Triple
T454632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Market Square, Knoxville |
E7206
|
entity |
| Predicate | hasShops |
P4285
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Market Square, Knoxville, hasShops, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasShops Context triple: [Market Square, Knoxville, hasShops, yes]
-
A.
hasShoppingDistrict
chosen
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
-
B.
hasGiftShop
Indicates that an entity includes or provides access to a gift shop as part of its facilities or services.
-
C.
numberOfStores
Indicates the total count of stores associated with a given entity or context.
-
D.
hasRetailFormat
Indicates that one entity operates or is organized according to a particular retail format or store type.
-
E.
isCommercialHubIn
Indicates that a place functions as a primary center of commercial or business activity within a specified larger area or region.
- 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_69a2e7e5c5bc8190a1dc8178218fba40 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef87cc7c8190a0fec933457821e2 |
completed | Feb. 28, 2026, 1:37 p.m. |
| PD | Predicate disambiguation | batch_69a2ede4de008190b5a6c159e741522e |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.