Triple
T8504882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Utica Square area |
E201308
|
entity |
| Predicate | isShoppingDestinationFor |
P47325
|
FINISHED |
| Object | Tulsa residents |
—
|
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: Tulsa residents | Statement: [Utica Square area, isShoppingDestinationFor, Tulsa residents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isShoppingDestinationFor Context triple: [Utica Square area, isShoppingDestinationFor, Tulsa residents]
-
A.
isShoppingDestination
chosen
Indicates that a place serves as a primary location where people go to shop for goods or services.
-
B.
isShoppingStreet
Indicates that a location functions primarily as a street characterized by a concentration of shops and commercial retail activity.
-
C.
hasShoppingMall
Indicates that one entity possesses, contains, or includes a shopping mall within its area or domain.
-
D.
hasShoppingDistrict
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
-
E.
hasShopsOn
Indicates that one entity (typically a street, area, or building) contains or is lined with shops located on or along it.
- 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_69ca831fe47c8190b5c57b456d2aefa0 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe5d8b7208190b199c56bf366c692 |
completed | March 31, 2026, 3:18 p.m. |
| PD | Predicate disambiguation | batch_69cbd10cfd208190a519049fad32c508 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:14 p.m.