Triple
T15217866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Modified National Institute of Standards and Technology database |
E363685
|
entity |
| Predicate | dataSplit |
P117562
|
FINISHED |
| Object | training set |
—
|
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: training set | Statement: [Modified National Institute of Standards and Technology database, dataSplit, training set]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dataSplit Context triple: [Modified National Institute of Standards and Technology database, dataSplit, training set]
-
A.
developmentSplitWith
Indicates that a development effort, project, or process is divided or shared between multiple parties or components.
-
B.
dividedBetween
Indicates that something is partitioned or shared among two or more distinct entities or groups.
-
C.
data
Indicates that one entity serves as informational content, measurements, or recorded values associated with another entity.
-
D.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
E.
trainsetComposition
Indicates the relationship specifying how a trainset is composed from its constituent vehicles or units.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076f90c481909989befe031a2cae |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca8479188190b2e5d3bc708d7d07 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2ca6148190967c319728ec3661 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:11 a.m.