Triple
T31591273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mexican criminal justice system |
E806103
|
entity |
| Predicate | adversarialSystemFullyImplementedBy |
P171995
|
FINISHED |
| Object | 2016 |
—
|
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: 2016 | Statement: [Mexican criminal justice system, adversarialSystemFullyImplementedBy, 2016]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adversarialSystemFullyImplementedBy Context triple: [Mexican criminal justice system, adversarialSystemFullyImplementedBy, 2016]
-
A.
modelsAdversary
Indicates that one entity represents, simulates, or formally characterizes another entity as an adversary within a given context or system.
-
B.
otherAdversary
Indicates that one entity is an adversary of another, distinct from any primary or previously identified adversary.
-
C.
hasMainAdversary
Indicates that an entity’s primary or most significant opponent, rival, or enemy is another specified entity.
-
D.
adversaryView
Indicates that one entity observes, analyzes, or interprets another entity from the perspective of an opponent or potential attacker.
-
E.
primaryAdversaryPlanning
Indicates that an entity is the main opposing force actively formulating or directing plans against another entity.
- F. None of above. chosen
Provenance (4 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_69f348d4891c8190b02bae3c8ecb68b7 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a956e9b08190bf83547bba8e8147 |
completed | May 3, 2026, 1:48 a.m. |
| PD | Predicate disambiguation | batch_69f6a75656e081908739ed9e2f600e42 |
completed | May 3, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f6a8036ab481908019f2f071fa406e |
completed | May 3, 2026, 1:42 a.m. |
Created at: April 30, 2026, 10:28 p.m.