Triple
T13036163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kima Ventures |
E326564
|
entity |
| Predicate | typicalInvestmentSizeRange |
P15700
|
FINISHED |
| Object | tens of thousands of euros |
—
|
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: tens of thousands of euros | Statement: [Kima Ventures, typicalInvestmentSizeRange, tens of thousands of euros]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalInvestmentSizeRange Context triple: [Kima Ventures, typicalInvestmentSizeRange, tens of thousands of euros]
-
A.
typicalInvestmentSize
chosen
Indicates the usual or most common amount of money invested in a single investment or deal.
-
B.
typicalUnitSize
Indicates the standard or most common size or quantity in which something is typically measured, packaged, or used.
-
C.
typicalRange
Indicates the usual or expected range of values, conditions, or states within which something normally occurs or applies.
-
D.
typicalGroupSizeRange
Indicates the usual minimum and maximum number of individuals that typically occur together in a group for the given entity.
-
E.
spendSize
Indicates the amount or magnitude of spending associated with an entity or transaction.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:55 p.m.