Triple
T221586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bituminous Coal Conservation Act of 1935 |
E4224
|
entity |
| Predicate | affectedIndustry |
P71
|
FINISHED |
| Object | bituminous coal mining |
—
|
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: bituminous coal mining | Statement: [Bituminous Coal Conservation Act of 1935, affectedIndustry, bituminous coal mining]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedIndustry Context triple: [Bituminous Coal Conservation Act of 1935, affectedIndustry, bituminous coal mining]
-
A.
affectedAgency
Indicates that one entity has an effect on, or causes a change in, the agency or capacity for action of another entity.
-
B.
affectedFranchise
Indicates that one entity has an impact on, or brings about a change in the status or condition of, a franchise.
-
C.
sector
chosen
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
D.
industryOfUnderlyingCompany
Indicates the industry sector in which the underlying company associated with this entity operates.
-
E.
economicAspect
Indicates that something is related to, characterized by, or has implications for economic factors, conditions, or outcomes.
- 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_69a2573508588190b522c2476d91acfe |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25efd0df48190b8fef4c422a1265f |
completed | Feb. 28, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69a25b54d790819093b35bd1a6f00f92 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.