Triple
T23954020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomainia |
E603727
|
entity |
| Predicate | hasFilmSettingElement |
P52439
|
FINISHED |
| Object | ghetto |
—
|
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: ghetto | Statement: [Tomainia, hasFilmSettingElement, ghetto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFilmSettingElement Context triple: [Tomainia, hasFilmSettingElement, ghetto]
-
A.
hasFilmSetType
Indicates that a film or scene is associated with a particular type or category of set used in its production.
-
B.
filmSetting
chosen
Indicates the place, time, or environment in which the events of a film are set or take place.
-
C.
hasCinematicFeature
Indicates that something possesses a specific cinematic characteristic, quality, or element related to film or visual storytelling.
-
D.
hasFilmStyle
Indicates that a film exhibits or is characterized by a particular cinematic style or aesthetic approach.
-
E.
hasFilmColorType
Indicates that a film is associated with a particular color process or color classification (e.g., color, black-and-white).
- 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_69e2954222288190a7323554d0cca8d7 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d0d558748190a51b5732a3e6713c |
completed | April 29, 2026, 9:35 a.m. |
| PD | Predicate disambiguation | batch_69f1615518088190a206f54e2fdb14a3 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 9:21 p.m.