Triple
T25862238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bupyeong Market |
E651511
|
entity |
| Predicate | hasShoppingAreaType |
P35906
|
FINISHED |
| Object | underground shopping center |
—
|
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: underground shopping center | Statement: [Bupyeong Market, hasShoppingAreaType, underground shopping center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasShoppingAreaType Context triple: [Bupyeong Market, hasShoppingAreaType, underground shopping center]
-
A.
hasShoppingDistrictType
chosen
Indicates that an entity is associated with a particular type or category of shopping district.
-
B.
hasShoppingDistrict
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
-
C.
hasShoppingMall
Indicates that one entity possesses, contains, or includes a shopping mall within its area or domain.
-
D.
hasRetailArea
Indicates that an entity possesses or includes a designated space used for retail or commercial sales activities.
-
E.
hasShoppingDistrictName
Indicates that an entity’s shopping district is identified by a specific name.
- 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_69e7ab3a199c81909227cb964cacfe24 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c6456608190b94e7c2e2c2a4824 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 22, 2026, 8:05 a.m.