Triple
T19647600
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Real Hero |
E471719
|
entity |
| Predicate | underscoresSceneType |
P136818
|
FINISHED |
| Object | climactic moments |
—
|
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: climactic moments | Statement: [The Real Hero, underscoresSceneType, climactic moments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: underscoresSceneType Context triple: [The Real Hero, underscoresSceneType, climactic moments]
-
A.
performedInSceneType
Indicates that an action or event was carried out within a scene of a specified type or category.
-
B.
filmSceneType
Indicates the type or category of a scene within a film, such as its narrative function, style, or setting.
-
C.
sceneLabel
Indicates the categorical label or type assigned to an entire scene based on its overall content or context.
-
D.
subgenreScene
Indicates that one scene is a more specific subgenre or subtype of another, broader scene category.
-
E.
scenarioType
Indicates the specific category or kind of situation, context, or use case that an entity or event is associated with.
- F. None of above. chosen
Provenance (4 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_69d8e51395348190ac1416d46dfc6db0 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64126cea88190a1a6929f46de4686 |
completed | April 20, 2026, 3:07 p.m. |
| PD | Predicate disambiguation | batch_69e514e941008190898d978d7bde91e4 |
completed | April 19, 2026, 5:46 p.m. |
| PDg | Predicate description generation | batch_69e51a23300c8190988552491d9783d7 |
completed | April 19, 2026, 6:08 p.m. |
Created at: April 10, 2026, 1:44 p.m.