Triple
T20697983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Man Logan |
E508704
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Kingpin |
—
|
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: Kingpin | Statement: [Old Man Logan, featuresCharacter, Kingpin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kingpin Context triple: [Old Man Logan, featuresCharacter, Kingpin]
-
A.
Kingpin
chosen
Kingpin is a powerful crime lord in the Marvel Universe, best known as a major adversary of heroes like Spider-Man and Daredevil.
-
B.
Kingpin
Kingpin is a 2003 television film that dramatizes the rise and fall of a powerful Mexican drug lord and his cartel.
-
C.
Kingpin
Kingpin is a 1996 sports comedy film about a washed-up former bowling prodigy who mentors an Amish bowling talent, known for its offbeat humor and cult following.
-
D.
Kingpin Suite
Kingpin Suite is a luxury, bowling-themed hotel suite in Las Vegas known for its in-room bowling lanes and over-the-top entertainment amenities.
-
E.
The Drug King
The Drug King is a South Korean crime drama film that chronicles the rise and fall of a small-time smuggler who becomes a powerful drug lord in 1970s Busan.
- 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_69e0b4c2b2a481909e31e9cb8f81ab55 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c1132178819081086e085f7b3ff8 |
completed | April 21, 2026, 12:13 a.m. |
Created at: April 16, 2026, 12:11 p.m.