Triple
T8801273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philip Seymour Hoffman as Freddie Miles |
E209412
|
entity |
| Predicate | contributesToTheme |
P53515
|
FINISHED |
| Object | class tension |
—
|
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 tension | Statement: [Philip Seymour Hoffman as Freddie Miles, contributesToTheme, class tension]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contributesToTheme Context triple: [Philip Seymour Hoffman as Freddie Miles, contributesToTheme, class tension]
-
A.
supportsThemeOf
chosen
Indicates that one entity reinforces, aligns with, or contributes to the central theme expressed by another entity.
-
B.
followsInTheme
Indicates that one element continues or succeeds another while maintaining the same theme or thematic context.
-
C.
usesThemeFrom
Indicates that one work incorporates, references, or is based on the thematic material of another work.
-
D.
followsTheme
Indicates that one entity adheres to, is guided by, or is structured according to the theme established by another entity.
-
E.
centralThemeContribution
Indicates that one entity contributes significantly to shaping, supporting, or expressing the central theme of another entity.
- 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_69ca836320e48190b5cf585b90a322c4 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fb9c5c88190881b069e1face10c |
completed | March 31, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1f28ec8190a34311cb412920c2 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:44 p.m.