Triple
T7286429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Timbercreek Asset Management |
E163879
|
entity |
| Predicate | targetInvestments |
P45042
|
FINISHED |
| Object | income-generating real estate |
—
|
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: income-generating real estate | Statement: [Timbercreek Asset Management, targetInvestments, income-generating real estate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetInvestments Context triple: [Timbercreek Asset Management, targetInvestments, income-generating real estate]
-
A.
investmentScope
chosen
Indicates the range or boundaries of activities, assets, or sectors that an investment is intended or allowed to cover.
-
B.
investmentOptions
Indicates that one entity offers, defines, or is associated with possible ways another entity can allocate resources or capital.
-
C.
vestments
Indicates that one entity is wearing or is ceremonially clothed in special religious or official garments associated with a role or office.
-
D.
investmentType
Indicates the specific category or nature of an investment associated with an entity or transaction.
-
E.
vestmentStyle
Indicates the style or type of clothing or ceremonial garments associated with an 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_69c6886093b88190a254b1ce6db8bae7 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb532adc8190bcbbf31bb54383fb |
completed | March 27, 2026, 8:40 p.m. |
| PD | Predicate disambiguation | batch_69c6e76c5fbc8190b378830082f11cb0 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:59 p.m.