Triple
T20601906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosalie Boca |
E506202
|
entity |
| Predicate | murderPlotTarget |
P130740
|
FINISHED |
| Object | her unfaithful husband |
—
|
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: her unfaithful husband | Statement: [Rosalie Boca, murderPlotTarget, her unfaithful husband]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: murderPlotTarget Context triple: [Rosalie Boca, murderPlotTarget, her unfaithful husband]
-
A.
targetOfCrime
Indicates that the subject is the person, organization, or entity against whom the referenced crime is committed.
-
B.
victimOfMurderPlot
chosen
Indicates that one entity is the intended target or victim in another entity’s plan or plot to commit murder.
-
C.
murderVictimOf
Indicates that one entity is the person who was killed by another entity in an act of murder.
-
D.
reasonForMurder
Indicates the motive or underlying cause that led someone to commit a murder.
-
E.
hasMurderer
Indicates that one entity is the person who committed the murder of 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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa20f5c881909265ce7d96efc487 |
completed | April 20, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69e59fffe1748190825e4eaa90340631 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:41 a.m.