Triple
T5890936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | file (Unix) |
E130984
|
entity |
| Predicate | typicalUsageExample |
P12995
|
FINISHED |
| Object | file filename |
—
|
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: file filename | Statement: [file (Unix), typicalUsageExample, file filename]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalUsageExample Context triple: [file (Unix), typicalUsageExample, file filename]
-
A.
typicalUsageFormat
Indicates the usual or standard way in which something is expressed, presented, or formatted in practice.
-
B.
typicalFunction
Indicates that something serves as the usual or characteristic function or role of an entity.
-
C.
toolUseExamples
Indicates that one entity provides example instances or demonstrations of how a particular tool is or can be used by another entity.
-
D.
usagePattern
chosen
Indicates how something is typically used or the recurring manner in which it is employed or consumed.
-
E.
usedAsExampleIn
Indicates that one entity is cited or presented as an illustrative example within another entity, such as a text, discussion, or explanation.
- 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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03fe07b7081909f8577ec3a9a1a8d |
completed | March 22, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69c0334bdc308190ad0d7199ab975588 |
completed | March 22, 2026, 6:22 p.m. |
Created at: March 22, 2026, 3:58 p.m.