Triple
T979372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Canty |
E21131
|
entity |
| Predicate | literaryThemeAssociated |
P16928
|
FINISHED |
| Object | class difference |
—
|
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: class difference | Statement: [Tom Canty, literaryThemeAssociated, class difference]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryThemeAssociated Context triple: [Tom Canty, literaryThemeAssociated, class difference]
-
A.
hasLiterarySignificance
Indicates that something holds notable importance, influence, or value within the realm of literature or literary studies.
-
B.
literaryInfluence
Indicates that one entity has had a significant impact on the style, themes, or development of another entity’s literary work.
-
C.
literaryFeature
chosen
Indicates a relationship where something possesses or exhibits a characteristic, device, or stylistic element used in literature.
-
D.
literarySource
Indicates that one entity serves as the written or literary origin, reference, or basis for another entity.
-
E.
literaryTradition
Indicates a relationship where a work, practice, or expression belongs to, arises from, or participates in a particular established body of literary customs, styles, or conventions.
- 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b479e8f081908183448c36244e1f |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a8a3b08190b4538e119b13f7f5 |
completed | March 1, 2026, 9:42 p.m. |
Created at: March 1, 2026, 7:40 p.m.