Triple
T37145308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MLC |
E920225
|
entity |
| Predicate | opposedToDuringWar |
P29270
|
FINISHED |
| Object | Laurent-Désiré Kabila government |
—
|
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: Laurent-Désiré Kabila government | Statement: [MLC, opposedToDuringWar, Laurent-Désiré Kabila government]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: opposedToDuringWar Context triple: [MLC, opposedToDuringWar, Laurent-Désiré Kabila government]
-
A.
opposedWar
chosen
Indicates that an entity actively resisted, disagreed with, or worked against a particular war or military conflict.
-
B.
enemyDuringWar
Indicates that one entity is an enemy of another specifically in the context of a particular war or armed conflict.
-
C.
supportedDuringWar
Indicates that one entity provided assistance, resources, or backing to another entity specifically during a time of war.
-
D.
statusDuringWar
Indicates the role, condition, or classification an entity held specifically during a period of war.
-
E.
isWartimeCounterpartOf
Indicates that one entity serves as the equivalent or corresponding version of another entity specifically in a wartime context.
- 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_69f76e9f87c08190b4c8f7fafbd8345a |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd7e364a648190a1e9e1d9fc76e99e |
completed | May 8, 2026, 6:09 a.m. |
| PD | Predicate disambiguation | batch_69fd7bb547608190a3b04dddbca6b8bc |
completed | May 8, 2026, 5:59 a.m. |
Created at: May 3, 2026, 4:15 p.m.