Triple
T25640017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rene Mullins |
E642812
|
entity |
| Predicate | connectedToTopic |
P37306
|
FINISHED |
| Object | hate crimes in the United States |
—
|
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: hate crimes in the United States | Statement: [Rene Mullins, connectedToTopic, hate crimes in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectedToTopic Context triple: [Rene Mullins, connectedToTopic, hate crimes in the United States]
-
A.
connectedToEvent
Indicates that an entity has a direct association or linkage with a specific event.
-
B.
connectionTo
chosen
Indicates a relationship in which one entity is linked, associated, or otherwise related to another entity.
-
C.
publishesOnTopic
Indicates that an entity (such as a person or organization) produces and releases content whose subject matter concerns a specified topic.
-
D.
followsTopic
Indicates that one entity subscribes to or tracks updates, content, or activity related to a particular topic associated with another entity.
-
E.
connectedToOrganization
Indicates that an entity has a relationship or association with a particular organization, such as membership, affiliation, or formal linkage.
- 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_69e77e7ce28081908b08d65ee6e5c8be |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6b2a65c7c8190ac40f1466ceadefc |
completed | May 3, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f6b14d7d508190bc7d4c89dfba4a32 |
completed | May 3, 2026, 2:22 a.m. |
Created at: April 21, 2026, 5:39 p.m.