Triple
T32482843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grampa Flick |
E830156
|
entity |
| Predicate | methodOfFeeding |
P15764
|
FINISHED |
| Object | torturing children with the shining |
—
|
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: torturing children with the shining | Statement: [Grampa Flick, methodOfFeeding, torturing children with the shining]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: methodOfFeeding Context triple: [Grampa Flick, methodOfFeeding, torturing children with the shining]
-
A.
feedingType
chosen
Indicates the manner or method by which one entity provides nourishment or food to another.
-
B.
includesFeedingType
Indicates that one entity encompasses or specifies a particular type or category of feeding associated with another entity.
-
C.
feedingHabitat
Indicates the type of environment or location where an organism typically obtains and consumes its food.
-
D.
feedingStructure
Indicates a relationship where one entity serves as the anatomical or mechanical structure used by another entity to obtain or ingest food.
-
E.
notableFeedingBehavior
Indicates a characteristic way or pattern in which an entity typically obtains or consumes food that is considered distinctive or noteworthy.
- 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_69f3491ff3b48190b50a7fa00bb05b1f |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6db6af1d88190989810182354d60f |
completed | May 3, 2026, 5:21 a.m. |
| PD | Predicate disambiguation | batch_69f6d82d068c8190940a3200ed760e38 |
completed | May 3, 2026, 5:07 a.m. |
Created at: May 1, 2026, 12:58 a.m.