Triple
T15254097
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deborah Kara Unger |
E364598
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Payback |
E528663
|
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: Payback | Statement: [Deborah Kara Unger, notableWork, Payback]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Payback Context triple: [Deborah Kara Unger, notableWork, Payback]
-
A.
Payback
chosen
Payback is a 1999 neo-noir crime film starring Mel Gibson as a vengeful thief seeking repayment after being double-crossed.
-
B.
The Payback
The Payback is a 1973 funk album by James Brown, widely regarded as one of his most influential and sampled works, featuring extended grooves and socially charged themes.
-
C.
Payback at Morning Peak
Payback at Morning Peak is a Western novel by actor and author Gene Hackman that follows a young man seeking vengeance after the brutal murder of his family.
-
D.
The Pay Day
The Pay Day is a British heist comedy film that follows a broke IT technician who is drawn into a risky data theft scheme that spirals out of control.
-
E.
Payback Is a Grandmother
Payback Is a Grandmother is a chapter from Laura Esquivel’s novel "Like Water for Chocolate," contributing to the book’s blend of magical realism, family drama, and culinary symbolism.
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007f8cb308190933c4478aa096e24 |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5f572348190bd7719805e257e03 |
completed | May 9, 2026, 7:44 a.m. |
Created at: April 10, 2026, 3:13 a.m.