Triple
T6700450
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mason Verger |
E152864
|
entity |
| Predicate | causeOfDisfigurement |
P14656
|
FINISHED |
| Object | self-mutilation under influence of Hannibal Lecter |
—
|
LITERAL FINISHED |
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: self-mutilation under influence of Hannibal Lecter | Statement: [Mason Verger, causeOfDisfigurement, self-mutilation under influence of Hannibal Lecter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causeOfDisfigurement Context triple: [Mason Verger, causeOfDisfigurement, self-mutilation under influence of Hannibal Lecter]
-
A.
causeOfInjury
chosen
Indicates that one entity is the source or reason that another entity sustained an injury.
-
B.
causeOf
Indicates that one entity brings about, produces, or is responsible for the occurrence or existence of another entity or event.
-
C.
causeOfDisability
Indicates that one entity is the reason or source that brings about another entity’s disability.
-
D.
colorationCause
Indicates that one entity is the cause or source of the coloration observed in another entity.
-
E.
skinCharacteristic
Indicates a relationship where an entity is associated with a particular quality, feature, or condition of its skin.
- 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_69c68807adbc8190b8632df42b39eda0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16897e48190b43eda2206b14d6a |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d089c7488190a00853fb12f53b2a |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:05 p.m.