Triple
T8740676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Molasses Act 1733 |
E207490
|
entity |
| Predicate | industryAffected |
P44194
|
FINISHED |
| Object | rum production in the American colonies |
—
|
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: rum production in the American colonies | Statement: [Molasses Act 1733, industryAffected, rum production in the American colonies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: industryAffected Context triple: [Molasses Act 1733, industryAffected, rum production in the American colonies]
-
A.
industryContext
Indicates the industry or sector within which an entity, activity, or relationship is situated or most relevant.
-
B.
industryStart
Indicates the point in time or event at which an industry, industrial activity, or industrial era begins.
-
C.
industryResponseTo
Indicates that an industry reacts or responds in some way to a particular event, condition, policy, or influence.
-
D.
impactOnIndustry
chosen
Indicates the effect or influence that one entity, event, or action has on the state, performance, or development of an industry.
-
E.
operatorIndustry
Indicates that an operator (such as a company or organization) is engaged in or associated with a particular industry sector.
- 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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d4a0cf481909c770cb39fd00fcd |
completed | March 31, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69cc457322b481908712a9630a17b954 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:38 p.m.