Triple
T23349505
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crime Alley |
E591965
|
entity |
| Predicate | frequentlyDepictedAs |
P18297
|
FINISHED |
| Object | dark |
—
|
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: dark | Statement: [Crime Alley, frequentlyDepictedAs, dark]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequentlyDepictedAs Context triple: [Crime Alley, frequentlyDepictedAs, dark]
-
A.
oftenDepictedAs
chosen
Indicates that one entity is frequently represented or portrayed in the form, appearance, or symbolism of another entity.
-
B.
commonlyDepictedOn
Indicates that something is frequently shown or represented on the surface, medium, or context of another thing.
-
C.
workOftenDepicts
Indicates that one entity’s work frequently portrays, represents, or includes the other entity as a subject or theme.
-
D.
typicallyDepicts
Indicates that one entity is most commonly or characteristically portrayed or represented by the other in depictions or images.
-
E.
depictedSubject
Indicates that one entity visually represents or portrays another entity as its subject in an image or depiction.
- 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_69e25d20e3d08190bcede87673cafb25 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f199cb2a3c8190a5c0c8d8735256c7 |
completed | April 29, 2026, 5:40 a.m. |
| PD | Predicate disambiguation | batch_69effcfd8d288190937a887fe6023c11 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 17, 2026, 5:19 p.m.