Triple
T9920119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kevin Chapman |
E185971
|
entity |
| Predicate | frequentlyPortrays |
P58519
|
FINISHED |
| Object | law enforcement officers |
—
|
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: law enforcement officers | Statement: [Kevin Chapman, frequentlyPortrays, law enforcement officers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequentlyPortrays Context triple: [Kevin Chapman, frequentlyPortrays, law enforcement officers]
-
A.
oftenDepictedAs
Indicates that one entity is frequently represented or portrayed in the form, appearance, or symbolism of another entity.
-
B.
workOftenDepicts
chosen
Indicates that one entity’s work frequently portrays, represents, or includes the other entity as a subject or theme.
-
C.
commonlyDepictedOn
Indicates that something is frequently shown or represented on the surface, medium, or context of another thing.
-
D.
isFrequentlyDescribedAs
Indicates that something is often characterized or referred to using a particular description or set of attributes.
-
E.
depicts
Indicates that one entity visually represents, portrays, or shows another entity.
- 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_69ca829b45f481909040f7b99a1976ed |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb5699bc48190961e036d1131fef0 |
completed | April 2, 2026, 12:16 a.m. |
| PD | Predicate disambiguation | batch_69cd1d90b8a8819081748f129c0c6ab6 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:42 p.m.