Triple
T30624905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nriga |
E779545
|
entity |
| Predicate | offenseType |
P161364
|
FINISHED |
| Object | unintentional offense |
—
|
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: unintentional offense | Statement: [Nriga, offenseType, unintentional offense]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offenseType Context triple: [Nriga, offenseType, unintentional offense]
-
A.
offenseAgainst
Indicates that one party has committed a harmful, illegal, or rule-violating act directed against another party or entity.
-
B.
offense
Indicates that one entity commits, causes, or is responsible for a violation, wrongdoing, or rule-breaking act against another entity or governing norms.
-
C.
offenseDescription
chosen
Indicates the specific nature or characterization of an offense, typically summarizing what violation or wrongdoing occurred.
-
D.
offensiveMisplayType
Indicates the specific kind of mistake or error committed by the offensive side during a play or action.
-
E.
offensiveCharacteristic
Indicates that one entity possesses a trait, behavior, or quality that is considered insulting, disrespectful, or likely to cause offense to another entity or group.
- 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_69f224a431548190a44ad9d088dbf91f |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68a18eee08190b6d3d752e1685177 |
completed | May 2, 2026, 11:34 p.m. |
| PD | Predicate disambiguation | batch_69f67e448a9c8190b591374d98799fe3 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 29, 2026, 8:27 p.m.