Triple
T37486930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All Ghillied Up |
E931556
|
entity |
| Predicate | hasSequelMission |
P36269
|
FINISHED |
| Object | One Shot, One Kill |
—
|
NE NERFINISHED |
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: One Shot, One Kill | Statement: [All Ghillied Up, hasSequelMission, One Shot, One Kill]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSequelMission Context triple: [All Ghillied Up, hasSequelMission, One Shot, One Kill]
-
A.
hasSequel
Indicates that one work is followed by another work that continues its story, timeline, or thematic development.
-
B.
hasSequelPiece
Indicates that one creative work is a subsequent installment or continuation that follows another work in a series.
-
C.
hasSequelInTheme
Indicates that one work has a sequel whose story, style, or subject matter continues or closely aligns with the thematic elements of the original.
-
D.
nextMission
chosen
Indicates that one mission directly follows another in a planned or chronological sequence.
-
E.
hasSequelType
Indicates that one work has a sequel of a specified type or category in relation to another work.
- 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_69f76ec382248190b47844df596123c6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a00868083b081909afc3d8d4ad56b43 |
completed | May 10, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_6a0084f5f72c8190b08afa82690e322a |
completed | May 10, 2026, 1:15 p.m. |
Created at: May 3, 2026, 4:17 p.m.