Triple
T21945815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emma Thompson as Gareth Peirce |
E541929
|
entity |
| Predicate | filmTheme |
P120580
|
FINISHED |
| Object | miscarriage of justice |
—
|
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: miscarriage of justice | Statement: [Emma Thompson as Gareth Peirce, filmTheme, miscarriage of justice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmTheme Context triple: [Emma Thompson as Gareth Peirce, filmTheme, miscarriage of justice]
-
A.
filmThemeConnection
chosen
Indicates a relationship where a film is associated with, explores, or is centered around a particular theme.
-
B.
filmType
Indicates the specific category or genre that a film belongs to.
-
C.
identifierForFilmTheme
Indicates that something serves as an identifying label or code specifically assigned to a film theme.
-
D.
fictionalTheme
Indicates that a work, element, or context is centered around or characterized by a fictional theme or motif.
-
E.
film
Indicates that an entity is a movie or cinematic work, or that a relationship involves such a movie.
- 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_69e0c47ef0e48190a50e1bcc43f4b3fd |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12427c2b48190949c41bd3be2d9f3 |
completed | April 28, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69e6f5efc208819091ed2cf6841fa600 |
completed | April 21, 2026, 3:58 a.m. |
Created at: April 16, 2026, 7:57 p.m.