Triple
T7486411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Austin v. Michigan Chamber of Commerce |
E176891
|
entity |
| Predicate | affectedEntityType |
P51675
|
FINISHED |
| Object | for-profit corporations |
—
|
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: for-profit corporations | Statement: [Austin v. Michigan Chamber of Commerce, affectedEntityType, for-profit corporations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedEntityType Context triple: [Austin v. Michigan Chamber of Commerce, affectedEntityType, for-profit corporations]
-
A.
affectedEntity
Indicates that an entity is the one that is impacted, influenced, or acted upon as a result of an event, action, or process.
-
B.
includedEntityType
Indicates that one entity type is contained within, or forms a component part of, another entity type.
-
C.
appliedToEntityType
chosen
Indicates that something (such as a rule, constraint, or operation) is specifically applied to entities of a given type.
-
D.
affectedCompany
Indicates that a company is impacted or influenced by a particular event, action, or entity.
-
E.
affectedPerson
Indicates that a particular person is impacted or influenced by an event, action, or condition.
- 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_69c69f24ac508190bb98fe927c0bd065 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f556955c8190bf014a065e04c5d8 |
completed | March 27, 2026, 9:23 p.m. |
| PD | Predicate disambiguation | batch_69c6f03eeaa88190a5215772ed05ee9f |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:42 p.m.