Triple
T20734302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ted Baker |
E509652
|
entity |
| Predicate | firstStoreType |
P40799
|
FINISHED |
| Object | menswear shop |
—
|
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: menswear shop | Statement: [Ted Baker, firstStoreType, menswear shop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstStoreType Context triple: [Ted Baker, firstStoreType, menswear shop]
-
A.
storeType
chosen
Indicates the category or kind of store associated with an entity, such as its retail or service type.
-
B.
firstStageType
Indicates that one entity is the type or category of the first stage or initial phase associated with another entity.
-
C.
openedFirstHypermarket
Indicates that the subject entity was the first to open a hypermarket for the object entity or within the context specified.
-
D.
firstTargetStoreLocation
Indicates the location of the initial or primary store that is targeted in a given context or operation.
-
E.
establishmentType
Indicates the specific kind or category of establishment that an entity is classified as.
- 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_69e0b4c589c08190834fb5d86d0efa2b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1ef3040819085c8056e75571104 |
completed | April 21, 2026, 12:16 a.m. |
| PD | Predicate disambiguation | batch_69e5c04b31248190b9b9d91b5cb854e3 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:31 p.m.