Triple
T17885207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Icon |
E447181
|
entity |
| Predicate | coversTopics |
P24066
|
FINISHED |
| Object | crime in urban communities |
—
|
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: crime in urban communities | Statement: [Icon, coversTopics, crime in urban communities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversTopics Context triple: [Icon, coversTopics, crime in urban communities]
-
A.
coveredTopics
Indicates that certain subjects or themes have been addressed or included within a discussion, document, or activity.
-
B.
includesTopics
chosen
Indicates that one entity contains, covers, or addresses the specified topics as part of its content or scope.
-
C.
featuresTopic
Indicates that something (such as a work, event, or item) prominently includes, focuses on, or is organized around a particular topic.
-
D.
primaryTopicOf
Indicates that a given subject is the main or central topic described by another resource (such as a document, page, or record).
-
E.
interviewTopic
Indicates that a particular subject or theme is the focus of discussion during an interview.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49c123748819091bf46178105079b |
completed | April 19, 2026, 9:10 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:18 a.m.