Triple
T7554894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King of Prussia Mall |
E178636
|
entity |
| Predicate | hasDiningArea |
P55802
|
FINISHED |
| Object | food court |
—
|
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: food court | Statement: [King of Prussia Mall, hasDiningArea, food court]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDiningArea Context triple: [King of Prussia Mall, hasDiningArea, food court]
-
A.
hasDiningFeature
chosen
Indicates that something possesses a specific characteristic, amenity, or attribute related to dining.
-
B.
hasCharacterDining
Indicates that an entity offers or includes dining experiences where guests can eat while interacting with costumed characters.
-
C.
isDiningDestination
Indicates that a place serves as a destination where people go specifically to eat meals or dine.
-
D.
hasDiningPolicy
Indicates that an entity enforces or follows a specific set of rules or guidelines related to dining or meal-related activities.
-
E.
diningStyle
Indicates the manner or format in which dining is conducted, such as casual, formal, buffet, or family-style.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8d82dd481908351876edc70c4ec |
completed | March 27, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69c6f4daad6c8190af2b8ae88d2c8cb7 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:49 p.m.