Triple
T36543145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coles |
E901073
|
entity |
| Predicate | numberOfStoresRegion |
P8902
|
FINISHED |
| Object | over 800 supermarkets in Australia |
—
|
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: over 800 supermarkets in Australia | Statement: [Coles, numberOfStoresRegion, over 800 supermarkets in Australia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStoresRegion Context triple: [Coles, numberOfStoresRegion, over 800 supermarkets in Australia]
-
A.
numberOfStores
chosen
Indicates the total count of stores associated with a given entity or context.
-
B.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
C.
hasNumberOfRegionalOffices
Indicates the quantity of regional offices that an entity possesses or operates.
-
D.
numberOfDistributionCenters
Indicates the quantity of distribution centers associated with a given entity.
-
E.
hasRetailStores
Indicates that an entity operates or possesses one or more physical retail store locations.
- 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_69f76e61217081908b79d610fe67b013 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:11 p.m.