Triple
T2653659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ollie’s Barbecue |
E53956
|
entity |
| Predicate | affectedInterstateCommerce |
P41962
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Ollie’s Barbecue, affectedInterstateCommerce, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedInterstateCommerce Context triple: [Ollie’s Barbecue, affectedInterstateCommerce, yes]
-
A.
hasInterstate
Indicates that there exists an interstate connection or highway link between the related entities.
-
B.
isInterchange
Indicates that two entities can be substituted or exchanged for one another without affecting the relevant system, function, or outcome.
-
C.
affectedU.S.State
Indicates that a particular U.S. state is impacted or influenced by a specified event, condition, or entity.
-
D.
transboundaryContext
Indicates a context, situation, or effect that crosses or involves the boundaries between two or more distinct jurisdictions, regions, or territories.
-
E.
effectOnUnitedStates
Indicates the impact, influence, or consequences that something has on the United States.
- F. None of above. chosen
Provenance (4 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_69ab495e192081909c77b622e8e7e15a |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abda0ba2208190ad87763ecbef8c3c |
completed | March 7, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69abd815d06481909535c02b0aba8553 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abda0a13308190a986df86270258a7 |
completed | March 7, 2026, 7:55 a.m. |
Created at: March 6, 2026, 9:53 p.m.