Triple
T21211298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stanley Ipkiss |
E522726
|
entity |
| Predicate | firstAppearance |
P795
|
FINISHED |
| Object | Mayhem #1 |
—
|
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: Mayhem #1 | Statement: [Stanley Ipkiss, firstAppearance, Mayhem #1]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mayhem #1 Context triple: [Stanley Ipkiss, firstAppearance, Mayhem #1]
-
A.
Mayhem #1
chosen
Mayhem #1 is a comic book issue notable for introducing the character and storyline that would become the cult-favorite series "The Mask."
-
B.
Mayhem (2010)
Mayhem (2010) is a rockabilly- and blues-infused studio album by Irish singer Imelda May that showcases her retro style and powerful vocals.
-
C.
Mayhem Pictures
Mayhem Pictures is a film and television production company known for developing and producing motion picture projects.
-
D.
Mayhem
Mayhem is a pioneering Norwegian black metal band known for its influential yet controversial role in shaping the early 1990s black metal scene.
-
E.
Mayhem
Mayhem is a 2017 action-horror film in which Steven Yeun plays a corporate employee trapped in a quarantined office building where a virus unleashes violent chaos among workers.
- 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_69e0b511ed84819099b449b4a111085c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7346eb20c8190aeb3c0cc0a24aaf9 |
completed | April 21, 2026, 8:25 a.m. |
Created at: April 16, 2026, 3:37 p.m.