Triple
T18482232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DK3 |
E451588
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | “Rage” |
—
|
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: “Rage” | Statement: [DK3, hasPart, “Rage”]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: “Rage” Context triple: [DK3, hasPart, “Rage”]
-
A.
Rage!
Rage! is a funk-driven studio album by the American jam band Lettuce, showcasing their energetic, groove-heavy instrumental style.
-
B.
The Rage
The Rage is a 2007 American horror film directed by special effects artist Robert Kurtzman, known for its gruesome practical effects and over-the-top gore.
-
C.
Rage
Rage is a psychological horror novel by Stephen King, originally published under the pseudonym Richard Bachman, that follows a troubled high school student who takes his class hostage.
-
D.
Rage
Rage is a Japanese crime drama film featuring Ken Watanabe in a central role, exploring the aftermath of a brutal murder and the corrosive effects of suspicion on human relationships.
-
E.
Rage
Rage is a steel roller coaster at Adventure Island known for its steep drops and intense inversions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
Provenance (2 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_69d8d38465a0819099b9b42d2a662ac1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e531d49a1881908cc2ad6132953c96 |
completed | April 19, 2026, 7:49 p.m. |
Created at: April 10, 2026, 11:35 a.m.