Triple
T31346448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mai Kadra massacre |
E799458
|
entity |
| Predicate | legalCharacterizationProposed |
P13138
|
FINISHED |
| Object | war crimes |
—
|
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: war crimes | Statement: [Mai Kadra massacre, legalCharacterizationProposed, war crimes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalCharacterizationProposed Context triple: [Mai Kadra massacre, legalCharacterizationProposed, war crimes]
-
A.
legalCharacterization
chosen
Indicates how an action, event, or situation is classified or characterized under a specific legal framework or set of laws.
-
B.
ruleCharacterization
Indicates that one rule is described, defined, or characterized in terms of another rule or set of rules.
-
C.
legalCharacter
Indicates that an entity possesses a status, role, or nature that is recognized and defined by law.
-
D.
claimsCharacterization
Indicates that one entity asserts or alleges a particular description, quality, or characterization about another entity or situation.
-
E.
theoryCharacterization
Indicates that one entity provides a defining description, formulation, or account of a theory associated with another entity.
- 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_69f224e51614819083141459a080e97c |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe8f74748c8190bd14a856c057f9f7 |
completed | May 9, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_69fe8e7ed8088190929e0df67aca4de9 |
completed | May 9, 2026, 1:31 a.m. |
Created at: April 29, 2026, 9:17 p.m.