Triple
T28669337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uncle Julian Blackwood |
E725665
|
entity |
| Predicate | relationshipToTragedy |
P202069
|
FINISHED |
| Object | both victim and chronicler |
—
|
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: both victim and chronicler | Statement: [Uncle Julian Blackwood, relationshipToTragedy, both victim and chronicler]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToTragedy Context triple: [Uncle Julian Blackwood, relationshipToTragedy, both victim and chronicler]
-
A.
impactOfTragedy
Indicates the effect or consequences that a tragic event has on an entity or situation.
-
B.
victimRelation
Indicates that one entity is the victim or target of harm, wrongdoing, or an adverse action caused by another entity.
-
C.
relationshipToHeed
Indicates a relationship in which one entity is expected to pay attention to, respect, or follow the guidance, warnings, or wishes of another entity.
-
D.
familyTragedyInvolvedChildren
Indicates that the family tragedy specifically involved one or more children as affected parties.
-
E.
victimRelationship
Indicates that one entity is the victim in relation to another entity involved in a harmful, criminal, or adverse act.
- 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_69f01d85be388190b669a0e401e2f2c4 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_6a004c41f13c8190bc47daf6a5ca3949 |
completed | May 10, 2026, 9:13 a.m. |
| PD | Predicate disambiguation | batch_6a004bce3e3081909b35ae5b3bf2b35e |
completed | May 10, 2026, 9:11 a.m. |
| PDg | Predicate description generation | batch_6a004c41009c8190b18e41acf1fd7372 |
completed | May 10, 2026, 9:13 a.m. |
Created at: April 28, 2026, 5:02 a.m.