Triple
T36989409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Section 14, Petaling Jaya |
E915050
|
entity |
| Predicate | hasTypeOfEateries |
P81236
|
FINISHED |
| Object | hawker stalls |
—
|
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: hawker stalls | Statement: [Section 14, Petaling Jaya, hasTypeOfEateries, hawker stalls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfEateries Context triple: [Section 14, Petaling Jaya, hasTypeOfEateries, hawker stalls]
-
A.
hasRestaurantType
chosen
Indicates that an entity is associated with or classified as a particular type or category of restaurant.
-
B.
alsoEats
Indicates that an entity consumes something in addition to another item or items it already eats.
-
C.
isDiningDestination
Indicates that a place serves as a destination where people go specifically to eat meals or dine.
-
D.
hasDishType
Indicates that an item (such as a food or menu entry) is classified as belonging to a particular type of dish (e.g., appetizer, main course, dessert).
-
E.
hasDiningOptionType
Indicates that an entity offers or is associated with a specific type or category of dining option (e.g., dine-in, takeout, delivery).
- 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_69f76e8dd0408190b8b46da118ea5128 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ffbb1c5bf88190a0bf791213045885 |
completed | May 9, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69ffba0ab0f881908f84ef81f7a1bfe8 |
completed | May 9, 2026, 10:49 p.m. |
Created at: May 3, 2026, 4:14 p.m.