Triple
T18689782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aircel Comics |
E456960
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Bodycount |
—
|
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: Bodycount | Statement: [Aircel Comics, notableWork, Bodycount]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bodycount Context triple: [Aircel Comics, notableWork, Bodycount]
-
A.
Body Count
"Body Count" is a soulful, emotionally charged song by Canadian singer-songwriter Jessie Reyez that confronts double standards and judgment around women's sexuality.
-
B.
Body Count
chosen
Body Count is an American heavy metal band fronted by rapper Ice-T, known for blending hardcore metal with politically charged lyrics and controversy.
-
C.
Slaughter
Slaughter is an American glam metal band best known for their early 1990s hits like "Up All Night" and "Fly to the Angels."
-
D.
Slaughter
Slaughter is the surname of Louise Slaughter, a long-serving American congresswoman known for her work on health care, ethics, and women's rights.
-
E.
Killings
"Killings" is a short story by Andre Dubus that explores themes of grief, revenge, and moral ambiguity, and served as the basis for the film "In the Bedroom."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8d391eb488190ac2e9abf5bf255e4 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e562e28e5c8190b0033c1667d50e05 |
completed | April 19, 2026, 11:18 p.m. |
Created at: April 10, 2026, 11:49 a.m.