Triple
T30734429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Midnight Meat Train |
E782507
|
entity |
| Predicate | hasKillerName |
P117040
|
FINISHED |
| Object | Mahogany the Butcher |
—
|
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: Mahogany the Butcher | Statement: [The Midnight Meat Train, hasKillerName, Mahogany the Butcher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKillerName Context triple: [The Midnight Meat Train, hasKillerName, Mahogany the Butcher]
-
A.
hasKiller
Indicates that one entity is the killer or cause of death of another entity.
-
B.
hasKillerType
Indicates a relationship where an entity is associated with a specific type or category of killer.
-
C.
hasKillerWeapon
Indicates that a particular weapon was used by an entity to commit a killing.
-
D.
hasKillerDoll
Indicates that an entity possesses, is associated with, or is responsible for a doll characterized as a killer.
-
E.
fictionalKillerName
chosen
Indicates that an entity is known by a particular fictional name as a killer or murderer.
- F. None of above.
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_69f224ad9f9c81908e02a79ae0001137 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68ee54d1c8190a4c020394f7fb19a |
completed | May 2, 2026, 11:55 p.m. |
| PD | Predicate disambiguation | batch_69f686140aa08190a35f62572b2db9b6 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:37 p.m.