Triple
T12917012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Viktor Navorski |
E309010
|
entity |
| Predicate | usesForIncome |
P107001
|
FINISHED |
| Object | airport luggage carts |
—
|
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: airport luggage carts | Statement: [Viktor Navorski, usesForIncome, airport luggage carts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesForIncome Context triple: [Viktor Navorski, usesForIncome, airport luggage carts]
-
A.
incomeUsedFor
Indicates that some or all of an income amount is allocated or spent for a specified purpose, activity, or recipient.
-
B.
usedInTaxPayments
Indicates that something is employed or applied as part of making or fulfilling tax payments.
-
C.
incomeType
Indicates the category or source classification of an entity’s income within a given context.
-
D.
income
Indicates the amount of money an entity receives, typically over a specified period, from work, investments, or other sources.
-
E.
revenueUse
Indicates how generated revenue is allocated, spent, or applied toward specific purposes or activities.
- F. None of above. chosen
Provenance (4 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_69d7bdf92b588190acdf2a2291ac4590 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971a1e8088190af697629baecf59f |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa9b7708190a9e9fa30f59ff580 |
completed | April 10, 2026, 9:46 p.m. |
| PDg | Predicate description generation | batch_69d9708a86bc8190bcdcf97e845bb413 |
completed | April 10, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:41 p.m.