Triple
T17961966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Monique government |
E449105
|
entity |
| Predicate | notableAntagonistConnection |
P11706
|
FINISHED |
| Object | Mr. Big's drug cartel |
—
|
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: Mr. Big's drug cartel | Statement: [San Monique government, notableAntagonistConnection, Mr. Big's drug cartel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableAntagonistConnection Context triple: [San Monique government, notableAntagonistConnection, Mr. Big's drug cartel]
-
A.
notableAdversary
chosen
Indicates that one entity is recognized as a significant or prominent opponent or rival of another entity.
-
B.
hasAntagonisticProtagonist
Indicates that the work features a main character who opposes or undermines the typical heroic or moral expectations of a traditional protagonist.
-
C.
notableCharacterAffiliation
Indicates that a notable character is formally associated with, aligned to, or a member of a particular group, organization, or affiliation.
-
D.
antagonistOf
Indicates a relationship where one entity actively opposes, conflicts with, or serves as an adversary to another.
-
E.
laterEnemyOf
Indicates that one entity becomes an enemy of another at a later time, after not initially being in an antagonistic relationship.
- 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_69d8b9f8cca8819099836916c56b7c95 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4b132cc10819088526a0b4b098d69 |
completed | April 19, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69e3f8f2bd088190b1e22ad4d9cc8b13 |
completed | April 18, 2026, 9:34 p.m. |
Created at: April 10, 2026, 10:22 a.m.