Triple
T20444111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Allom |
E501471
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Killing Machine |
—
|
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: Killing Machine | Statement: [Tom Allom, notableWork, Killing Machine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Killing Machine Context triple: [Tom Allom, notableWork, Killing Machine]
-
A.
Killing Machine
Killing Machine is a film featuring American model and actress Margaux Hemingway in a prominent role.
-
B.
Killing Machine
chosen
"Killing Machine" is a 1978 heavy metal album by British band Judas Priest, known for helping define the genre’s classic sound and image.
-
C.
Kill You
"Kill You" is a controversial and aggressive rap song by Eminem from his acclaimed album "The Marshall Mathers LP."
-
D.
Misery Machine
Misery Machine is a song by Marilyn Manson from his debut studio album "Portrait of an American Family," known for its dark, industrial-influenced sound and provocative themes.
-
E.
Fit to Kill
Fit to Kill is a crime novel by British author Geoffrey Moore, known for its suspenseful plotting and exploration of moral ambiguity.
- 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_69e0b4ac0a1c81908845d0f8a56abce8 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e68cfa7dd08190883a37e3480b152c |
completed | April 20, 2026, 8:30 p.m. |
Created at: April 16, 2026, 11:32 a.m.