Triple
T16065553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juno Temple |
E389721
|
entity |
| Predicate | actedIn |
P1668
|
FINISHED |
| Object | Kaboom |
E604456
|
NE FINISHED |
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: Kaboom | Statement: [Juno Temple, actedIn, Kaboom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaboom Context triple: [Juno Temple, actedIn, Kaboom]
-
A.
Kaboom
chosen
Kaboom is a 2010 surreal coming-of-age dark comedy film written and directed by Gregg Araki.
-
B.
Kaboom!
Kaboom! is a fast-paced 1981 Atari 2600 action video game in which players catch falling bombs with buckets, widely regarded as one of the console’s classic titles.
-
C.
Ka-boom Ka-boom
"Ka-boom Ka-boom" is a track by Marilyn Manson featured on his 2003 industrial metal album *The Golden Age of Grotesque*.
-
D.
Kaboings
Kaboings are a type of Kremling enemy from the Donkey Kong video game series, known for their distinctive bouncing movement and dual-headed appearance.
-
E.
Kaboom Town!
Kaboom Town! is an annual Independence Day fireworks and entertainment festival held in Addison, Texas, known for one of the most spectacular pyrotechnic displays in the United States.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1837bec688190a77ad347600b6bdc |
completed | April 17, 2026, 12:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe47ef6648190bf1fe216e78ef660 |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:57 a.m.