Triple
T15176087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brutus "Brutal" Howell |
E362611
|
entity |
| Predicate | treatsHumanely |
P117008
|
FINISHED |
| Object | death row inmates |
—
|
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: death row inmates | Statement: [Brutus "Brutal" Howell, treatsHumanely, death row inmates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: treatsHumanely Context triple: [Brutus "Brutal" Howell, treatsHumanely, death row inmates]
-
A.
treatsHumansAs
Indicates how one entity regards or behaves toward humans, characterizing them in a particular way (e.g., as equals, tools, resources, or threats).
-
B.
animalWelfarePractice
Indicates practices, actions, or policies that affect the well-being, treatment, and living conditions of animals.
-
C.
usesAnimalCarcass
Indicates that an entity makes use of an animal’s dead body or its remains for some purpose.
-
D.
treatmentOf
Indicates a relationship where one entity administers, provides, or is responsible for a therapeutic intervention directed toward another entity (typically a patient or condition).
-
E.
usesCaninesFor
Indicates a relationship where an entity employs or relies on its canine teeth for a particular function or activity.
- F. None of above. chosen
Provenance (4 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0066236d481909e8ac47f496861ad |
completed | April 15, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69deb97bd8bc8190b2ad4888f97cf963 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec72059c08190a34f513a00185b08 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:09 a.m.