Triple
T23204448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Jenkins |
E580410
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Semi-Tough |
—
|
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: Semi-Tough | Statement: [Dan Jenkins, notableWork, Semi-Tough]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Semi-Tough Context triple: [Dan Jenkins, notableWork, Semi-Tough]
-
A.
Semi-Tough
chosen
Semi-Tough is a 1977 sports comedy film that satirizes professional football and 1970s self-help culture, starring Burt Reynolds, Kris Kristofferson, and Jill Clayburgh.
-
B.
Too Tough
Too Tough is a track featured on the hip-hop album "Undercover."
-
C.
The Hard
The Hard is a waterfront area in Portsmouth, England, known as a major transport hub and gateway to the city’s historic dockyard and ferry services.
-
D.
The Tough Ones
The Tough Ones is a 1970s Italian poliziottesco crime-action film known for its gritty violence, tough cop antihero, and cult status among Eurocrime cinema fans.
-
E.
Down and Dirty
Down and Dirty is a shared-world superhero anthology novel in the Wild Cards series, featuring interconnected stories about people transformed by an alien virus.
- 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_69e24602ae1481908aaa6bc7ca493867 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1907b3e88819088a397a99456bf77 |
completed | April 29, 2026, 5 a.m. |
Created at: April 17, 2026, 4:07 p.m.