Triple
T25273604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shoprite Holdings |
E633632
|
entity |
| Predicate | hasApproximateStoreCount |
P160109
|
FINISHED |
| Object | thousands of 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: thousands of stores | Statement: [Shoprite Holdings, hasApproximateStoreCount, thousands of stores]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApproximateStoreCount Context triple: [Shoprite Holdings, hasApproximateStoreCount, thousands of stores]
-
A.
hasApproximateMemberCount
Indicates that an entity is associated with a group or collection for which only an estimated or non-exact number of members is known.
-
B.
hasApproximateEntryCount
Indicates that an entity is associated with a number representing an estimated or non-exact count of its entries.
-
C.
hasApproximateBrickCount
Indicates that an entity is associated with an estimated or non-exact number of bricks.
-
D.
approximateDocumentCount
Indicates an estimated number of documents associated with or contained by a given entity, rather than an exact count.
-
E.
prototypeCountApproximate
Indicates that the number of prototypes involved is an estimated or approximate count rather than an exact value.
- F. None of above. chosen
Provenance (4 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_69e75a92f48881909974ff9c11150a2e |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f6018f91248190985323d1a678e539 |
completed | May 2, 2026, 1:52 p.m. |
| PD | Predicate disambiguation | batch_69f5f7f99dc08190afcfb3bc4dfbec1d |
completed | May 2, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69f5ffc6268c8190b63f6360ebadab73 |
completed | May 2, 2026, 1:44 p.m. |
Created at: April 21, 2026, 1:17 p.m.