Triple
T22797944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rinaldo Pazzi |
E564300
|
entity |
| Predicate | pursues |
P134
|
FINISHED |
| Object | Hannibal Lecter |
—
|
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: Hannibal Lecter | Statement: [Rinaldo Pazzi, pursues, Hannibal Lecter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hannibal Lecter Context triple: [Rinaldo Pazzi, pursues, Hannibal Lecter]
-
A.
Hannibal Lecter
chosen
Hannibal Lecter is a brilliant but psychopathic cannibalistic serial killer and psychiatrist, best known as the chilling antagonist in Thomas Harris’s novels and their film adaptations.
-
B.
Dick Lecter
Dick Lecter is a supporting character in the satirical comedy film "Pootie Tang," known for his role in the movie’s offbeat, absurdist humor.
-
C.
Colonel Woodrow Dolarhyde
Colonel Woodrow Dolarhyde is a tough, authoritarian cattle baron and former Civil War officer who becomes a key leader in the fight against an alien invasion in the film "Cowboys & Aliens."
-
D.
Francis Dolarhyde
Francis Dolarhyde is the fictional serial killer known as "The Tooth Fairy" in Thomas Harris's novel *Red Dragon* and its screen adaptations.
-
E.
Michael Ripps
Michael Ripps is a film editor known for his work on the movie "Stakeout."
- 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_69e2458185f88190b0045227ee420411 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17cda76448190891c5190e1d75ae0 |
completed | April 29, 2026, 3:36 a.m. |
Created at: April 17, 2026, 3:30 p.m.