Triple
T20670200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alexis Nihon Complex |
E507999
|
entity |
| Predicate | hasRetailAreaType |
P25135
|
FINISHED |
| Object | enclosed shopping mall |
—
|
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: enclosed shopping mall | Statement: [Alexis Nihon Complex, hasRetailAreaType, enclosed shopping mall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRetailAreaType Context triple: [Alexis Nihon Complex, hasRetailAreaType, enclosed shopping mall]
-
A.
hasRetailArea
chosen
Indicates that an entity possesses or includes a designated space used for retail or commercial sales activities.
-
B.
hasRetailCategory
Indicates that an entity is associated with a specific retail category or type of retail business.
-
C.
hasRetailPresenceIn
Indicates that an entity conducts retail operations or maintains a retail outlet, store, or sales presence within a specified location.
-
D.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
hasRetailCharacteristic
Indicates that an entity possesses a specific attribute, feature, or quality relevant to retail contexts (such as pricing, packaging, or point-of-sale properties).
- 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_69e0b4c059bc81908ea762cd73ea4424 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b5c735048190a01cb7692928d66e |
completed | April 20, 2026, 11:24 p.m. |
| PD | Predicate disambiguation | batch_69e5c03caee881908be4dd25796a03d5 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 11:44 a.m.