Triple
T21480068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Patrick Reynolds |
E529963
|
entity |
| Predicate | industryOpposed |
P437
|
FINISHED |
| Object | tobacco industry |
—
|
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: tobacco industry | Statement: [Patrick Reynolds, industryOpposed, tobacco industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: industryOpposed Context triple: [Patrick Reynolds, industryOpposed, tobacco industry]
-
A.
opposedBy
chosen
Indicates that one entity actively resists, disagrees with, or works against the actions, views, or position of another entity.
-
B.
opposition
Indicates a relationship in which one entity actively resists, disagrees with, or is positioned against another entity, idea, or action.
-
C.
opposedAdministration
Indicates that one entity actively resisted, challenged, or worked against the governance, policies, or authority exercised by another entity or administration.
-
D.
opposedProject
Indicates that an entity actively resisted, disagreed with, or worked against a particular project.
-
E.
primaryOpposition
Indicates the main or most significant opposing relationship between two entities, such as their chief rival, counterforce, or contrasting position.
- 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_69e0c45acc3881908e38d3f28964152b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea1b0130819088b8e96ddbb29317 |
completed | April 23, 2026, 9:44 a.m. |
| PD | Predicate disambiguation | batch_69e631ec1d048190b6da97da8222e413 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:20 p.m.