Triple
T27934074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2009 assault on Rihanna |
E708055
|
entity |
| Predicate | involvedRelationshipType |
P10690
|
FINISHED |
| Object | intimate partner violence |
—
|
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: intimate partner violence | Statement: [2009 assault on Rihanna, involvedRelationshipType, intimate partner violence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvedRelationshipType Context triple: [2009 assault on Rihanna, involvedRelationshipType, intimate partner violence]
-
A.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
basisOfRelationship
Indicates that one entity serves as the foundational reason, cause, or justification for the relationship that exists between two or more entities.
-
C.
relationshipTypeStart
Indicates the type or category of relationship that begins or is initiated at a specific point or event.
-
D.
relatedType
Indicates that one entity is connected to another through a specified type or category of relationship.
-
E.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
- 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_69ef96bbf2c48190a9d0e0291457aab6 |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f73223675481908c1bc3208c0f5284 |
completed | May 3, 2026, 11:31 a.m. |
| PD | Predicate disambiguation | batch_69f7317690108190b3aae2cd2e1d069e |
completed | May 3, 2026, 11:28 a.m. |
Created at: April 27, 2026, 7:04 p.m.