Triple
T32062138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ADIF |
E818772
|
entity |
| Predicate | fileGranularity |
P173168
|
FINISHED |
| Object | whole-file oriented format |
—
|
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: whole-file oriented format | Statement: [ADIF, fileGranularity, whole-file oriented format]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fileGranularity Context triple: [ADIF, fileGranularity, whole-file oriented format]
-
A.
granularityLevel
Indicates the degree of detail or resolution at which something is specified, measured, or analyzed within a given context.
-
B.
scalingGranularity
Indicates the level of detail or resolution at which a quantity, process, or system is adjusted or scaled.
-
C.
timeGranularity
Indicates the level of temporal detail or precision at which an event, measurement, or relationship is defined (e.g., seconds, days, months).
-
D.
controlGranularity
Indicates the level of detail or fineness with which control or regulation is applied within a given process or system.
-
E.
compressionGranularity
Indicates the level of detail or size of units at which data or content is compressed within a system or process.
- 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_69f348fdacec8190b9f74375ca3b2094 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b4f638908190957f64096c07535f |
completed | May 3, 2026, 2:37 a.m. |
| PD | Predicate disambiguation | batch_69f6b154b3dc819087115f5f63f7b00f |
completed | May 3, 2026, 2:22 a.m. |
| PDg | Predicate description generation | batch_69f6b267b90c8190807208cadce5ae8d |
completed | May 3, 2026, 2:26 a.m. |
Created at: May 1, 2026, 12:22 a.m.