Triple
T2745558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold Shipman |
E60857
|
entity |
| Predicate | typicalVictimProfile |
P699
|
FINISHED |
| Object | elderly female patients |
—
|
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: elderly female patients | Statement: [Harold Shipman, typicalVictimProfile, elderly female patients]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalVictimProfile Context triple: [Harold Shipman, typicalVictimProfile, elderly female patients]
-
A.
victimGroup
chosen
Indicates that one group or entity is the target or recipient of harm, abuse, or wrongdoing caused by another.
-
B.
portraysAsVictim
Indicates that one entity represents or depicts another entity as a victim in a given context or narrative.
-
C.
victimAge
Indicates the age of the person who is the victim in the described event or situation.
-
D.
coVictim
Indicates that two or more entities are victims in the same harmful event or incident.
-
E.
typicalPlayerProfile
Indicates the usual or characteristic attributes, behaviors, or demographics associated with a representative player in a given context.
- 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_69ab4b79846081909096725374d65ce9 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb4d37a481908cc2ad4666f3ac94 |
completed | March 7, 2026, 8:01 a.m. |
| PD | Predicate disambiguation | batch_69abd829f1e88190aab1d54f87c69714 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.