Triple
T9134706
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simon Premium Outlets |
E219171
|
entity |
| Predicate | hasLeasingType |
P17722
|
FINISHED |
| Object | long-term retail leases |
—
|
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: long-term retail leases | Statement: [Simon Premium Outlets, hasLeasingType, long-term retail leases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLeasingType Context triple: [Simon Premium Outlets, hasLeasingType, long-term retail leases]
-
A.
isPrivatelyLeased
Indicates that an entity is under a lease agreement held by a private (non-governmental) party rather than being publicly owned or leased.
-
B.
wasLeasedFor
Indicates that one entity was leased in exchange for a specified payment amount, purpose, or consideration.
-
C.
rentalModel
chosen
Indicates that one entity is used or provided to another under a specific rental arrangement, defining how the rental relationship is structured or operates.
-
D.
hasAcquisitionType
Indicates the specific kind or category of acquisition relationship that exists between one entity acquiring another.
-
E.
hasCartRental
Indicates that an entity provides or is associated with the service of renting carts to another entity.
- 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_69ca83debfc0819095800583e97ab10f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8de0dec8190978c80b9ec8bf25c |
completed | April 1, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69cc6601d77881908299d58db6e64937 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:18 p.m.