Triple
T5166898
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clinical Laboratory Improvement Amendments program |
E116581
|
entity |
| Predicate | appliesToSettings |
P1129
|
FINISHED |
| Object | hospital laboratories |
—
|
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: hospital laboratories | Statement: [Clinical Laboratory Improvement Amendments program, appliesToSettings, hospital laboratories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToSettings Context triple: [Clinical Laboratory Improvement Amendments program, appliesToSettings, hospital laboratories]
-
A.
appliesTo
chosen
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
B.
appliesToFeature
Indicates that something (such as a rule, constraint, or configuration) is relevant to, or governs, a specific feature.
-
C.
appliesToPolicy
Indicates that something is relevant or applicable to a particular policy.
-
D.
usedAsSettingFor
Indicates that one entity serves as the backdrop, location, or environment in which another entity (such as an event, story, or activity) takes place.
-
E.
appliesToProductType
Indicates that something (such as a rule, offer, or condition) is relevant or applicable specifically to a certain type or category of product.
- 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_69bd445ff97c81909a2615cc56235470 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd792c5ea88190b6aa0e519c744155 |
completed | March 20, 2026, 4:43 p.m. |
| PD | Predicate disambiguation | batch_69bd77b36c008190b91011a9fa52b3d2 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:45 p.m.