Triple
T13537687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lifetime Original Movies |
E323302
|
entity |
| Predicate | notableSubgenre |
P81889
|
FINISHED |
| Object | women-in-peril films |
—
|
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: women-in-peril films | Statement: [Lifetime Original Movies, notableSubgenre, women-in-peril films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableSubgenre Context triple: [Lifetime Original Movies, notableSubgenre, women-in-peril films]
-
A.
subgenre
Indicates that one genre is a more specific, subordinate category within a broader parent genre.
-
B.
hasNotableSubgenre
chosen
Indicates that one genre is recognized as a particularly significant or prominent subgenre of another genre.
-
C.
secondaryGenre
Indicates that an entity (such as a work or item) has an additional, non-primary genre classification associated with it.
-
D.
notableSubstyle
Indicates that one style is a notable or distinct subcategory within another broader style.
-
E.
notableSubculture
Indicates that one entity is a significant or well-recognized subculture within the context or domain of the other 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafbe39948190808062d4eff91841 |
completed | April 12, 2026, 2:44 p.m. |
| PD | Predicate disambiguation | batch_69dbae1046c48190b4ee98c6c9cb9d85 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:45 p.m.