Triple
T28996599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Last-Modified |
E736181
|
entity |
| Predicate | comparisonGranularity |
P119939
|
FINISHED |
| Object | one-second resolution |
—
|
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: one-second resolution | Statement: [Last-Modified, comparisonGranularity, one-second resolution]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: comparisonGranularity Context triple: [Last-Modified, comparisonGranularity, one-second resolution]
-
A.
timeGranularity
chosen
Indicates the level of temporal detail or precision at which an event, measurement, or relationship is defined (e.g., seconds, days, months).
-
B.
scalingGranularity
Indicates the level of detail or resolution at which a quantity, process, or system is adjusted or scaled.
-
C.
comparisonLevel
Indicates the degree or intensity to which two or more entities are being compared within a given context.
-
D.
granularityLevel
Indicates the degree of detail or resolution at which something is specified, measured, or analyzed within a given context.
-
E.
pricingGranularity
Indicates the level of detail or resolution at which prices are defined, grouped, or applied within a pricing structure or model.
- 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_69f077eacd0481908ef0bafd74491cd0 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69f6f8565134819096aac0175f924a9f |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f65fd1d08190b88e5e68ba268500 |
completed | May 3, 2026, 7:16 a.m. |
Created at: April 28, 2026, 9:31 a.m.