Triple
T8329456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Every Which Way but Loose |
E195037
|
entity |
| Predicate | partOfGenreHistory |
P1451
|
FINISHED |
| Object | 1970s American action-comedy 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: 1970s American action-comedy films | Statement: [Every Which Way but Loose, partOfGenreHistory, 1970s American action-comedy films]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfGenreHistory Context triple: [Every Which Way but Loose, partOfGenreHistory, 1970s American action-comedy films]
-
A.
historicallyImportantGenre
Indicates that the subject genre has played a significant and influential role in history or cultural development.
-
B.
historicalGenre
Indicates that something belongs to or is categorized within a particular historical genre.
-
C.
partOfHistoryOf
chosen
Indicates that one entity forms a component, episode, or contributing element within the historical development or narrative of another entity.
-
D.
hasGenrePeriod
Indicates a relationship where an entity is associated with a specific historical or stylistic period that characterizes its genre.
-
E.
historicalCategory
Indicates that an entity is classified within a particular historical grouping, period, or type based on its time-related characteristics or context.
- 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_69ca82e87f2c8190bdb71ee29dfc642d |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fb812508190aed8a283dacf712e |
completed | March 31, 2026, 8:03 a.m. |
| PD | Predicate disambiguation | batch_69cb70c3231c81909e3d463192c9de22 |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 5:56 p.m.