Triple
T3838000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UPS Worldwide Express |
E91181
|
entity |
| Predicate | typicalUseCases |
P2529
|
FINISHED |
| Object | urgent international documents |
—
|
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: urgent international documents | Statement: [UPS Worldwide Express, typicalUseCases, urgent international documents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUseCases Context triple: [UPS Worldwide Express, typicalUseCases, urgent international documents]
-
A.
usageType
chosen
Indicates the specific manner, purpose, or context in which something is used or intended to be used.
-
B.
typicalTools
Indicates that the related tools are commonly or characteristically used to perform the associated activity, task, or function.
-
C.
widelyUsedIn
Indicates that something is commonly or extensively utilized within a particular context, domain, or group.
-
D.
typicalFeatures
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
E.
toolUseExamples
Indicates that one entity provides example instances or demonstrations of how a particular tool is or can be used by 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_69aed960b538819096561c8ed448dec9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeeb9d11f081909fc51e84657ec7f1 |
completed | March 9, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69aee74dcecc819098285483ec721b40 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:18 p.m.