Triple
T2197516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Ring |
E50409
|
entity |
| Predicate | humanRightsImpact |
P25890
|
FINISHED |
| Object | forced deportations |
—
|
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: forced deportations | Statement: [Operation Ring, humanRightsImpact, forced deportations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: humanRightsImpact Context triple: [Operation Ring, humanRightsImpact, forced deportations]
-
A.
humanRightsConcern
chosen
Indicates a situation, action, or condition that raises issues related to the recognition, protection, or violation of human rights.
-
B.
humanRightsSituation
Indicates the overall condition, treatment, and respect for individuals’ fundamental rights and freedoms within a given context or jurisdiction.
-
C.
humanImpact
Indicates the effect or influence that human activities have on another entity, system, or environment.
-
D.
civilianImpact
Indicates the extent to which an action, event, or situation affects civilians, especially in terms of harm, disruption, or other consequences.
-
E.
socialImpact
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbf79f3e08190b56e9d7c0ff27237 |
completed | March 7, 2026, 6:02 a.m. |
| PD | Predicate disambiguation | batch_69abbda706f4819094de73e1d1d1f539 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.