISHED |
| Object | mall-based department store |
—
|
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: mall-based department store | Statement: [Goldsmith’s, hadStoreFormat, mall-based department store]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadStoreFormat Context triple: [Goldsmith’s, hadStoreFormat, mall-based department store]
-
A.
hadFormat
Indicates that a resource was previously available in a different format or media type than it is now.
-
B.
hasRetailFormat
chosen
Indicates that one entity operates or is organized according to a particular retail format or store type.
-
C.
hasShop
Indicates that one entity owns, operates, or is associated with a shop or retail establishment.
-
D.
hasRetailHistory
Indicates that an entity has a past record of involvement in retail activities, such as operating, selling, or transacting in retail contexts.
-
E.
hadSpecialShopSystem
Indicates that an entity implemented or used a distinct, non-standard shop or purchasing system compared to the usual one.
- 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_69f76df000488190ab7c97f565677055 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79533b88c8190934ec4cb21770e24 |
completed | May 3, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69f79104f5b48190a496cdffde8472da |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4:03 p.m.