Triple
T13599122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | assassination of Leon Trotsky |
E324897
|
entity |
| Predicate | victimPoliticalAffiliation |
P56095
|
FINISHED |
| Object | Trotskyism |
—
|
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: Trotskyism | Statement: [assassination of Leon Trotsky, victimPoliticalAffiliation, Trotskyism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: victimPoliticalAffiliation Context triple: [assassination of Leon Trotsky, victimPoliticalAffiliation, Trotskyism]
-
A.
affectedPoliticalParty
Indicates that a political party is impacted or influenced by a particular event, action, policy, or situation.
-
B.
holderPoliticalAffiliation
chosen
Indicates that a person or officeholder is associated with or belongs to a particular political party or ideology.
-
C.
parentPoliticalAffiliation
Indicates that one entity has a political affiliation that is associated with, derived from, or characteristic of their parent.
-
D.
usesPoliticalLabel
Indicates that one entity applies or assigns a specific political label or classification to another entity.
-
E.
designerPoliticalAffiliation
Indicates the political party, ideology, or affiliation associated with a designer.
- 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_69d80769eaf081909d82f44e484d6113 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0795acc8190a08667ab9dcb0d44 |
completed | April 12, 2026, 2:47 p.m. |
| PD | Predicate disambiguation | batch_69dbae18eaf48190809e8b365856cde9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:49 p.m.