Triple
T7030470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kapor Capital |
E163254
|
entity |
| Predicate | hasInvestmentModel |
P38266
|
FINISHED |
| Object | impact investing |
—
|
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: impact investing | Statement: [Kapor Capital, hasInvestmentModel, impact investing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInvestmentModel Context triple: [Kapor Capital, hasInvestmentModel, impact investing]
-
A.
hasInvestmentTheme
chosen
Indicates that an investment, fund, or financial product is associated with a particular overarching theme or strategic focus (such as technology, sustainability, or healthcare).
-
B.
hasTradingModel
Indicates that one entity uses, is governed by, or is associated with a particular trading model.
-
C.
includesInvestmentVehicles
Indicates that one entity contains, comprises, or makes use of specific investment vehicles as part of its structure or offerings.
-
D.
hasStudyModel
Indicates that an entity is associated with, or defined by, a particular study model used for analysis, simulation, or representation.
-
E.
hasPrimaryInvestor
Indicates that an entity has a main or leading investor that provides the primary financial backing or funding.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e458ad9c81908c3f492b317ce291 |
completed | March 27, 2026, 8:11 p.m. |
| PD | Predicate disambiguation | batch_69c6e1b9a2488190aea351d96afa5a12 |
completed | March 27, 2026, 7:59 p.m. |
Created at: March 27, 2026, 2:35 p.m.