Triple
T13796478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Django (1966 film) |
E331529
|
entity |
| Predicate | hasUnofficialSequels |
P111492
|
FINISHED |
| Object | numerous Django-titled Spaghetti Westerns |
—
|
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: numerous Django-titled Spaghetti Westerns | Statement: [Django (1966 film), hasUnofficialSequels, numerous Django-titled Spaghetti Westerns]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUnofficialSequels Context triple: [Django (1966 film), hasUnofficialSequels, numerous Django-titled Spaghetti Westerns]
-
A.
hasSequelInCanon
Indicates that a work has a subsequent work that continues its story within the officially recognized continuity.
-
B.
hasSequelRumors
Indicates that there are rumors or unconfirmed reports suggesting a sequel exists or may be produced for the referenced work.
-
C.
hasSequel
Indicates that one work is followed by another work that continues its story, timeline, or thematic development.
-
D.
hasSecondSequel
Indicates that an entity has a second sequel, i.e., a third work in a series that continues its storyline or content.
-
E.
hasSequelOrRelated
Indicates that one work follows, continues, or is otherwise narratively or thematically related to another work.
- 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de025be1f08190aac525d72d7dc0c3 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc85fb600819098a2aab48169be96 |
completed | April 12, 2026, 4:29 p.m. |
| PDg | Predicate description generation | batch_69dcad0eea9881908f71e1eed9a2446b |
completed | April 13, 2026, 8:45 a.m. |
Created at: April 9, 2026, 10:11 p.m.