Triple
T2503493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal Mine Safety and Health Act of 1977 |
E52521
|
entity |
| Predicate | section101Subject |
P450
|
FINISHED |
| Object | mandatory safety and health standards |
—
|
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: mandatory safety and health standards | Statement: [Federal Mine Safety and Health Act of 1977, section101Subject, mandatory safety and health standards]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: section101Subject Context triple: [Federal Mine Safety and Health Act of 1977, section101Subject, mandatory safety and health standards]
-
A.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
-
B.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
C.
secondPartSubject
Indicates that the referenced entity serves as the second part or component of the subject in a composite or multipart relationship.
-
D.
book4Subject
Indicates that something is the subject or topic that a particular book is about.
-
E.
book3Subject
Indicates that an entity serves as the third subject or topic discussed or treated in a particular book.
- 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_69ab4957b3a88190adf968ae0c1b931c |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1cb4f6481908d4e0a1dc0d84d3c |
completed | March 7, 2026, 7:20 a.m. |
| PD | Predicate disambiguation | batch_69abd0bba5348190bb4637d3165cb339 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:46 p.m.