Triple
T34975494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIPS 199 |
E1008664
|
entity |
| Predicate | classificationModel |
P146618
|
FINISHED |
| Object | low-moderate-high impact model |
—
|
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: low-moderate-high impact model | Statement: [FIPS 199, classificationModel, low-moderate-high impact model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: classificationModel Context triple: [FIPS 199, classificationModel, low-moderate-high impact model]
-
A.
classificationProblem
Indicates a relationship where a task involves assigning items or instances to one of several predefined categories or classes based on their features.
-
B.
classificationApproach
Indicates the method or strategy used to categorize or assign entities into classes or groups.
-
C.
classificationTest
Indicates that an entity is involved in or associated with a test or evaluation process used for classification purposes.
-
D.
learningModel
chosen
Indicates that one entity functions as a learning model used to learn from data or examples in relation to another entity.
-
E.
classificationConsensus
Indicates that multiple agents or sources agree on the same classification or category assignment for a given entity or item.
- 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_69f76dc78a308190a1ac29ad4a9a4895 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78710282c81909146dc0be91e983f |
completed | May 3, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f784162134819098413482ef52042f |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4:01 p.m.