Triple
T14826172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lambing Flat |
E348577
|
entity |
| Predicate | hasEthnicConflictType |
P1397
|
FINISHED |
| Object | anti-Chinese violence |
—
|
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: anti-Chinese violence | Statement: [Lambing Flat, hasEthnicConflictType, anti-Chinese violence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEthnicConflictType Context triple: [Lambing Flat, hasEthnicConflictType, anti-Chinese violence]
-
A.
hasEthnicTensionHistory
Indicates that there has been a history of conflict, strain, or hostility between ethnic groups within the referenced context.
-
B.
ethnoPoliticalConflict
Indicates a relationship in which groups defined by ethnic identity are engaged in political struggle, tension, or violence against one another or against a governing authority.
-
C.
hasPartOfConflict
Indicates that one conflict includes another conflict as a constituent or subordinate part of it.
-
D.
conflictType
chosen
Indicates the specific kind or category of conflict that characterizes the relationship or interaction between entities.
-
E.
hasCauseOfConflict
Indicates a relationship where one entity is the source or reason for a conflict involving 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_69d822eb8f588190bf53445e730a934f |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded0713700819097bbb0352650984b |
completed | April 14, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69de8c13418c819088ff9905ace1416a |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:51 a.m.