Triple
T21447696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Orange |
E529120
|
entity |
| Predicate | usesAlias |
P23264
|
FINISHED |
| Object | Mr. Orange |
—
|
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: Mr. Orange | Statement: [Mr. Orange, usesAlias, Mr. Orange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. Orange Context triple: [Mr. Orange, usesAlias, Mr. Orange]
-
A.
Mr. Orange
chosen
Mr. Orange is an undercover police officer posing as a criminal in Quentin Tarantino's crime film "Reservoir Dogs."
-
B.
L’Homme qui assassina
L’Homme qui assassina is a French film best known for featuring actor Pierre Fresnay in a prominent role.
-
C.
Mr. Blonde
Mr. Blonde is a sadistic, unpredictable criminal and one of the central gang members in Quentin Tarantino’s film "Reservoir Dogs," notorious for his brutal violence and iconic torture scene.
-
D.
The Laughing Man
The Laughing Man is a short story by J.D. Salinger that follows a youth baseball team whose enigmatic coach captivates them with a darkly evolving adventure tale about a disfigured outlaw hero.
-
E.
Black Book
Black Book is a 2006 Dutch World War II thriller film directed by Paul Verhoeven, acclaimed for its gripping espionage story and moral complexity.
- 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d04548819086594c20faa5217d |
completed | April 23, 2026, 9:43 a.m. |
Created at: April 16, 2026, 6:06 p.m.