Triple
T27818464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GetRequest |
E702746
|
entity |
| Predicate | hasTypicalUseCase |
P144936
|
FINISHED |
| Object | reading a single resource by ID |
—
|
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: reading a single resource by ID | Statement: [GetRequest, hasTypicalUseCase, reading a single resource by ID]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalUseCase Context triple: [GetRequest, hasTypicalUseCase, reading a single resource by ID]
-
A.
hasTypicalUseContext
Indicates that something is commonly or characteristically used within a particular situation, setting, or context.
-
B.
hasUseCase
Indicates that one entity is employed, applied, or utilized as a solution or method to address a particular need, problem, or scenario associated with another entity.
-
C.
hasTypicalUseTime
Indicates the usual or expected duration or time period during which something is commonly used or in operation.
-
D.
hasTypicalUsageRegion
Indicates that something is most commonly or characteristically used within a particular geographic region.
-
E.
frequentUseCase
chosen
Indicates a situation, scenario, or pattern of use that occurs regularly or more often than others in relation to the subject.
- 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_69ef840ad1e88190b5bff2d1ddec8700 |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
Created at: April 27, 2026, 5:47 p.m.