Triple
T30270719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turnbull & Asser |
E769785
|
entity |
| Predicate | retailModel |
P80649
|
FINISHED |
| Object | brick-and-mortar stores |
—
|
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: brick-and-mortar stores | Statement: [Turnbull & Asser, retailModel, brick-and-mortar stores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: retailModel Context triple: [Turnbull & Asser, retailModel, brick-and-mortar stores]
-
A.
retailConcept
chosen
Indicates that one entity represents a retail-related concept, model, or framework that characterizes or defines the nature of another entity’s retail activity or context.
-
B.
hasRetailCharacteristic
Indicates that an entity possesses a specific attribute, feature, or quality relevant to retail contexts (such as pricing, packaging, or point-of-sale properties).
-
C.
hasRetailProduct
Indicates that an entity offers, sells, or makes available a particular product in a retail context.
-
D.
hasRetailOption
Indicates that one entity offers, includes, or is associated with a particular retail option (such as a sales channel, purchase method, or retail configuration) for another entity.
-
E.
hasRetailUnits
Indicates that one entity possesses, operates, or is associated with one or more retail units (such as stores or outlets).
- 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_69f224856d9881908c7f0dd64f059672 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f680d43c708190a28635b6f09d3895 |
completed | May 2, 2026, 10:55 p.m. |
| PD | Predicate disambiguation | batch_69f6760216108190bbb708d53a6c2c25 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 29, 2026, 7:43 p.m.