Triple
T4823747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elena Milashina |
E107770
|
entity |
| Predicate | hasNotableVictimGroupCovered |
P870
|
FINISHED |
| Object | LGBT people in Chechnya |
—
|
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: LGBT people in Chechnya | Statement: [Elena Milashina, hasNotableVictimGroupCovered, LGBT people in Chechnya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableVictimGroupCovered Context triple: [Elena Milashina, hasNotableVictimGroupCovered, LGBT people in Chechnya]
-
A.
notableVictim
chosen
Indicates that the subject is a person or entity who is notably recognized as a victim of the object (such as an event, crime, or harmful action).
-
B.
isVictimOf
Indicates that one entity suffers harm, loss, or wrongdoing as a result of another entity’s actions or events.
-
C.
hasVictimCount
Indicates the number of victims associated with a particular event, action, or entity.
-
D.
hasVictimNationalities
Indicates that an event, incident, or action involved victims belonging to one or more specified nationalities.
-
E.
hasPoliticalAlignmentOfVictims
Indicates that an entity is associated with victims characterized by a particular political alignment.
- 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_69bd43f9efa081908314cb3e94fa1695 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6ddd17d881909f7731ff2b460e83 |
completed | March 20, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69bd6c1fe130819087ae01309f96a0c8 |
completed | March 20, 2026, 3:47 p.m. |
Created at: March 20, 2026, 1:24 p.m.