Triple
T11233482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roppongi Hills |
E265883
|
entity |
| Predicate | containsShoppingMall |
P16039
|
FINISHED |
| Object | Roppongi Hills shops and restaurants |
—
|
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: Roppongi Hills shops and restaurants | Statement: [Roppongi Hills, containsShoppingMall, Roppongi Hills shops and restaurants]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsShoppingMall Context triple: [Roppongi Hills, containsShoppingMall, Roppongi Hills shops and restaurants]
-
A.
hasShoppingMall
chosen
Indicates that one entity possesses, contains, or includes a shopping mall within its area or domain.
-
B.
hasShoppingMallName
Indicates that an entity (such as a shopping mall) is associated with or identified by a specific name.
-
C.
hasShoppingDistrict
Indicates that a place contains or is associated with a designated area where multiple shops and commercial retail activities are concentrated.
-
D.
hasShop
Indicates that one entity owns, operates, or is associated with a shop or retail establishment.
-
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_69d6aac656d48190b275efaa7d6074ee |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e903b8ec81909f9c89776d35c650 |
completed | April 9, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69d75cfdf7a88190aae21572e57ef208 |
completed | April 9, 2026, 8:02 a.m. |
Created at: April 8, 2026, 9:30 p.m.