Triple
T18495673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | H2O Retailing Corporation |
E451939
|
entity |
| Predicate | typeOfRetail |
P17849
|
FINISHED |
| Object | brick-and-mortar retail |
—
|
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 retail | Statement: [H2O Retailing Corporation, typeOfRetail, brick-and-mortar retail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfRetail Context triple: [H2O Retailing Corporation, typeOfRetail, brick-and-mortar retail]
-
A.
merchantType
Indicates the category or classification of a merchant based on the type of goods or services they provide or the business model they operate under.
-
B.
retailConcept
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.
-
C.
hasRetailCategory
chosen
Indicates that an entity is associated with a specific retail category or type of retail business.
-
D.
hasRetailPresenceIn
Indicates that an entity conducts retail operations or maintains a retail outlet, store, or sales presence within a specified location.
-
E.
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).
- 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_69d8d3855d50819097fc8561b0299dd9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e532bfeef4819096b2fa28abb662b9 |
completed | April 19, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69e469dbf5208190b6fc49e02a087f54 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 11:35 a.m.